From 2513bde26046d5ce44988451ea1a89cbcd812daa Mon Sep 17 00:00:00 2001 From: feder-cr <85809106+feder-cr@users.noreply.github.com> Date: Thu, 5 Dec 2024 01:01:47 +0100 Subject: [PATCH] First --- .github/CODE_OF_CONDUCT.md | 73 ++ .github/CONTRIBUTING.md | 79 ++ .github/FUNDING.yml | 1 + .github/ISSUE_TEMPLATE/bug-issue.yml | 90 ++ .github/ISSUE_TEMPLATE/config.yml | 9 + .../ISSUE_TEMPLATE/documentation-issue.yml | 39 + .github/ISSUE_TEMPLATE/enhancement-issue.yml | 46 ++ .github/workflows/ci.yml | 25 + .github/workflows/stale.yml | 42 + .gitignore | 164 ++++ CONTRIBUTING.md | 201 +++++ LICENSE | 661 +++++++++++++++ README.md | 769 ++++++++++++++++++ assets/AIHawk.png | Bin 0 -> 16195 bytes assets/resume_schema.yaml | 133 +++ config.py | 22 + data_folder/plain_text_resume.yaml | 130 +++ data_folder/secrets.yaml | 1 + data_folder/work_preferences.yaml | 47 ++ data_folder_example/plain_text_resume.yaml | 141 ++++ data_folder_example/resume_liam_murphy.txt | 55 ++ data_folder_example/secrets.yaml | 1 + data_folder_example/work_preferences.yaml | 48 ++ main.py | 501 ++++++++++++ requirements.txt | 31 + src/__init__.py | 0 src/job.py | 35 + src/jobContext.py | 10 + src/job_application_saver.py | 92 +++ src/libs/llm_manager.py | 709 ++++++++++++++++ src/libs/resume_and_cover_builder/__init__.py | 6 + src/libs/resume_and_cover_builder/config.py | 34 + .../cover_letter_prompt/__init__.py | 0 .../cover_letter_prompt/strings_feder-cr.py | 52 ++ .../llm/llm_generate_cover_letter_from_job.py | 91 +++ .../llm/llm_generate_resume.py | 322 ++++++++ .../llm/llm_generate_resume_from_job.py | 134 +++ .../llm/llm_job_parser.py | 208 +++++ .../resume_and_cover_builder/module_loader.py | 13 + .../resume_and_cover_builder/resume_facade.py | 165 ++++ .../resume_generator.py | 47 ++ .../resume_job_description_prompt/__init__.py | 0 .../strings_feder-cr.py | 169 ++++ .../resume_prompt/__init__.py | 0 .../resume_prompt/strings_feder-cr.py | 92 +++ .../resume_style/__init__.py | 0 .../resume_style/style_cloyola.css | 149 ++++ .../resume_style/style_josylad_blue.css | 166 ++++ .../resume_style/style_josylad_grey.css | 208 +++++ .../resume_style/style_krishnavalliappan.css | 136 ++++ .../resume_style/style_samodum_bold.css | 191 +++++ .../resume_and_cover_builder/style_manager.py | 126 +++ .../resume_and_cover_builder/template_base.py | 239 ++++++ src/libs/resume_and_cover_builder/utils.py | 131 +++ src/logging.py | 80 ++ src/resume_schemas/job_application_profile.py | 186 +++++ src/resume_schemas/resume.py | 197 +++++ src/utils/__init__.py | 0 src/utils/browser_utils.py | 86 ++ src/utils/chrome_utils.py | 91 +++ src/utils/constants.py | 77 ++ 61 files changed, 7551 insertions(+) create mode 100644 .github/CODE_OF_CONDUCT.md create mode 100644 .github/CONTRIBUTING.md create mode 100644 .github/FUNDING.yml create mode 100644 .github/ISSUE_TEMPLATE/bug-issue.yml create mode 100644 .github/ISSUE_TEMPLATE/config.yml create mode 100644 .github/ISSUE_TEMPLATE/documentation-issue.yml create mode 100644 .github/ISSUE_TEMPLATE/enhancement-issue.yml create mode 100644 .github/workflows/ci.yml create mode 100644 .github/workflows/stale.yml create mode 100644 .gitignore create mode 100644 CONTRIBUTING.md create mode 100644 LICENSE create mode 100644 README.md create mode 100644 assets/AIHawk.png create mode 100644 assets/resume_schema.yaml create mode 100644 config.py create mode 100644 data_folder/plain_text_resume.yaml create mode 100644 data_folder/secrets.yaml create mode 100644 data_folder/work_preferences.yaml create mode 100644 data_folder_example/plain_text_resume.yaml create mode 100644 data_folder_example/resume_liam_murphy.txt create mode 100644 data_folder_example/secrets.yaml create mode 100644 data_folder_example/work_preferences.yaml create mode 100644 main.py create mode 100644 requirements.txt create mode 100644 src/__init__.py create mode 100644 src/job.py create mode 100644 src/jobContext.py create mode 100644 src/job_application_saver.py create mode 100644 src/libs/llm_manager.py create mode 100644 src/libs/resume_and_cover_builder/__init__.py create mode 100644 src/libs/resume_and_cover_builder/config.py create mode 100644 src/libs/resume_and_cover_builder/cover_letter_prompt/__init__.py create mode 100644 src/libs/resume_and_cover_builder/cover_letter_prompt/strings_feder-cr.py create mode 100644 src/libs/resume_and_cover_builder/llm/llm_generate_cover_letter_from_job.py create mode 100644 src/libs/resume_and_cover_builder/llm/llm_generate_resume.py create mode 100644 src/libs/resume_and_cover_builder/llm/llm_generate_resume_from_job.py create mode 100644 src/libs/resume_and_cover_builder/llm/llm_job_parser.py create mode 100644 src/libs/resume_and_cover_builder/module_loader.py create mode 100644 src/libs/resume_and_cover_builder/resume_facade.py create mode 100644 src/libs/resume_and_cover_builder/resume_generator.py create mode 100644 src/libs/resume_and_cover_builder/resume_job_description_prompt/__init__.py create mode 100644 src/libs/resume_and_cover_builder/resume_job_description_prompt/strings_feder-cr.py create mode 100644 src/libs/resume_and_cover_builder/resume_prompt/__init__.py create mode 100644 src/libs/resume_and_cover_builder/resume_prompt/strings_feder-cr.py create mode 100644 src/libs/resume_and_cover_builder/resume_style/__init__.py create mode 100644 src/libs/resume_and_cover_builder/resume_style/style_cloyola.css create mode 100644 src/libs/resume_and_cover_builder/resume_style/style_josylad_blue.css create mode 100644 src/libs/resume_and_cover_builder/resume_style/style_josylad_grey.css create mode 100644 src/libs/resume_and_cover_builder/resume_style/style_krishnavalliappan.css create mode 100644 src/libs/resume_and_cover_builder/resume_style/style_samodum_bold.css create mode 100644 src/libs/resume_and_cover_builder/style_manager.py create mode 100644 src/libs/resume_and_cover_builder/template_base.py create mode 100644 src/libs/resume_and_cover_builder/utils.py create mode 100644 src/logging.py create mode 100644 src/resume_schemas/job_application_profile.py create mode 100644 src/resume_schemas/resume.py create mode 100644 src/utils/__init__.py create mode 100644 src/utils/browser_utils.py create mode 100644 src/utils/chrome_utils.py create mode 100644 src/utils/constants.py diff --git a/.github/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..203ff8f1 --- /dev/null +++ b/.github/CODE_OF_CONDUCT.md @@ -0,0 +1,73 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to making participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, gender identity and expression, level of experience, +education, socio-economic status, nationality, personal appearance, race, +religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +- Using welcoming and inclusive language +- Being respectful of differing viewpoints and experiences +- Gracefully accepting constructive criticism +- Focusing on what is best for the community +- Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +- The use of sexualized language or imagery and unwelcome sexual attention or + advances +- Trolling, insulting/derogatory comments, and personal or political attacks +- Public or private harassment +- Publishing others' private information, such as a physical or electronic + address, without explicit permission +- Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies both within project spaces and in public spaces +when an individual is representing the project or its community. Examples of +representing a project or community include using an official project e-mail +address, posting via an official social media account, or acting as an appointed +representative at an online or offline event. Representation of a project may be +further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at . All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md new file mode 100644 index 00000000..5edba8fb --- /dev/null +++ b/.github/CONTRIBUTING.md @@ -0,0 +1,79 @@ +# Issues Reporting Guidelines + +Welcome to the AI Hawk Contributing Guide and Issues Tracker! To keep things organized and ensure issues are resolved quickly, please follow the guidelines below when submitting a bug report, feature request, or any other issue. + +If you have a general question, are curious about how something in Python works, please remember that [Google](https://google.com) is your friend and it can answer many questions. + +This is a work in progress and you may encounter bugs. + +The employers who you are applying to are not looking for candidates who need someone to hold their hand and do everything for them, they are not your parents, they are your potential boses; they will be expecting you to be able to solve simple problems on your own, the AI Hawk mods and devs expect the same of you. + +Please do not beg in the issues tracker, discussions or chat. We are not here to give you a job, we are here to provide you with a tool for you to go out and find a job on your own. We will try to have instructions for all steps of the process, but you must read the docs, learn on your own, and understand that this is an open-source project run by volunteers. It will require you to do some work of your own. + +If you see something that needs to be documented, or some documentation which could be improved, submit a documentation request or document it yourself and submit a PR to help others understand how that part of the software functions and how to use it. + +## Before You Submit an Issue + +### 1. Search Existing Issues + +Please search through the existing open issues and closed issues to ensure your issue hasn’t already been reported. This helps avoid duplicates and allows us to focus on unresolved problems. + +### 2. Check Documentation + +Review the README and any available documentation to see if your issue is covered. + +Watch this [Intro to AI Hawk video on YouTube](https://www.youtube.com/watch?v=gdW9wogHEUM) + +Join us on [Telegram](https://t.me/AIhawkCommunity) to check with the community about issues and ask for help with issues. If a dev, mod, contributor or other community member is available, a live conversation will likely resolve your small issues and configuration problems faster than using this issues tracker would. + +### 3. Provide Detailed Information + +If you are reporting a bug, make sure you include enough details to reproduce the issue. The more information you provide, the faster we can diagnose and fix the problem. + +## Issue Types + +### 1. Bug Reports + +Please include the following information: + +- **Description:** A clear and concise description of the problem. +- **Steps to Reproduce:** Provide detailed steps to reproduce the bug. +- **Expected Behavior:** What should have happened. +- **Actual Behavior:** What actually happened. +- **Environment Details:** Include your OS, browser version (if applicable), which LLM you are using and any other relevant environment details. +- **Logs/Screenshots:** If applicable, attach screenshots or log outputs. + +### 2. Feature Requests + +For new features or improvements: + +- Clearly describe the feature you would like to see. +- Explain the problem this feature would solve or the benefit it would bring. +- If possible, provide examples or references to similar features in other tools or platforms. + +### 3. Questions/Discussions + +- If you’re unsure whether something is a bug or if you’re seeking clarification on functionality, you can ask a question. The best place to ask a question is on [Telegram](https://t.me/AIhawkCommunity). If you are asking a question on GitHub, please make sure to label your issue as a question. + +## Issue Labeling and Response Time + +We use the following labels to categorize issues: + +- **bug:** An issue where something isn't functioning as expected. +- **documentation:** Improvements or additions to project documentation. +- **duplicate:** This issue or pull request already exists elsewhere. +- **enhancement:** A request for a new feature or improvement. +- **good first issue:** A simple issue suitable for newcomers. +- **help wanted:** The issue needs extra attention or assistance. +- **invalid:** The issue is not valid or doesn't seem correct. +- **question:** Additional information or clarification is needed. +- **wontfix:** The issue will not be fixed or addressed. +- We aim to respond to issues as early as possible. Please be patient, as maintainers may have limited availability. + +## Contributing Fixes + +If you’re able to contribute a fix for an issue: + +1. Fork the repository and create a new branch for your fix. +2. Reference the issue number in your branch and pull request. +3. Submit a pull request with a detailed description of the changes and how they resolve the issue. diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml new file mode 100644 index 00000000..42abba60 --- /dev/null +++ b/.github/FUNDING.yml @@ -0,0 +1 @@ +github: feder-cr diff --git a/.github/ISSUE_TEMPLATE/bug-issue.yml b/.github/ISSUE_TEMPLATE/bug-issue.yml new file mode 100644 index 00000000..0e5956da --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug-issue.yml @@ -0,0 +1,90 @@ +name: Bug report +description: Report a bug or an issue that isn't working as expected. +title: "[BUG]: " +labels: ["bug"] +assignees: [] + +body: + - type: markdown + attributes: + value: | + Please fill out the following information to help us resolve the issue. + + - type: input + id: description + attributes: + label: Describe the bug + description: A clear and concise description of what the bug is. + placeholder: "Describe the bug in detail..." + + - type: textarea + id: steps + attributes: + label: Steps to reproduce + description: | + Steps to reproduce the behavior: + 1. Use branch named '...' + 2. Go to file '...' + 3. Find property named '...' + 4. Change '...' + 5. Run program using command '...' + 6. See error + placeholder: "List the steps to reproduce the bug..." + + - type: input + id: expected + attributes: + label: Expected behavior + description: What you expected to happen. + placeholder: "What was the expected result?" + + - type: input + id: actual + attributes: + label: Actual behavior + description: What actually happened instead. + placeholder: "What happened instead?" + + - type: dropdown + id: branch + attributes: + label: Branch + description: Specify the branch you were using when the bug occurred. + options: + - main + - other + + - type: input + id: otherBranch + attributes: + label: Branch name + description: If you selected ```other``` branch for the previous question, what is the branch name? + placeholder: "what-is-the-name-of-the-branch-you-were-using" + + - type: input + id: pythonVersion + attributes: + label: Python version + description: Specify the version of Python you were using when the bug occurred. + placeholder: "e.g., 3.12.5(64b)" + + - type: input + id: llm + attributes: + label: LLM Used + description: Specify the LLM provider you were using when the bug occurred. + placeholder: "e.g., ChatGPT" + + - type: input + id: model + attributes: + label: Model used + description: Specify the LLM model you were using when the bug occurred. + placeholder: "e.g., GPT-4o-mini" + + - type: textarea + id: additional + attributes: + label: Additional context + description: Add any other context about the problem here. + placeholder: "Any additional information..." diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 00000000..07b1ca6e --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,9 @@ +blank_issues_enabled: true +contact_links: + - name: Questions + url: t.me/AIhawkCommunity + about: You can join the discussions on Telegram. + - name: New issue + url: >- + https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk/blob/v3/.github/CONTRIBUTING.md + about: "Before opening a new issue, please make sure to read CONTRIBUTING.md" diff --git a/.github/ISSUE_TEMPLATE/documentation-issue.yml b/.github/ISSUE_TEMPLATE/documentation-issue.yml new file mode 100644 index 00000000..14f63a44 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/documentation-issue.yml @@ -0,0 +1,39 @@ +name: Documentation request +description: Suggest improvements or additions to the project's documentation. +title: "[DOCS]: " +labels: ["documentation"] +assignees: [] + +body: + - type: markdown + attributes: + value: | + Thanks for helping to improve the project's documentation! Please provide the following details to ensure your request is clear. + + - type: input + id: doc_section + attributes: + label: Affected documentation section + description: Specify which part of the documentation needs improvement or addition. + placeholder: "e.g., Installation Guide, API Reference..." + + - type: textarea + id: description + attributes: + label: Documentation improvement description + description: Describe the specific improvements or additions you suggest. + placeholder: "Explain what changes you propose and why..." + + - type: input + id: reason + attributes: + label: Why is this change necessary? + description: Explain why the documentation needs to be updated or expanded. + placeholder: "Describe the issue or gap in the documentation..." + + - type: input + id: additional + attributes: + label: Additional context + description: Add any other context, such as related documentation, external resources, or screenshots. + placeholder: "Add any other supporting information..." diff --git a/.github/ISSUE_TEMPLATE/enhancement-issue.yml b/.github/ISSUE_TEMPLATE/enhancement-issue.yml new file mode 100644 index 00000000..433ef841 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/enhancement-issue.yml @@ -0,0 +1,46 @@ +name: Feature request +description: Suggest a new feature or improvement for the project. +title: "[FEATURE]: " +labels: ["enhancement"] +assignees: [] + +body: + - type: markdown + attributes: + value: | + Thank you for suggesting a feature! Please fill out the form below to help us understand your idea. + + - type: input + id: summary + attributes: + label: Feature summary + description: Provide a short summary of the feature you're requesting. + placeholder: "Summarize the feature in a few words..." + + - type: textarea + id: description + attributes: + label: Feature description + description: A detailed description of the feature or improvement. + placeholder: "Describe the feature in detail..." + + - type: input + id: motivation + attributes: + label: Motivation + description: Explain why this feature would be beneficial and how it solves a problem. + placeholder: "Why do you need this feature?" + + - type: textarea + id: alternatives + attributes: + label: Alternatives considered + description: List any alternative solutions or features you've considered. + placeholder: "Are there any alternative features or solutions you’ve considered?" + + - type: input + id: additional + attributes: + label: Additional context + description: Add any other context or screenshots to support your feature request. + placeholder: "Any additional information..." diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 00000000..00dc722e --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,25 @@ +name: Python CI + +on: + push: + pull_request: + types: [opened, synchronize, reopened] + +jobs: + test: + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v3 + + - name: Set up Python + uses: actions/setup-python@v3 + with: + python-version: '3.x' + + - name: Install dependencies + run: pip install -r requirements.txt + + - name: Run tests + run: pytest \ No newline at end of file diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml new file mode 100644 index 00000000..e2caac67 --- /dev/null +++ b/.github/workflows/stale.yml @@ -0,0 +1,42 @@ +name: Mark and Close Stale Issues + +on: + # Schedule the workflow to run periodically (e.g., daily at 1:30 AM UTC) + schedule: + - cron: "30 1 * * *" + workflow_dispatch: + +jobs: + stale: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + + steps: + - name: Run Stale Action + uses: actions/stale@v9 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + days-before-issue-stale: 10 # Days of inactivity before marking an issue as stale + days-before-issue-close: 5 # Days after being marked stale before closing the issue + stale-issue-label: "stale" # Label to apply to stale issues + exempt-issue-labels: "pinned,important" # Labels to exclude from being marked as stale + exempt-issue-assignees: true # Exempt issues with assignees from being marked as stale + stale-issue-message: "This issue has been marked as stale due to inactivity. Please comment or update if this is still relevant." + close-issue-message: "This issue was closed due to prolonged inactivity." + days-before-pr-stale: 10 # Days of inactivity before marking a PR as stale + days-before-pr-close: 2 # Days after being marked stale before closing the PR + stale-pr-label: "stale" # Label to apply to stale PRs + exempt-pr-labels: "pinned,important" # Labels to exclude from being marked as stale + stale-pr-message: > + "This pull request has been marked as stale due to inactivity. + To keep it open, you can: + - Show progress by updating the PR with new commits. + - Continue the conversation by adding comments or requesting clarification on any blockers. + - Resolve pending feedback by replying to unresolved comments or implementing suggested changes. + - Indicate readiness for review by explicitly requesting a review from maintainers or reviewers. + If no action is taken within 7 days, this pull request will be closed." + close-pr-message: "This PR was closed due to prolonged inactivity." + remove-stale-when-updated: true # Remove the stale label if there is new activity + operations-per-run: 20 # Number of issues to process per run (default is 30) diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..6a4eb916 --- /dev/null +++ b/.gitignore @@ -0,0 +1,164 @@ +# application files and logs +/generated_cv +/log/* + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST +chrome_profile/* +data_folder/output/* +answers.json +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec +.venv +.pytest_cache +virtual + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ +_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv’s dependency resolution may lead to different +# Pipfile.lock files generated on each colleague’s machine. +# Thus, uncomment the following line if the pipenv environment is expected to be identical +# across all environments. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# MacOS +.DS_Store + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# PyCharm and all JetBrains IDEs +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 +.idea/ +*.iml + +# Visual Studio Code +.vscode/ + +# Visual Studio 2015/2017/2019/2022 +.vs/ +*.opendb +*.VC.db + +# User-specific files +*.suo +*.user +*.userosscache +*.sln.docstates + +# Mono Auto Generated Files +mono_crash.* + +job_applications/ \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..a9f3ab1b --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,201 @@ +# Contributing to Auto_Jobs_Applier_AIHawk + +## Table of Contents + +- [Issue Labels](#issue-labels) +- [Bug Reports](#bug-reports) +- [Feature Requests](#feature-requests) +- [Branch Rules](#branch-rules) +- [Version Control](#version-control) +- [Release Process](#release-process) +- [Roles](#roles) +- [Pull Request Process](#pull-request-process) +- [Code Style Guidelines](#code-style-guidelines) +- [Development Setup](#development-setup) +- [Testing](#testing) +- [Communication](#communication) +- [Development Diagrams](./docs/development_diagrams.md) + +Thank you for your interest in contributing to Auto_Jobs_Applier_AIHawk. This document provides guidelines for contributing to the project. + +## Issue Labels + +The project uses the following labels: + +- **bug**: Something isn't working correctly +- **enhancement**: New feature requests +- **good first issue**: Good for newcomers +- **help wanted**: Extra attention needed +- **documentation**: Documentation improvements + +## Bug Reports + +When submitting a bug report, please include: + +- A clear, descriptive title prefixed with [BUG] +- Steps to reproduce the issue +- Expected behavior +- Actual behavior +- Any error messages or screenshots +- Your environment details (OS, Python version, etc.) + +## Feature Requests + +For feature requests, please: + +- Prefix the title with [FEATURE] +- Include a feature summary +- Provide detailed feature description +- Explain your motivation for the feature +- List any alternatives you've considered + +## Branch Rules + +- `main` - Production-ready code, protected branch +- `develop` - Integration branch for features +- `feature/*` - New features +- `release/*` - Release preparation +- `bugfix/*` - Bug fixes for development +- `hotfix/*` - Emergency production fixes + +## Version Control + +- Semantic versioning: `vMAJOR.MINOR.PATCH` +- Release tags on `main` branch only +- Package versions match git tags + +## Release Process + +week one for `release/v4.1.0` + +- Planning meeting for `release/v4.1.0` with release scope and milestone objectives set by the maintainers. Release and maintainer meeting agendas and schedules are posted on the project repository [wiki](https://github.com/AIHawk/AIHawk/wiki) and shared in the `#releases` channel on Discord. +- `release/v4.0.0` release candidate ready for release +- `release/v4.0.0` merge into `develop`, `main` +- tag `main` as `release/v4.0.0` +- `release/v4.0.0` published to AIHawk/releases and PyPI as a package with release documentation +- delete `release/v4.0.0` branch + +release/v4.1.0 release weeks + +- Contributers work on issues and PRs, prioritizing next milestone +- Maintainers review PRs from `feature/*`, `bugfix/*` branches and issues, merging into `develop` +- Maintainers review PRs from `hotfix/*` branches and issues, merged into `main` and `develop`, `main` tagged and merged into `4.0.1` package and `release/v4.0.1` and `release/v4.1.0`, documentation is updated + +last week, release candidate + +- `develop` is frozen, only bug fixes +- create release branch `release/v4.1.0` from `develop` +- only bug fixes are merged into `release/v4.1.0` +- additional testing and release candidate review + +week one is repeated for `release/v4.2.0` + +```mermaid +gantt + title Release Cycle Process + dateFormat YYYY-MM-DD + section Retro/Plan + Planning release/v4.1.0 : 2025-01-01, 2d + Publish release/v4.0.0 :milestone, m1, 2025-01-01, 1d + + section Dev Cycle + Feature Development :2025-01-03, 27d + PR Reviews :2025-01-03, 27d + + section Release + Freeze develop :milestone, m3, 2025-01-30, 1d + Create release/v4.1.0 :milestone, m4, 2025-01-30, 1d + Bug Fixes Only :2025-01-30, 2d + RC Testing :2025-01-30, 2d + + section Next Cycle + Skip Weekend :2025-02-01, 2d + Planning release/v4.2.0 :2025-02-03, 2d + Publish release/v4.1.0 :milestone, m4, 2025-02-03, 1d +``` + +## Roles + +### Organization Owner + +- Has full access to all repositories +- Controls organization-wide settings and permissions +- Can set base permissions for all members +- Manages repository settings and collaborator access + +### Release Manager + +- Creates and manages release branch from develop +- Coordinates release cycles and versioning +- Merges release into main + +### Maintainer + +- Reviews and approves develop, feature PRs +- Triage issues, bugs, PRs +- Manages feature, bugfix PRs merge into develop +- Leads feature development, bug prioritization +- Manages README, CONTRIBUTING, and other documentation + +### Moderator + +- Moderates Telegram, Discord channels +- Manages project wiki +- Contributes to README, CONTRIBUTING, and other documentation + +### Contributor + +- Creates feature branches from develop +- Implements new features, bug fixes, and other changes +- creates PRs on features +- Collaborates with other developers on features + +## Pull Request Process + +1. Fork the repository +2. Create a new branch for your feature or bug +3. Write clear commit messages +4. Update documentation as needed +5. Add tests for new functionality +6. Ensure tests pass +7. Submit a pull request with a clear description + +## Merging Pull Requests + +- All PRs are reviewed by maintainers +- At least 2 Maintainers approve PRs for merge +- PRs are merged into `develop` +- PRs are tested and verified to work as expected + +## Code Style Guidelines + +- Follow PEP 8 standards for Python code +- Include docstrings for new functions and classes +- Add comments for complex logic +- Maintain consistent naming conventions +- Security best practices +- Any performance considerations + +## Development Setup + +1. Clone the repository +2. Install dependencies from requirements.txt +3. Set up necessary API keys and configurations + +## Testing + +Before submitting a PR: + +- Test your changes thoroughly +- Ensure existing tests pass +- Add new tests for new functionality +- Verify functionality with different configurations + +## Communication + +- Be respectful and constructive in discussions +- Use clear and concise language +- Reference relevant issues in commits and PRs +- Ask for help when needed + +The project maintainers reserve the right to reject any contribution that doesn't meet these guidelines or align with the project's goals. diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..9aa17f0b --- /dev/null +++ b/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2024 AI Hawk FOSS
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+ + + + + + +# AIHawk the first Jobs Applier AI Agent + + ![CI](https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk/actions/workflows/ci.yml/badge.svg) + +**🤖🔍 Your AI-powered job search assistant. Automate applications, get personalized recommendations, and land your dream job faster.** + +Join our community: [Telegram](https://t.me/AIhawkCommunity) (for Normal user) | [Discord](https://discord.gg/mMZcMTH9K6) (for Open Source contributors) + +[![Telegram](https://img.shields.io/badge/Telegram-2CA5E0?style=for-the-badge&logo=telegram&logoColor=white)](https://t.me/AIhawkCommunity) +[![Discord](https://img.shields.io/badge/Discord-7289DA?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/mMZcMTH9K6) + +
+ +**Creator** [feder-cr](https://github.com/feder-cr), Co-Founder of AIHawk
+As AIHawk is focusing on their proprietary product - solving problems in hiring for companies, currently this project is led, managed, and maintained by a group of open-source contributors, with a focus on building tools to help job seekers land the jobs they deserve. + +**Project Maintainers / Leads**: [surapuramakhil](https://github.com/surapuramakhil), [sarob](https://github.com/sarob), [cjbbb](https://github.com/cjbbb) + +We are looking to expand our FOSS maintainers team! If you are from a non-technical background, you can be part of the project management team, triaging issues, and shaping the project. If you are a technical person, you can join in doing code reviews, participating in releases, and buidling better version of this product. + +reach out to [surapuramakhil](https://github.com/surapuramakhil) on [Discord](https://discord.gg/mMZcMTH9K6). [Special thanks](#special-thanks) + +Auto_Jobs_Applier_AIHawk is continuously evolving, and your feedback, suggestions, and contributions are highly valued. Feel free to open issues, suggest enhancements, or submit pull requests to help improve the project. Let's work together to make Auto_Jobs_Applier_AIHawk a powerful tool for job seekers worldwide. + +## Table of Contents + +1. [Introduction](#introduction) +2. [Features](#features) +3. [Installation](#installation) +4. [Configuration](#configuration) +5. [Usage](#usage) +6. [Documentation](#documentation) +7. [Troubleshooting](#troubleshooting) +8. [Conclusion](#conclusion) +9. [Contributors](#contributors) +10. [License](#license) +11. [Disclaimer](#disclaimer) + +## Introduction + +Auto_Jobs_Applier_AIHawk is a cutting-edge, automated tool designed to revolutionize the job search and application process. In today's fiercely competitive job market, where opportunities can vanish in the blink of an eye, this program offers job seekers a significant advantage. By leveraging the power of automation and artificial intelligence, Auto_Jobs_Applier_AIHawk enables users to apply to a vast number of relevant positions efficiently and in a personalized manner, maximizing their chances of landing their dream job. + +### The Challenge of Modern Job Hunting + +In the digital age, the job search landscape has undergone a dramatic transformation. While online platforms have opened up a world of opportunities, they have also intensified competition. Job seekers often find themselves spending countless hours scrolling through listings, tailoring applications, and repetitively filling out forms. This process can be not only time-consuming but also emotionally draining, leading to job search fatigue and missed opportunities. + +### Enter Auto_Jobs_Applier_AIHawk: Your Personal Job Search Assistant + +Auto_Jobs_Applier_AIHawk steps in as a game-changing solution to these challenges. It's not just a tool; it's your tireless, 24/7 job search partner. By automating the most time-consuming aspects of the job search process, it allows you to focus on what truly matters - preparing for interviews and developing your professional skills. + +## Features + +1. **Intelligent Job Search Automation** + - Customizable search criteria + - Continuous scanning for new openings + - Smart filtering to exclude irrelevant listings + +2. **Rapid and Efficient Application Submission** + - One-click applications + - Form auto-fill using your profile information + - Automatic document attachment (resume, cover letter) + +3. **AI-Powered Personalization** + - Dynamic response generation for employer-specific questions + - Tone and style matching to fit company culture + - Keyword optimization for improved application relevance + +4. **Volume Management with Quality** + - Bulk application capability + - Quality control measures + - Detailed application tracking + +5. **Intelligent Filtering and Blacklisting** + - Company blacklist to avoid unwanted employers + - Title filtering to focus on relevant positions + +6. **Dynamic Resume Generation** + - Automatically creates tailored resumes for each application + - Customizes resume content based on job requirements + +7. **Secure Data Handling** + - Manages sensitive information securely using YAML files + +## Installation + +**Confirmed successful runs on the following:** + +- Operating Systems: + - Windows 10 + - Ubuntu 22 +- Python versions: + - 3.10 + - 3.11.9(64b) + - 3.12.5(64b) + +### Option 1: Using Python Virtual Environment + +1. **Download and Install Python:** + + Ensure you have the last Python version installed. If not, download and install it from Python's official website. For detailed instructions, refer to the tutorials: + + - [How to Install Python on Windows](https://www.geeksforgeeks.org/how-to-install-python-on-windows/) + - [How to Install Python on Linux](https://www.geeksforgeeks.org/how-to-install-python-on-linux/) + - [How to Download and Install Python on macOS](https://www.geeksforgeeks.org/how-to-download-and-install-python-latest-version-on-macos-mac-os-x/) + +2. **Download and Install Google Chrome:** + - Download and install the latest version of Google Chrome in its default location from the [official website](https://www.google.com/chrome). + +3. **Clone the repository:** + + ```bash + git clone https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk.git + + cd Auto_Jobs_Applier_AIHawk + ``` + +4. **Activate virtual environment:** + + ```bash + python3 -m venv virtual + ``` + + ```bash + source virtual/bin/activate + ``` + + or for Windows-based machines - + + ```bash + .\virtual\Scripts\activate + ``` + +5. **Install the required packages:** + + ```bash + pip install -r requirements.txt + ``` + +### Option 2: Using Conda + +1. **Install Conda:** + - Download and install Miniconda from the [official website](https://docs.conda.io/en/latest/miniconda.html) + - Or install Anaconda from the [Anaconda website](https://www.anaconda.com/download) + +2. **Create and activate conda environment:** + ```bash + # Create new environment + conda create -n aihawk python=3.11 + + # Activate environment + conda activate aihawk + ``` + +3. **Clone the repository:** + ```bash + git clone https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk.git + cd Auto_Jobs_Applier_AIHawk + ``` + +4. **Install dependencies:** + ```bash + # Install from requirements.txt + pip install -r requirements.txt + ``` + + +## Configuration + +### 1. secrets.yaml + +This file contains sensitive information. Never share or commit this file to version control. + +- `llm_api_key: [Your OpenAI or Ollama API key or Gemini API key]` + - Replace with your OpenAI API key for GPT integration + - To obtain an API key, follow the tutorial at: + - Note: You need to add credit to your OpenAI account to use the API. You can add credit by visiting the [OpenAI billing dashboard](https://platform.openai.com/account/billing). + - According to the [OpenAI community](https://community.openai.com/t/usage-tier-free-to-tier-1/919150) and our users' reports, right after setting up the OpenAI account and purchasing the required credits, users still have a `Free` account type. This prevents them from having unlimited access to OpenAI models and allows only 200 requests per day. This might cause runtime errors such as: + `Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. ...}}` + `{'error': {'message': 'Rate limit reached for gpt-4o-mini in organization on requests per day (RPD): Limit 200, Used 200, Requested 1.}}` + OpenAI will update your account automatically, but it might take some time, ranging from a couple of hours to a few days. + You can find more about your organization limits on the [official page](https://platform.openai.com/settings/organization/limits). + - For obtaining Gemini API key visit [Google AI for Devs](https://ai.google.dev/gemini-api/docs/api-key) + +### 2. work_preferences.yaml + +This file defines your job search parameters and bot behavior. Each section contains options that you can customize: + +- `remote: [true/false]` + + - Set to `true` to include remote jobs, `false` to exclude them + +- `hybrid: [true/false]` + + - Set to `true` to include hybrid jobs, `false` to exclude them + +- `onsite: [true/false]` + + - Set to `true` to include onsite jobs, `false` to exclude them + +- `experience_level:` + + - Set desired experience levels to `true`, others to `false` + +- `job_types:` + - Set desired job types to `true`, others to `false` + +- `date:` + - Choose one time range for job postings by setting it to `true`, others to `false` + +- `positions:` + - List job titles you're interested in, one per line + - Example: + + ```yaml + positions: + - Software Developer + - Data Scientist + ``` + +- `locations:` + - List locations you want to search in, one per line + - Example: + + ```yaml + locations: + - Italy + - London + ``` + +- `apply_once_at_company: [True/False]` + - Set to `True` to apply only once per company, `False` to allow multiple applications per company + +- `distance: [number]` + - Set the radius for your job search in miles + - Example: `distance: 50` + +- `companyBlacklist:` + - List companies you want to exclude from your search, one per line + - Example: + + ```yaml + companyBlacklist: + - Company X + - Company Y + ``` + +- `titleBlacklist:` + - List keywords in job titles you want to avoid, one per line + - Example: + + ```yaml + titleBlacklist: + - Sales + - Marketing + ``` + +#### 2.1 config.py - Customize LLM model endpoint + +- `LLM_MODEL_TYPE`: + - Choose the model type, supported: openai / ollama / claude / gemini +- `LLM_MODEL`: + - Choose the LLM model, currently supported: + - openai: gpt-4o + - ollama: llama2, mistral:v0.3 + - claude: any model + - gemini: any model +- `LLM_API_URL`: + - Link of the API endpoint for the LLM model + - openai: + - ollama: + - claude: + - gemini: +- Note: To run local Ollama, follow the guidelines here: [Guide to Ollama deployment](https://github.com/ollama/ollama) + +### 3. plain_text_resume.yaml + +This file contains your resume information in a structured format. Fill it out with your personal details, education, work experience, and skills. This information is used to auto-fill application forms and generate customized resumes. + +Each section has specific fields to fill out: + +- `personal_information:` + - This section contains basic personal details to identify yourself and provide contact information. + - **name**: Your first name. + - **surname**: Your last name or family name. + - **date_of_birth**: Your birth date in the format DD/MM/YYYY. + - **country**: The country where you currently reside. + - **city**: The city where you currently live. + - **address**: Your full address, including street and number. + - **zip_code**: Your postal/ZIP code. + - **phone_prefix**: The international dialing code for your phone number (e.g., +1 for the USA, +44 for the UK). + - **phone**: Your phone number without the international prefix. + - **email**: Your primary email address. + - **github**: URL to your GitHub profile, if applicable. + - **linkedin**: URL to your LinkedIn profile, if applicable. + - Example + + ```yaml + personal_information: + name: "Jane" + surname: "Doe" + date_of_birth: "01/01/1990" + country: "USA" + city: "New York" + address: "123 Main St" + zip_code: "520123" + phone_prefix: "+1" + phone: "5551234567" + email: "jane.doe@example.com" + github: "https://github.com/janedoe" + linkedin: "https://www.linkedin.com/in/janedoe/" + ``` + +- `education_details:` + - This section outlines your academic background, including degrees earned and relevant coursework. + - **degree**: The type of degree obtained (e.g., Bachelor's Degree, Master's Degree). + - **university**: The name of the university or institution where you studied. + - **final_evaluation_grade**: Your Grade Point Average or equivalent measure of academic performance. + - **start_date**: The start year of your studies. + - **graduation_year**: The year you graduated. + - **field_of_study**: The major or focus area of your studies. + - **exam**: A list of courses or subjects taken along with their respective grades. + + - Example: + + ```yaml + education_details: + - education_level: "Bachelor's Degree" + institution: "University of Example" + field_of_study: "Software Engineering" + final_evaluation_grade: "4/4" + start_date: "2021" + year_of_completion: "2023" + exam: + Algorithms: "A" + Data Structures: "B+" + Database Systems: "A" + Operating Systems: "A-" + Web Development: "B" + ``` + +- `experience_details:` + - This section details your work experience, including job roles, companies, and key responsibilities. + - **position**: Your job title or role. + - **company**: The name of the company or organization where you worked. + - **employment_period**: The timeframe during which you were employed in the role, using the format MM/YYYY - MM/YYYY. + - **location**: The city and country where the company is located. + - **industry**: The industry or field in which the company operates. + - **key_responsibilities**: A list of major responsibilities or duties you had in the role, e.g. responsibility: "Developed web applications using React and Node.js". + - **skills_acquired**: Skills or expertise gained through this role, e.g. "React". + + - Example: + + ```yaml + experience_details: + - position: "Software Developer" + company: "Tech Innovations Inc." + employment_period: "06/2021 - Present" + location: "San Francisco, CA" + industry: "Technology" + key_responsibilities: + - responsibility: "Developed web applications using React and Node.js" + - responsibility: "Collaborated with cross-functional teams to design and implement new features" + - responsibility: "Troubleshot and resolved complex software issues" + skills_acquired: + - "React" + - "Node.js" + - "Software Troubleshooting" + ``` + +- `projects:` + - Include notable projects you have worked on, including personal or professional projects. + - **name**: The name or title of the project. + - **description**: A brief summary of what the project involves or its purpose. + - **link**: URL to the project, if available (e.g., GitHub repository, website). + + - Example: + + ```yaml + projects: + - name: "Weather App" + description: "A web application that provides real-time weather information using a third-party API." + link: "https://github.com/janedoe/weather-app" + - name: "Task Manager" + description: "A task management tool with features for tracking and prioritizing tasks." + link: "https://github.com/janedoe/task-manager" + ``` + +- `achievements:` + - Highlight notable accomplishments or awards you have received. + - **name**: The title or name of the achievement. + - **description**: A brief explanation of the achievement and its significance. + + - Example: + + ```yaml + achievements: + - name: "Employee of the Month" + description: "Recognized for exceptional performance and contributions to the team." + - name: "Hackathon Winner" + description: "Won first place in a national hackathon competition." + ``` + +- `certifications:` + - Include any professional certifications you have earned. + - name: "PMP" + description: "Certification for project management professionals, issued by the Project Management Institute (PMI)" + + - Example: + + ```yaml + certifications: + - "Certified Scrum Master" + - "AWS Certified Solutions Architect" + ``` + +- `languages:` + - Detail the languages you speak and your proficiency level in each. + - **language**: The name of the language. + - **proficiency**: Your level of proficiency (e.g., Native, Fluent, Intermediate). + + - Example: + + ```yaml + languages: + - language: "English" + proficiency: "Fluent" + - language: "Spanish" + proficiency: "Intermediate" + ``` + +- `interests:` + + - Mention your professional or personal interests that may be relevant to your career. + - **interest**: A list of interests or hobbies. + + - Example: + + ```yaml + interests: + - "Machine Learning" + - "Cybersecurity" + - "Open Source Projects" + - "Digital Marketing" + - "Entrepreneurship" + ``` + +- `availability:` + - State your current availability or notice period. + - **notice_period**: The amount of time required before you can start a new role (e.g., "2 weeks", "1 month"). + + - Example: + + ```yaml + availability: + notice_period: "2 weeks" + ``` + +- `salary_expectations:` + - Provide your expected salary range. + - **salary_range_usd**: The salary range you are expecting, expressed in USD. + + - Example: + + ```yaml + salary_expectations: + salary_range_usd: "80000 - 100000" + ``` + +- `self_identification:` + - Provide information related to personal identity, including gender and pronouns. + - **gender**: Your gender identity. + - **pronouns**: The pronouns you use (e.g., He/Him, She/Her, They/Them). + - **veteran**: Your status as a veteran (e.g., Yes, No). + - **disability**: Whether you have a disability (e.g., Yes, No). + - **ethnicity**: Your ethnicity. + + - Example: + + ```yaml + self_identification: + gender: "Female" + pronouns: "She/Her" + veteran: "No" + disability: "No" + ethnicity: "Asian" + ``` + +- `legal_authorization:` + - Indicate your legal ability to work in various locations. + - **eu_work_authorization**: Whether you are authorized to work in the European Union (Yes/No). + - **us_work_authorization**: Whether you are authorized to work in the United States (Yes/No). + - **requires_us_visa**: Whether you require a visa to work in the United States (Yes/No). + - **requires_us_sponsorship**: Whether you require sponsorship to work in the United States (Yes/No). + - **requires_eu_visa**: Whether you require a visa to work in the European Union (Yes/No). + - **legally_allowed_to_work_in_eu**: Whether you are legally allowed to work in the European Union (Yes/No). + - **legally_allowed_to_work_in_us**: Whether you are legally allowed to work in the United States (Yes/No). + - **requires_eu_sponsorship**: Whether you require sponsorship to work in the European Union (Yes/No). + - **canada_work_authorization**: Whether you are authorized to work in Canada (Yes/No). + - **requires_canada_visa**: Whether you require a visa to work in Canada (Yes/No). + - **legally_allowed_to_work_in_canada**: Whether you are legally allowed to work in Canada (Yes/No). + - **requires_canada_sponsorship**: Whether you require sponsorship to work in Canada (Yes/No). + - **uk_work_authorization**: Whether you are authorized to work in the United Kingdom (Yes/No). + - **requires_uk_visa**: Whether you require a visa to work in the United Kingdom (Yes/No). + - **legally_allowed_to_work_in_uk**: Whether you are legally allowed to work in the United Kingdom (Yes/No). + - **requires_uk_sponsorship**: Whether you require sponsorship to work in the United Kingdom (Yes/No). + + - Example: + + ```yaml + legal_authorization: + eu_work_authorization: "Yes" + us_work_authorization: "Yes" + requires_us_visa: "No" + requires_us_sponsorship: "Yes" + requires_eu_visa: "No" + legally_allowed_to_work_in_eu: "Yes" + legally_allowed_to_work_in_us: "Yes" + requires_eu_sponsorship: "No" + canada_work_authorization: "Yes" + requires_canada_visa: "No" + legally_allowed_to_work_in_canada: "Yes" + requires_canada_sponsorship: "No" + uk_work_authorization: "Yes" + requires_uk_visa: "No" + legally_allowed_to_work_in_uk: "Yes" + requires_uk_sponsorship: "No" + ``` + +- `work_preferences:` + - Specify your preferences for work arrangements and conditions. + - **remote_work**: Whether you are open to remote work (Yes/No). + - **in_person_work**: Whether you are open to in-person work (Yes/No). + - **open_to_relocation**: Whether you are willing to relocate for a job (Yes/No). + - **willing_to_complete_assessments**: Whether you are willing to complete job assessments (Yes/No). + - **willing_to_undergo_drug_tests**: Whether you are willing to undergo drug testing (Yes/No). + - **willing_to_undergo_background_checks**: Whether you are willing to undergo background checks (Yes/No). + + - Example: + + ```yaml + work_preferences: + remote_work: "Yes" + in_person_work: "No" + open_to_relocation: "Yes" + willing_to_complete_assessments: "Yes" + willing_to_undergo_drug_tests: "No" + willing_to_undergo_background_checks: "Yes" + ``` + +### PLUS. data_folder_example + +The `data_folder_example` folder contains a working example of how the files necessary for the bot's operation should be structured and filled out. This folder serves as a practical reference to help you correctly set up your work environment for the job search bot. + +#### Contents + +Inside this folder, you'll find example versions of the key files: + +- `secrets.yaml` +- `config.yaml` +- `plain_text_resume.yaml` + +These files are already populated with fictitious but realistic data. They show you the correct format and type of information to enter in each file. + +#### Using the data_folder_example + +Using this folder as a guide can be particularly helpful for: + +1. Understanding the correct structure of each configuration file +2. Seeing examples of valid data for each field +3. Having a reference point while filling out your personal files + +## Usage + +0. **Account language** + To ensure the bot works, your account language must be set to English. + +1. **Data Folder:** + Ensure that your data_folder contains the following files: + - `secrets.yaml` + - `config.yaml` + - `plain_text_resume.yaml` + +2. **Output Folder:** + Contains the output of the bot. + - `data.json` results of the --collect mode + - `failed.json` failed applications + - `open_ai_calls.json` all the calls made to the LLM model + - `skipped.json` applications that were skipped + - `success.json` successful applications + + **Note:** `answers.json` is not part of the output folder and can be found in the root of the project. It is used to store the answers of the questions asked to the user. Can be used to update the bot with corrected answers. Search for `Select an option`, `0`, `Authorized`, and `how many years of` to verify correct answers. + +3. **Run the Bot:** + + Auto_Jobs_Applier_AIHawk offers flexibility in how it handles your pdf resume: + +- **Dynamic Resume Generation:** + If you don't use the `--resume` option, the bot will automatically generate a unique resume for each application. This feature uses the information from your `plain_text_resume.yaml` file and tailors it to each specific job application, potentially increasing your chances of success by customizing your resume for each position. + + ```bash + python main.py + ``` + +- **Using a Specific Resume:** + If you want to use a specific PDF resume for all applications, place your resume PDF in the `data_folder` directory and run the bot with the `--resume` option: + + ```bash + python main.py --resume /path/to/your/resume.pdf + ``` + +- **Using the collect mode:** + If you want to collect job data only to perform any type of data analytics you can use the bot with the `--collect` option. This will store in output/data.json file all data found from linkedin jobs offers. + + ```bash + python main.py --collect + ``` + +### Troubleshooting + +#### 1. OpenAI API Rate Limit Errors + +**Error Message:** + +openai.RateLimitError: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: .', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}} + +**Solution:** + +- Check your OpenAI API billing settings at +- Ensure you have added a valid payment method to your OpenAI account +- Note that ChatGPT Plus subscription is different from API access +- If you've recently added funds or upgraded, wait 12-24 hours for changes to take effect +- Free tier has a 3 RPM limit; spend at least $5 on API usage to increase + +#### 2. Easy Apply Button Not Found + +**Error Message:** + +Exception: No clickable 'Easy Apply' button found + +**Solution:** + +- Ensure that you're logged properly +- Check if the job listings you're targeting actually have the "Easy Apply" option +- Verify that your search parameters in the `config.yaml` file are correct and returning jobs with the "Easy Apply" button +- Try increasing the wait time for page loading in the script to ensure all elements are loaded before searching for the button + +#### 3. Incorrect Information in Job Applications + +**Issue:** Bot provides inaccurate data for experience, CTC, and notice period + +**Solution:** + +- Update prompts for professional experience specificity +- Add fields in `config.yaml` for current CTC, expected CTC, and notice period +- Modify bot logic to use these new config fields + +#### 4. YAML Configuration Errors + +**Error Message:** + +yaml.scanner.ScannerError: while scanning a simple key + +**Solution:** + +- Copy example `config.yaml` and modify gradually +- Ensure proper YAML indentation and spacing +- Use a YAML validator tool +- Avoid unnecessary special characters or quotes + +#### 5. Bot Logs In But Doesn't Apply to Jobs + +**Issue:** Bot searches for jobs but continues scrolling without applying + +**Solution:** + +- Check for security checks or CAPTCHAs +- Verify `config.yaml` job search parameters +- Ensure your account profile meets job requirements +- Review console output for error messages + +### General Troubleshooting Tips + +- Use the latest version of the script +- Verify all dependencies are installed and updated +- Check internet connection stability +- Clear browser cache and cookies if issues persist + +For further assistance, please create an issue on the [GitHub repository](https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk/issues) with detailed information about your problem, including error messages and your configuration (with sensitive information removed). + +## Documentation + +### For Users + +- Ollama & Gemini Setup + - To install and configure **Ollama** and **Gemini**, [Download Ollama and Gemini Setup Guide (PDF)](https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk/blob/main/docs/guide_to_setup_ollama_and_gemini.pdf) + - Follow the instructions in these guides to ensure proper configuration of **AIHawk** with **Ollama** and **Gemini**. + - Written by Rushi, [Linkedin](https://www.linkedin.com/in/rushichaganti/), support him by following. + +- Editing YAML Files + - For detailed instructions on editing YAML configuration sections for **AIHawk**, refer to this document: + - [Download YAML Editing Guide (PDF)](https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk/blob/main/docs/guide_yaml_sections.pdf) + - Written by Rushi, [Linkedin](https://www.linkedin.com/in/rushichaganti/), support him by following. + +- Auto-start AIHawk + - To make **AIHawk** automatically start when your system boots, follow the steps in this guide: + - [Download Auto-start AIHawk Guide (PDF)](https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk/blob/main/docs/guide_to_autostart_aihawk.pdf) + - Written by Rushi, [Linkedin](https://www.linkedin.com/in/rushichaganti/), support him by following. + +- Video Tutorial + - [How to set up Auto_Jobs_Applier_AIHawk](https://youtu.be/gdW9wogHEUM) + - Written by Rushi, [Linkedin](https://www.linkedin.com/in/rushichaganti/), support him by following. + +- [OpenAI API Documentation](https://platform.openai.com/docs/) + +### For Developers + +- [Contribution Guidelines](CONTRIBUTING.md) + +- [Lang Chain Developer Documentation](https://python.langchain.com/v0.2/docs/integrations/components/) + +- [Workflow diagrams](docs/workflow_diagrams.md) + +- If you encounter any issues, you can open an issue on [GitHub](https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk/issues). + Please add valuable details to the subject and to the description. If you need new feature then please reflect this. + I'll be more than happy to assist you! + +- Note for Contributors: If you would like to submit a Pull Request (PR), please target the `release` branch instead of `main`. The `release` branch is used for testing new code changes and will be periodically merged into `main` after validation. This approach ensures that only tested features make it into the main branch. + +## Conclusion + +Auto_Jobs_Applier_AIHawk provides a significant advantage in the modern job market by automating and enhancing the job application process. With features like dynamic resume generation and AI-powered personalization, it offers unparalleled flexibility and efficiency. Whether you're a job seeker aiming to maximize your chances of landing a job, a recruiter looking to streamline application submissions, or a career advisor seeking to offer better services, Auto_Jobs_Applier_AIHawk is an invaluable resource. By leveraging cutting-edge automation and artificial intelligence, this tool not only saves time but also significantly increases the effectiveness and quality of job applications in today's competitive landscape. + +## Star History + +[![Star History Chart](https://api.star-history.com/svg?repos=feder-cr/Auto_Jobs_Applier_AIHawk&type=Date)](https://star-history.com/#feder-cr/Auto_Jobs_Applier_AIHawk&Date) + +If you like the project please star ⭐ the repository! + +## Special Thanks +[![Contributors](https://img.shields.io/github/contributors/feder-cr/Auto_Jobs_Applier_AIHawk)](https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk/graphs/contributors) + + + + + +Made with [contrib.rocks](https://contrib.rocks). + +## License + +This project is licensed under the AGPL License. Documentation is licensed under CC BY - see the [AGPL LICENSE](LICENSE) and [CC BY LICENSE](docs/LICENSE) files for details. + +The AGPL License requires that any derivative work must also be open source and distributed under the same license. + +The CC BY License permits others to distribute, remix, adapt, and build upon your work, even for commercial purposes, as long as they credit you for the original creation. + + +## Disclaimer + +This tool, Auto_Jobs_Applier_AIHawk, is intended for use at your own risk. The creators / maintainers / contributors assume no responsibility for any consequences arising from its use. Users are advised to comply with the terms of service of relevant platforms and adhere to all applicable laws, regulations, and ethical guidelines. The use of automated tools for job applications may carry risks, including potential impacts on user accounts. 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zU&vAAFmpe>-*fNWOfXAPrU!=!+~8}#P9mcZ*Hv&v&;m;ob(8tlL1v>|2xeII7kPH zLFk(5qHr@58UHP$O%mzSS=<$eQV#=?hYSe9RrzOw!KcmQNM|_d4@!D(3K;r?ibx3j zHu~dltuVGwU2VParLHXYsXLvz)Fc!5{4Q5uAw!npu9%m-KM_L}_PQ?HWHIV0wcj>Vw>i(d-A?*||Pl(7{ zARtIAD2xpFeGVqUiRVwmKR*-f{eMF!|KX bool: + """Validate the format of an email address.""" + return bool(ConfigValidator.EMAIL_REGEX.match(email)) + + @staticmethod + def load_yaml(yaml_path: Path) -> dict: + """Load and parse a YAML file.""" + try: + with open(yaml_path, "r") as stream: + return yaml.safe_load(stream) + except yaml.YAMLError as exc: + raise ConfigError(f"Error reading YAML file {yaml_path}: {exc}") + except FileNotFoundError: + raise ConfigError(f"YAML file not found: {yaml_path}") + + @classmethod + def validate_config(cls, config_yaml_path: Path) -> dict: + """Validate the main configuration YAML file.""" + parameters = cls.load_yaml(config_yaml_path) + # Check for required keys and their types + for key, expected_type in cls.REQUIRED_CONFIG_KEYS.items(): + if key not in parameters: + if key in ["company_blacklist", "title_blacklist", "location_blacklist"]: + parameters[key] = [] + else: + raise ConfigError(f"Missing required key '{key}' in {config_yaml_path}") + elif not isinstance(parameters[key], expected_type): + if key in ["company_blacklist", "title_blacklist", "location_blacklist"] and parameters[key] is None: + parameters[key] = [] + else: + raise ConfigError( + f"Invalid type for key '{key}' in {config_yaml_path}. Expected {expected_type.__name__}." + ) + cls._validate_experience_levels(parameters["experience_level"], config_yaml_path) + cls._validate_job_types(parameters["job_types"], config_yaml_path) + cls._validate_date_filters(parameters["date"], config_yaml_path) + cls._validate_list_of_strings(parameters, ["positions", "locations"], config_yaml_path) + cls._validate_distance(parameters["distance"], config_yaml_path) + cls._validate_blacklists(parameters, config_yaml_path) + return parameters + + @classmethod + def _validate_experience_levels(cls, experience_levels: dict, config_path: Path): + """Ensure experience levels are booleans.""" + for level in cls.EXPERIENCE_LEVELS: + if not isinstance(experience_levels.get(level), bool): + raise ConfigError( + f"Experience level '{level}' must be a boolean in {config_path}" + ) + + @classmethod + def _validate_job_types(cls, job_types: dict, config_path: Path): + """Ensure job types are booleans.""" + for job_type in cls.JOB_TYPES: + if not isinstance(job_types.get(job_type), bool): + raise ConfigError( + f"Job type '{job_type}' must be a boolean in {config_path}" + ) + + @classmethod + def _validate_date_filters(cls, date_filters: dict, config_path: Path): + """Ensure date filters are booleans.""" + for date_filter in cls.DATE_FILTERS: + if not isinstance(date_filters.get(date_filter), bool): + raise ConfigError( + f"Date filter '{date_filter}' must be a boolean in {config_path}" + ) + + @classmethod + def _validate_list_of_strings(cls, parameters: dict, keys: list, config_path: Path): + """Ensure specified keys are lists of strings.""" + for key in keys: + if not all(isinstance(item, str) for item in parameters[key]): + raise ConfigError( + f"'{key}' must be a list of strings in {config_path}" + ) + + @classmethod + def _validate_distance(cls, distance: int, config_path: Path): + """Validate the distance value.""" + if distance not in cls.APPROVED_DISTANCES: + raise ConfigError( + f"Invalid distance value '{distance}' in {config_path}. Must be one of: {cls.APPROVED_DISTANCES}" + ) + + @classmethod + def _validate_blacklists(cls, parameters: dict, config_path: Path): + """Ensure blacklists are lists.""" + for blacklist in ["company_blacklist", "title_blacklist", "location_blacklist"]: + if not isinstance(parameters.get(blacklist), list): + raise ConfigError( + f"'{blacklist}' must be a list in {config_path}" + ) + if parameters[blacklist] is None: + parameters[blacklist] = [] + + @staticmethod + def validate_secrets(secrets_yaml_path: Path) -> str: + """Validate the secrets YAML file and retrieve the LLM API key.""" + secrets = ConfigValidator.load_yaml(secrets_yaml_path) + mandatory_secrets = ["llm_api_key"] + + for secret in mandatory_secrets: + if secret not in secrets: + raise ConfigError(f"Missing secret '{secret}' in {secrets_yaml_path}") + + if not secrets[secret]: + raise ConfigError(f"Secret '{secret}' cannot be empty in {secrets_yaml_path}") + + return secrets["llm_api_key"] + + +class FileManager: + """Handles file system operations and validations.""" + + REQUIRED_FILES = [SECRETS_YAML, WORK_PREFERENCES_YAML, PLAIN_TEXT_RESUME_YAML] + + @staticmethod + def validate_data_folder(app_data_folder: Path) -> Tuple[Path, Path, Path, Path]: + """Validate the existence of the data folder and required files.""" + if not app_data_folder.is_dir(): + raise FileNotFoundError(f"Data folder not found: {app_data_folder}") + + missing_files = [file for file in FileManager.REQUIRED_FILES if not (app_data_folder / file).exists()] + if missing_files: + raise FileNotFoundError(f"Missing files in data folder: {', '.join(missing_files)}") + + output_folder = app_data_folder / "output" + output_folder.mkdir(exist_ok=True) + + return ( + app_data_folder / SECRETS_YAML, + app_data_folder / WORK_PREFERENCES_YAML, + app_data_folder / PLAIN_TEXT_RESUME_YAML, + output_folder, + ) + + @staticmethod + def get_uploads(plain_text_resume_file: Path) -> Dict[str, Path]: + """Convert resume file paths to a dictionary.""" + if not plain_text_resume_file.exists(): + raise FileNotFoundError(f"Plain text resume file not found: {plain_text_resume_file}") + + uploads = {"plainTextResume": plain_text_resume_file} + + return uploads + + +def create_cover_letter(parameters: dict, llm_api_key: str): + """ + Logic to create a CV. + """ + try: + logger.info("Generating a CV based on provided parameters.") + + # Carica il resume in testo semplice + with open(parameters["uploads"]["plainTextResume"], "r", encoding="utf-8") as file: + plain_text_resume = file.read() + + style_manager = StyleManager() + style_manager.choose_style() + questions = [ + inquirer.Text('job_url', message="Please enter the URL of the job description:") + ] + answers = inquirer.prompt(questions) + job_url = answers.get('job_url') + resume_generator = ResumeGenerator() + resume_object = Resume(plain_text_resume) + driver = init_browser() + resume_generator.set_resume_object(resume_object) + resume_facade = ResumeFacade( + api_key=llm_api_key, + style_manager=style_manager, + resume_generator=resume_generator, + resume_object=resume_object, + output_path=Path("data_folder/output"), + ) + resume_facade.set_driver(driver) + resume_facade.link_to_job(job_url) + result_base64, suggested_name = resume_facade.create_cover_letter() + + # Decodifica Base64 in dati binari + try: + pdf_data = base64.b64decode(result_base64) + except base64.binascii.Error as e: + logger.error("Error decoding Base64: %s", e) + raise + + # Definisci il percorso della cartella di output utilizzando `suggested_name` + output_dir = Path(parameters["outputFileDirectory"]) / suggested_name + + # Crea la cartella se non esiste + try: + output_dir.mkdir(parents=True, exist_ok=True) + logger.info(f"Cartella di output creata o già esistente: {output_dir}") + except IOError as e: + logger.error("Error creating output directory: %s", e) + raise + + output_path = output_dir / "cover_letter_tailored.pdf" + try: + with open(output_path, "wb") as file: + file.write(pdf_data) + logger.info(f"CV salvato in: {output_path}") + except IOError as e: + logger.error("Error writing file: %s", e) + raise + except Exception as e: + logger.exception(f"An error occurred while creating the CV: {e}") + raise + + +def create_resume_pdf_job_tailored(parameters: dict, llm_api_key: str): + """ + Logic to create a CV. + """ + try: + logger.info("Generating a CV based on provided parameters.") + + # Carica il resume in testo semplice + with open(parameters["uploads"]["plainTextResume"], "r", encoding="utf-8") as file: + plain_text_resume = file.read() + + style_manager = StyleManager() + style_manager.choose_style() + questions = [inquirer.Text('job_url', message="Please enter the URL of the job description:")] + answers = inquirer.prompt(questions) + job_url = answers.get('job_url') + resume_generator = ResumeGenerator() + resume_object = Resume(plain_text_resume) + driver = init_browser() + resume_generator.set_resume_object(resume_object) + resume_facade = ResumeFacade( + api_key=llm_api_key, + style_manager=style_manager, + resume_generator=resume_generator, + resume_object=resume_object, + output_path=Path("data_folder/output"), + ) + resume_facade.set_driver(driver) + resume_facade.link_to_job(job_url) + result_base64, suggested_name = resume_facade.create_resume_pdf_job_tailored() + + # Decodifica Base64 in dati binari + try: + pdf_data = base64.b64decode(result_base64) + except base64.binascii.Error as e: + logger.error("Error decoding Base64: %s", e) + raise + + # Definisci il percorso della cartella di output utilizzando `suggested_name` + output_dir = Path(parameters["outputFileDirectory"]) / suggested_name + + # Crea la cartella se non esiste + try: + output_dir.mkdir(parents=True, exist_ok=True) + logger.info(f"Cartella di output creata o già esistente: {output_dir}") + except IOError as e: + logger.error("Error creating output directory: %s", e) + raise + + output_path = output_dir / "resume_tailored.pdf" + try: + with open(output_path, "wb") as file: + file.write(pdf_data) + logger.info(f"CV salvato in: {output_path}") + except IOError as e: + logger.error("Error writing file: %s", e) + raise + except Exception as e: + logger.exception(f"An error occurred while creating the CV: {e}") + raise + + +def create_resume_pdf(parameters: dict, llm_api_key: str): + """ + Logic to create a CV. + """ + try: + logger.info("Generating a CV based on provided parameters.") + + # Carica il resume in testo semplice + with open(parameters["uploads"]["plainTextResume"], "r", encoding="utf-8") as file: + plain_text_resume = file.read() + + style_manager = StyleManager() + style_manager.choose_style() + questions = [inquirer.Text('job_url', message="Please enter the URL of the job description:")] + answers = inquirer.prompt(questions) + job_url = answers.get('job_url') + resume_generator = ResumeGenerator() + resume_object = Resume(plain_text_resume) + driver = init_browser() + resume_generator.set_resume_object(resume_object) + resume_facade = ResumeFacade( + api_key=llm_api_key, + style_manager=style_manager, + resume_generator=resume_generator, + resume_object=resume_object, + output_path=Path("data_folder/output"), + ) + resume_facade.set_driver(driver) + resume_facade.link_to_job(job_url) + result_base64, suggested_name = resume_facade.create_resume_pdf() + + # Decodifica Base64 in dati binari + try: + pdf_data = base64.b64decode(result_base64) + except base64.binascii.Error as e: + logger.error("Error decoding Base64: %s", e) + raise + + # Definisci il percorso della cartella di output utilizzando `suggested_name` + output_dir = Path(parameters["outputFileDirectory"]) / suggested_name + + # Crea la cartella se non esiste + try: + output_dir.mkdir(parents=True, exist_ok=True) + logger.info(f"Cartella di output creata o già esistente: {output_dir}") + except IOError as e: + logger.error("Error creating output directory: %s", e) + raise + + output_path = output_dir / "resume.pdf" + try: + with open(output_path, "wb") as file: + file.write(pdf_data) + logger.info(f"CV salvato in: {output_path}") + except IOError as e: + logger.error("Error writing file: %s", e) + raise + except Exception as e: + logger.exception(f"An error occurred while creating the CV: {e}") + raise + +def handle_inquiries(selected_actions: List[str], parameters: dict, llm_api_key: str): + """ + Decide which function to call based on the selected user actions. + + :param selected_actions: List of actions selected by the user. + :param parameters: Configuration parameters dictionary. + :param llm_api_key: API key for the language model. + """ + try: + if selected_actions: + if "Generate Resume" in selected_actions: + logger.info("Crafting a standout professional resume...") + create_resume_pdf(parameters, llm_api_key) + + if "Generate Resume Tailored for Job Description" in selected_actions: + logger.info("Customizing your resume to enhance your job application...") + create_resume_pdf_job_tailored(parameters, llm_api_key) + + if "Generate Tailored Cover Letter for Job Description" in selected_actions: + logger.info("Designing a personalized cover letter to enhance your job application...") + create_cover_letter(parameters, llm_api_key) + + else: + logger.warning("No actions selected. Nothing to execute.") + except Exception as e: + logger.exception(f"An error occurred while handling inquiries: {e}") + raise + +def prompt_user_action() -> str: + """ + Use inquirer to ask the user which action they want to perform. + + :return: Selected action. + """ + try: + questions = [ + inquirer.List( + 'action', + message="Select the action you want to perform:", + choices=[ + "Generate Resume", + "Generate Resume Tailored for Job Description", + "Generate Tailored Cover Letter for Job Description", + ], + ), + ] + answer = inquirer.prompt(questions) + if answer is None: + print("No answer provided. The user may have interrupted.") + return "" + return answer.get('action', "") + except Exception as e: + print(f"An error occurred: {e}") + return "" + + +def main(): + """Main entry point for the AIHawk Job Application Bot.""" + try: + # Define and validate the data folder + data_folder = Path("data_folder") + secrets_file, config_file, plain_text_resume_file, output_folder = FileManager.validate_data_folder(data_folder) + + # Validate configuration and secrets + config = ConfigValidator.validate_config(config_file) + llm_api_key = ConfigValidator.validate_secrets(secrets_file) + + # Prepare parameters + config["uploads"] = FileManager.get_uploads(plain_text_resume_file) + config["outputFileDirectory"] = output_folder + + # Interactive prompt for user to select actions + selected_actions = prompt_user_action() + + # Handle selected actions and execute them + handle_inquiries(selected_actions, config, llm_api_key) + + except ConfigError as ce: + logger.error(f"Configuration error: {ce}") + logger.error( + "Refer to the configuration guide for troubleshooting: " + "https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk?tab=readme-ov-file#configuration" + ) + except FileNotFoundError as fnf: + logger.error(f"File not found: {fnf}") + logger.error("Ensure all required files are present in the data folder.") + except RuntimeError as re: + logger.error(f"Runtime error: {re}") + logger.debug(traceback.format_exc()) + except Exception as e: + logger.exception(f"An unexpected error occurred: {e}") + + +if __name__ == "__main__": + main() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..76214ed7 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,31 @@ +click +git+https://github.com/feder-cr/lib_resume_builder_AIHawk.git +httpx~=0.27.2 +inputimeout==1.0.4 +jsonschema==4.23.0 +jsonschema-specifications==2023.12.1 +langchain==0.2.11 +langchain-anthropic +langchain-huggingface +langchain-community==0.2.10 +langchain-core==0.2.36 +langchain-google-genai==1.0.10 +langchain-ollama==0.1.3 +langchain-openai==0.1.17 +langchain-text-splitters==0.2.2 +langsmith==0.1.93 +Levenshtein==0.25.1 +loguru==0.7.2 +openai==1.37.1 +pdfminer.six==20221105 +pytest>=8.3.3 +python-dotenv~=1.0.1 +PyYAML~=6.0.2 +regex==2024.7.24 +reportlab==4.2.2 +selenium==4.9.1 +webdriver-manager==4.0.2 +pytest +pytest-mock +pytest-cov +undetected-chromedriver==3.5.5 \ No newline at end of file diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/job.py b/src/job.py new file mode 100644 index 00000000..7a48e8df --- /dev/null +++ b/src/job.py @@ -0,0 +1,35 @@ +from dataclasses import dataclass +from src.logging import logger + +@dataclass +class Job: + role: str = "" + company: str = "" + location: str = "" + link: str = "" + apply_method: str = "" + description: str = "" + summarize_job_description: str = "" + recruiter_link: str = "" + resume_path: str = "" + cover_letter_path: str = "" + + def formatted_job_information(self): + """ + Formats the job information as a markdown string. + """ + logger.debug(f"Formatting job information for job: {self.role} at {self.company}") + job_information = f""" + # Job Description + ## Job Information + - Position: {self.role} + - At: {self.company} + - Location: {self.location} + - Recruiter Profile: {self.recruiter_link or 'Not available'} + + ## Description + {self.description or 'No description provided.'} + """ + formatted_information = job_information.strip() + logger.debug(f"Formatted job information: {formatted_information}") + return formatted_information \ No newline at end of file diff --git a/src/jobContext.py b/src/jobContext.py new file mode 100644 index 00000000..64594792 --- /dev/null +++ b/src/jobContext.py @@ -0,0 +1,10 @@ +from src.job import Job +from src.job_application import JobApplication + + +from dataclasses import dataclass + +@dataclass +class JobContext: + job: Job = None + job_application: JobApplication = None \ No newline at end of file diff --git a/src/job_application_saver.py b/src/job_application_saver.py new file mode 100644 index 00000000..a8554d2a --- /dev/null +++ b/src/job_application_saver.py @@ -0,0 +1,92 @@ +from src.logging import logger +import os +import json +import shutil + +from dataclasses import asdict + +from config import JOB_APPLICATIONS_DIR +from job import Job +from job_application import JobApplication + +# Base directory where all applications will be saved +BASE_DIR = JOB_APPLICATIONS_DIR + + +class ApplicationSaver: + + def __init__(self, job_application: JobApplication): + self.job_application = job_application + self.job_application_files_path = None + + # Function to create a directory for each job application + def create_application_directory(self): + job = self.job_application.job + + # Create a unique directory name using the application ID and company name + dir_name = f"{job.id} - {job.company} {job.title}" + dir_path = os.path.join(BASE_DIR, dir_name) + + # Create the directory if it doesn't exist + os.makedirs(dir_path, exist_ok=True) + self.job_application_files_path = dir_path + return dir_path + + # Function to save the job application details as a JSON file + def save_application_details(self): + + if self.job_application_files_path is None: + raise ValueError( + "Job application file path is not set. Please create the application directory first." + ) + + json_file_path = os.path.join( + self.job_application_files_path, "job_application.json" + ) + with open(json_file_path, "w") as json_file: + json.dump(self.job_application.application, json_file, indent=4) + + # Function to save files like Resume and CV + def save_file(self, dir_path, file_path, new_filename): + if dir_path is None: + raise ValueError("dir path cannot be None") + + # Copy the file to the application directory with a new name + destination = os.path.join(dir_path, new_filename) + shutil.copy(file_path, destination) + + # Function to save job description as a text file + def save_job_description(self): + if self.job_application_files_path is None: + raise ValueError( + "Job application file path is not set. Please create the application directory first." + ) + + job: Job = self.job_application.job + + json_file_path = os.path.join( + self.job_application_files_path, "job_description.json" + ) + with open(json_file_path, "w") as json_file: + json.dump(asdict(job), json_file, indent=4) + + @staticmethod + def save(job_application: JobApplication): + saver = ApplicationSaver(job_application) + saver.create_application_directory() + saver.save_application_details() + saver.save_job_description() + # todo: tempory fix, to rely on resume and cv path from job object instead of job application object + if job_application.resume_path: + saver.save_file( + saver.job_application_files_path, + job_application.job.resume_path, + "resume.pdf", + ) + logger.debug(f"Saving cover letter to path: {job_application.cover_letter_path}") + if job_application.cover_letter_path: + saver.save_file( + saver.job_application_files_path, + job_application.job.cover_letter_path, + "cover_letter.pdf" + ) diff --git a/src/libs/llm_manager.py b/src/libs/llm_manager.py new file mode 100644 index 00000000..c7db55f2 --- /dev/null +++ b/src/libs/llm_manager.py @@ -0,0 +1,709 @@ +import json +import os +import re +import textwrap +import time +from abc import ABC, abstractmethod +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Union + +import httpx +from dotenv import load_dotenv +from langchain_core.messages import BaseMessage +from langchain_core.messages.ai import AIMessage +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompt_values import StringPromptValue +from langchain_core.prompts import ChatPromptTemplate +from Levenshtein import distance + +import ai_hawk.llm.prompts as prompts +from config import JOB_SUITABILITY_SCORE +from src.utils.constants import ( + AVAILABILITY, + CERTIFICATIONS, + CLAUDE, + COMPANY, + CONTENT, + COVER_LETTER, + EDUCATION_DETAILS, + EXPERIENCE_DETAILS, + FINISH_REASON, + GEMINI, + HUGGINGFACE, + ID, + INPUT_TOKENS, + INTERESTS, + JOB_APPLICATION_PROFILE, + JOB_DESCRIPTION, + LANGUAGES, + LEGAL_AUTHORIZATION, + LLM_MODEL_TYPE, + LOGPROBS, + MODEL, + MODEL_NAME, + OLLAMA, + OPENAI, + PERPLEXITY, + OPTIONS, + OUTPUT_TOKENS, + PERSONAL_INFORMATION, + PHRASE, + PROJECTS, + PROMPTS, + QUESTION, + REPLIES, + RESPONSE_METADATA, + RESUME, + RESUME_EDUCATIONS, + RESUME_JOBS, + RESUME_PROJECTS, + RESUME_SECTION, + SALARY_EXPECTATIONS, + SELF_IDENTIFICATION, + SYSTEM_FINGERPRINT, + TEXT, + TIME, + TOKEN_USAGE, + TOTAL_COST, + TOTAL_TOKENS, + USAGE_METADATA, + WORK_PREFERENCES, +) +from src.job import Job +from src.logging import logger +import config as cfg + +load_dotenv() + + +class AIModel(ABC): + @abstractmethod + def invoke(self, prompt: str) -> str: + pass + + +class OpenAIModel(AIModel): + def __init__(self, api_key: str, llm_model: str): + from langchain_openai import ChatOpenAI + + self.model = ChatOpenAI( + model_name=llm_model, openai_api_key=api_key, temperature=0.4 + ) + + def invoke(self, prompt: str) -> BaseMessage: + logger.debug("Invoking OpenAI API") + response = self.model.invoke(prompt) + return response + + +class ClaudeModel(AIModel): + def __init__(self, api_key: str, llm_model: str): + from langchain_anthropic import ChatAnthropic + + self.model = ChatAnthropic(model=llm_model, api_key=api_key, temperature=0.4) + + def invoke(self, prompt: str) -> BaseMessage: + response = self.model.invoke(prompt) + logger.debug("Invoking Claude API") + return response + + +class OllamaModel(AIModel): + def __init__(self, llm_model: str, llm_api_url: str): + from langchain_ollama import ChatOllama + + if len(llm_api_url) > 0: + logger.debug(f"Using Ollama with API URL: {llm_api_url}") + self.model = ChatOllama(model=llm_model, base_url=llm_api_url) + else: + self.model = ChatOllama(model=llm_model) + + def invoke(self, prompt: str) -> BaseMessage: + response = self.model.invoke(prompt) + return response + +class PerplexityModel(AIModel): + def __init__(self, api_key: str, llm_model: str): + from langchain_community.chat_models import ChatPerplexity + self.model = ChatPerplexity(model=llm_model, api_key=api_key, temperature=0.4) + + def invoke(self, prompt: str) -> BaseMessage: + response = self.model.invoke(prompt) + return response + +# gemini doesn't seem to work because API doesn't rstitute answers for questions that involve answers that are too short +class GeminiModel(AIModel): + def __init__(self, api_key: str, llm_model: str): + from langchain_google_genai import ( + ChatGoogleGenerativeAI, + HarmBlockThreshold, + HarmCategory, + ) + + self.model = ChatGoogleGenerativeAI( + model=llm_model, + google_api_key=api_key, + safety_settings={ + HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_DEROGATORY: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_TOXICITY: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_VIOLENCE: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_SEXUAL: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_MEDICAL: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_DANGEROUS: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, + }, + ) + + def invoke(self, prompt: str) -> BaseMessage: + response = self.model.invoke(prompt) + return response + + +class HuggingFaceModel(AIModel): + def __init__(self, api_key: str, llm_model: str): + from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint + + self.model = HuggingFaceEndpoint( + repo_id=llm_model, huggingfacehub_api_token=api_key, temperature=0.4 + ) + self.chatmodel = ChatHuggingFace(llm=self.model) + + def invoke(self, prompt: str) -> BaseMessage: + response = self.chatmodel.invoke(prompt) + logger.debug( + f"Invoking Model from Hugging Face API. Response: {response}, Type: {type(response)}" + ) + return response + + +class AIAdapter: + def __init__(self, config: dict, api_key: str): + self.model = self._create_model(config, api_key) + + def _create_model(self, config: dict, api_key: str) -> AIModel: + llm_model_type = cfg.LLM_MODEL_TYPE + llm_model = cfg.LLM_MODEL + + llm_api_url = cfg.LLM_API_URL + + logger.debug(f"Using {llm_model_type} with {llm_model}") + + if llm_model_type == OPENAI: + return OpenAIModel(api_key, llm_model) + elif llm_model_type == CLAUDE: + return ClaudeModel(api_key, llm_model) + elif llm_model_type == OLLAMA: + return OllamaModel(llm_model, llm_api_url) + elif llm_model_type == GEMINI: + return GeminiModel(api_key, llm_model) + elif llm_model_type == HUGGINGFACE: + return HuggingFaceModel(api_key, llm_model) + elif llm_model_type == PERPLEXITY: + return PerplexityModel(api_key, llm_model) + else: + raise ValueError(f"Unsupported model type: {llm_model_type}") + + def invoke(self, prompt: str) -> str: + return self.model.invoke(prompt) + + +class LLMLogger: + def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel]): + self.llm = llm + logger.debug(f"LLMLogger successfully initialized with LLM: {llm}") + + @staticmethod + def log_request(prompts, parsed_reply: Dict[str, Dict]): + logger.debug("Starting log_request method") + logger.debug(f"Prompts received: {prompts}") + logger.debug(f"Parsed reply received: {parsed_reply}") + + try: + calls_log = os.path.join(Path("data_folder/output"), "open_ai_calls.json") + logger.debug(f"Logging path determined: {calls_log}") + except Exception as e: + logger.error(f"Error determining the log path: {str(e)}") + raise + + if isinstance(prompts, StringPromptValue): + logger.debug("Prompts are of type StringPromptValue") + prompts = prompts.text + logger.debug(f"Prompts converted to text: {prompts}") + elif isinstance(prompts, Dict): + logger.debug("Prompts are of type Dict") + try: + prompts = { + f"prompt_{i + 1}": prompt.content + for i, prompt in enumerate(prompts.messages) + } + logger.debug(f"Prompts converted to dictionary: {prompts}") + except Exception as e: + logger.error(f"Error converting prompts to dictionary: {str(e)}") + raise + else: + logger.debug("Prompts are of unknown type, attempting default conversion") + try: + prompts = { + f"prompt_{i + 1}": prompt.content + for i, prompt in enumerate(prompts.messages) + } + logger.debug( + f"Prompts converted to dictionary using default method: {prompts}" + ) + except Exception as e: + logger.error(f"Error converting prompts using default method: {str(e)}") + raise + + try: + current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + logger.debug(f"Current time obtained: {current_time}") + except Exception as e: + logger.error(f"Error obtaining current time: {str(e)}") + raise + + try: + token_usage = parsed_reply[USAGE_METADATA] + output_tokens = token_usage[OUTPUT_TOKENS] + input_tokens = token_usage[INPUT_TOKENS] + total_tokens = token_usage[TOTAL_TOKENS] + logger.debug( + f"Token usage - Input: {input_tokens}, Output: {output_tokens}, Total: {total_tokens}" + ) + except KeyError as e: + logger.error(f"KeyError in parsed_reply structure: {str(e)}") + raise + + try: + model_name = parsed_reply[RESPONSE_METADATA][MODEL_NAME] + logger.debug(f"Model name: {model_name}") + except KeyError as e: + logger.error(f"KeyError in response_metadata: {str(e)}") + raise + + try: + prompt_price_per_token = 0.00000015 + completion_price_per_token = 0.0000006 + total_cost = (input_tokens * prompt_price_per_token) + ( + output_tokens * completion_price_per_token + ) + logger.debug(f"Total cost calculated: {total_cost}") + except Exception as e: + logger.error(f"Error calculating total cost: {str(e)}") + raise + + try: + log_entry = { + MODEL: model_name, + TIME: current_time, + PROMPTS: prompts, + REPLIES: parsed_reply[CONTENT], + TOTAL_TOKENS: total_tokens, + INPUT_TOKENS: input_tokens, + OUTPUT_TOKENS: output_tokens, + TOTAL_COST: total_cost, + } + logger.debug(f"Log entry created: {log_entry}") + except KeyError as e: + logger.error( + f"Error creating log entry: missing key {str(e)} in parsed_reply" + ) + raise + + try: + with open(calls_log, "a", encoding="utf-8") as f: + json_string = json.dumps(log_entry, ensure_ascii=False, indent=4) + f.write(json_string + "\n") + logger.debug(f"Log entry written to file: {calls_log}") + except Exception as e: + logger.error(f"Error writing log entry to file: {str(e)}") + raise + + +class LoggerChatModel: + def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel]): + self.llm = llm + logger.debug(f"LoggerChatModel successfully initialized with LLM: {llm}") + + def __call__(self, messages: List[Dict[str, str]]) -> str: + logger.debug(f"Entering __call__ method with messages: {messages}") + while True: + try: + logger.debug("Attempting to call the LLM with messages") + + reply = self.llm.invoke(messages) + logger.debug(f"LLM response received: {reply}") + + parsed_reply = self.parse_llmresult(reply) + logger.debug(f"Parsed LLM reply: {parsed_reply}") + + LLMLogger.log_request(prompts=messages, parsed_reply=parsed_reply) + logger.debug("Request successfully logged") + + return reply + + except httpx.HTTPStatusError as e: + logger.error(f"HTTPStatusError encountered: {str(e)}") + if e.response.status_code == 429: + retry_after = e.response.headers.get("retry-after") + retry_after_ms = e.response.headers.get("retry-after-ms") + + if retry_after: + wait_time = int(retry_after) + logger.warning( + f"Rate limit exceeded. Waiting for {wait_time} seconds before retrying (extracted from 'retry-after' header)..." + ) + time.sleep(wait_time) + elif retry_after_ms: + wait_time = int(retry_after_ms) / 1000.0 + logger.warning( + f"Rate limit exceeded. Waiting for {wait_time} seconds before retrying (extracted from 'retry-after-ms' header)..." + ) + time.sleep(wait_time) + else: + wait_time = 30 + logger.warning( + f"'retry-after' header not found. Waiting for {wait_time} seconds before retrying (default)..." + ) + time.sleep(wait_time) + else: + logger.error( + f"HTTP error occurred with status code: {e.response.status_code}, waiting 30 seconds before retrying" + ) + time.sleep(30) + + except Exception as e: + logger.error(f"Unexpected error occurred: {str(e)}") + logger.info( + "Waiting for 30 seconds before retrying due to an unexpected error." + ) + time.sleep(30) + continue + + def parse_llmresult(self, llmresult: AIMessage) -> Dict[str, Dict]: + logger.debug(f"Parsing LLM result: {llmresult}") + + try: + if hasattr(llmresult, USAGE_METADATA): + content = llmresult.content + response_metadata = llmresult.response_metadata + id_ = llmresult.id + usage_metadata = llmresult.usage_metadata + + parsed_result = { + CONTENT: content, + RESPONSE_METADATA: { + MODEL_NAME: response_metadata.get( + MODEL_NAME, "" + ), + SYSTEM_FINGERPRINT: response_metadata.get( + SYSTEM_FINGERPRINT, "" + ), + FINISH_REASON: response_metadata.get( + FINISH_REASON, "" + ), + LOGPROBS: response_metadata.get( + LOGPROBS, None + ), + }, + ID: id_, + USAGE_METADATA: { + INPUT_TOKENS: usage_metadata.get( + INPUT_TOKENS, 0 + ), + OUTPUT_TOKENS: usage_metadata.get( + OUTPUT_TOKENS, 0 + ), + TOTAL_TOKENS: usage_metadata.get( + TOTAL_TOKENS, 0 + ), + }, + } + else: + content = llmresult.content + response_metadata = llmresult.response_metadata + id_ = llmresult.id + token_usage = response_metadata[TOKEN_USAGE] + + parsed_result = { + CONTENT: content, + RESPONSE_METADATA: { + MODEL_NAME: response_metadata.get( + MODEL, "" + ), + FINISH_REASON: response_metadata.get( + FINISH_REASON, "" + ), + }, + ID: id_, + USAGE_METADATA: { + INPUT_TOKENS: token_usage.prompt_tokens, + OUTPUT_TOKENS: token_usage.completion_tokens, + TOTAL_TOKENS: token_usage.total_tokens, + }, + } + logger.debug(f"Parsed LLM result successfully: {parsed_result}") + return parsed_result + + except KeyError as e: + logger.error(f"KeyError while parsing LLM result: missing key {str(e)}") + raise + + except Exception as e: + logger.error(f"Unexpected error while parsing LLM result: {str(e)}") + raise + + +class GPTAnswerer: + def __init__(self, config, llm_api_key): + self.ai_adapter = AIAdapter(config, llm_api_key) + self.llm_cheap = LoggerChatModel(self.ai_adapter) + + @property + def job_description(self): + return self.job.description + + @staticmethod + def find_best_match(text: str, options: list[str]) -> str: + logger.debug(f"Finding best match for text: '{text}' in options: {options}") + distances = [ + (option, distance(text.lower(), option.lower())) for option in options + ] + best_option = min(distances, key=lambda x: x[1])[0] + logger.debug(f"Best match found: {best_option}") + return best_option + + @staticmethod + def _remove_placeholders(text: str) -> str: + logger.debug(f"Removing placeholders from text: {text}") + text = text.replace("PLACEHOLDER", "") + return text.strip() + + @staticmethod + def _preprocess_template_string(template: str) -> str: + logger.debug("Preprocessing template string") + return textwrap.dedent(template) + + def set_resume(self, resume): + logger.debug(f"Setting resume: {resume}") + self.resume = resume + + def set_job(self, job: Job): + logger.debug(f"Setting job: {job}") + self.job = job + self.job.set_summarize_job_description( + self.summarize_job_description(self.job.description) + ) + + def set_job_application_profile(self, job_application_profile): + logger.debug(f"Setting job application profile: {job_application_profile}") + self.job_application_profile = job_application_profile + + def _clean_llm_output(self, output: str) -> str: + return output.replace("*", "").replace("#", "").strip() + + def summarize_job_description(self, text: str) -> str: + logger.debug(f"Summarizing job description: {text}") + prompts.summarize_prompt_template = self._preprocess_template_string( + prompts.summarize_prompt_template + ) + prompt = ChatPromptTemplate.from_template(prompts.summarize_prompt_template) + chain = prompt | self.llm_cheap | StrOutputParser() + raw_output = chain.invoke({TEXT: text}) + output = self._clean_llm_output(raw_output) + logger.debug(f"Summary generated: {output}") + return output + + def _create_chain(self, template: str): + logger.debug(f"Creating chain with template: {template}") + prompt = ChatPromptTemplate.from_template(template) + return prompt | self.llm_cheap | StrOutputParser() + + def answer_question_textual_wide_range(self, question: str) -> str: + logger.debug(f"Answering textual question: {question}") + chains = { + PERSONAL_INFORMATION: self._create_chain( + prompts.personal_information_template + ), + SELF_IDENTIFICATION: self._create_chain( + prompts.self_identification_template + ), + LEGAL_AUTHORIZATION: self._create_chain( + prompts.legal_authorization_template + ), + WORK_PREFERENCES: self._create_chain( + prompts.work_preferences_template + ), + EDUCATION_DETAILS: self._create_chain( + prompts.education_details_template + ), + EXPERIENCE_DETAILS: self._create_chain( + prompts.experience_details_template + ), + PROJECTS: self._create_chain(prompts.projects_template), + AVAILABILITY: self._create_chain(prompts.availability_template), + SALARY_EXPECTATIONS: self._create_chain( + prompts.salary_expectations_template + ), + CERTIFICATIONS: self._create_chain( + prompts.certifications_template + ), + LANGUAGES: self._create_chain(prompts.languages_template), + INTERESTS: self._create_chain(prompts.interests_template), + COVER_LETTER: self._create_chain(prompts.coverletter_template), + } + + prompt = ChatPromptTemplate.from_template(prompts.determine_section_template) + chain = prompt | self.llm_cheap | StrOutputParser() + raw_output = chain.invoke({QUESTION: question}) + output = self._clean_llm_output(raw_output) + + match = re.search( + r"(Personal information|Self Identification|Legal Authorization|Work Preferences|Education " + r"Details|Experience Details|Projects|Availability|Salary " + r"Expectations|Certifications|Languages|Interests|Cover letter)", + output, + re.IGNORECASE, + ) + if not match: + raise ValueError("Could not extract section name from the response.") + + section_name = match.group(1).lower().replace(" ", "_") + + if section_name == "cover_letter": + chain = chains.get(section_name) + raw_output = chain.invoke( + { + RESUME: self.resume, + JOB_DESCRIPTION: self.job_description, + COMPANY: self.job.company, + } + ) + output = self._clean_llm_output(raw_output) + logger.debug(f"Cover letter generated: {output}") + return output + resume_section = getattr(self.resume, section_name, None) or getattr( + self.job_application_profile, section_name, None + ) + if resume_section is None: + logger.error( + f"Section '{section_name}' not found in either resume or job_application_profile." + ) + raise ValueError( + f"Section '{section_name}' not found in either resume or job_application_profile." + ) + chain = chains.get(section_name) + if chain is None: + logger.error(f"Chain not defined for section '{section_name}'") + raise ValueError(f"Chain not defined for section '{section_name}'") + raw_output = chain.invoke( + {RESUME_SECTION: resume_section, QUESTION: question} + ) + output = self._clean_llm_output(raw_output) + logger.debug(f"Question answered: {output}") + return output + + def answer_question_numeric( + self, question: str, default_experience: str = 3 + ) -> str: + logger.debug(f"Answering numeric question: {question}") + func_template = self._preprocess_template_string( + prompts.numeric_question_template + ) + prompt = ChatPromptTemplate.from_template(func_template) + chain = prompt | self.llm_cheap | StrOutputParser() + raw_output_str = chain.invoke( + { + RESUME_EDUCATIONS: self.resume.education_details, + RESUME_JOBS: self.resume.experience_details, + RESUME_PROJECTS: self.resume.projects, + QUESTION: question, + } + ) + output_str = self._clean_llm_output(raw_output_str) + logger.debug(f"Raw output for numeric question: {output_str}") + try: + output = self.extract_number_from_string(output_str) + logger.debug(f"Extracted number: {output}") + except ValueError: + logger.warning( + f"Failed to extract number, using default experience: {default_experience}" + ) + output = default_experience + return output + + def extract_number_from_string(self, output_str): + logger.debug(f"Extracting number from string: {output_str}") + numbers = re.findall(r"\d+", output_str) + if numbers: + logger.debug(f"Numbers found: {numbers}") + return str(numbers[0]) + else: + logger.error("No numbers found in the string") + raise ValueError("No numbers found in the string") + + def answer_question_from_options(self, question: str, options: list[str]) -> str: + logger.debug(f"Answering question from options: {question}") + func_template = self._preprocess_template_string(prompts.options_template) + prompt = ChatPromptTemplate.from_template(func_template) + chain = prompt | self.llm_cheap | StrOutputParser() + raw_output_str = chain.invoke( + { + RESUME: self.resume, + JOB_APPLICATION_PROFILE: self.job_application_profile, + QUESTION: question, + OPTIONS: options, + } + ) + output_str = self._clean_llm_output(raw_output_str) + logger.debug(f"Raw output for options question: {output_str}") + best_option = self.find_best_match(output_str, options) + logger.debug(f"Best option determined: {best_option}") + return best_option + + def determine_resume_or_cover(self, phrase: str) -> str: + logger.debug( + f"Determining if phrase refers to resume or cover letter: {phrase}" + ) + prompt = ChatPromptTemplate.from_template( + prompts.resume_or_cover_letter_template + ) + chain = prompt | self.llm_cheap | StrOutputParser() + raw_response = chain.invoke({PHRASE: phrase}) + response = self._clean_llm_output(raw_response) + logger.debug(f"Response for resume_or_cover: {response}") + if "resume" in response: + return "resume" + elif "cover" in response: + return "cover" + else: + return "resume" + + def is_job_suitable(self): + logger.info("Checking if job is suitable") + prompt = ChatPromptTemplate.from_template(prompts.is_relavant_position_template) + chain = prompt | self.llm_cheap | StrOutputParser() + raw_output = chain.invoke( + { + RESUME: self.resume, + JOB_DESCRIPTION: self.job_description, + } + ) + output = self._clean_llm_output(raw_output) + logger.debug(f"Job suitability output: {output}") + + try: + score = re.search(r"Score:\s*(\d+)", output, re.IGNORECASE).group(1) + reasoning = re.search(r"Reasoning:\s*(.+)", output, re.IGNORECASE | re.DOTALL).group(1) + except AttributeError: + logger.warning("Failed to extract score or reasoning from LLM. Proceeding with application, but job may or may not be suitable.") + return True + + logger.info(f"Job suitability score: {score}") + if int(score) < JOB_SUITABILITY_SCORE: + logger.debug(f"Job is not suitable: {reasoning}") + return int(score) >= JOB_SUITABILITY_SCORE diff --git a/src/libs/resume_and_cover_builder/__init__.py b/src/libs/resume_and_cover_builder/__init__.py new file mode 100644 index 00000000..398492d1 --- /dev/null +++ b/src/libs/resume_and_cover_builder/__init__.py @@ -0,0 +1,6 @@ +__version__ = '0.1' + +# Import all the necessary classes and functions, called when the package is imported +from .resume_generator import ResumeGenerator +from .style_manager import StyleManager +from .resume_facade import ResumeFacade \ No newline at end of file diff --git a/src/libs/resume_and_cover_builder/config.py b/src/libs/resume_and_cover_builder/config.py new file mode 100644 index 00000000..0f9f162e --- /dev/null +++ b/src/libs/resume_and_cover_builder/config.py @@ -0,0 +1,34 @@ +""" +This module is used to store the global configuration of the application. +""" +# app/libs/resume_and_cover_builder/config.py +from pathlib import Path + +class GlobalConfig: + def __init__(self): + self.STRINGS_MODULE_RESUME_PATH: Path = None + self.STRINGS_MODULE_RESUME_JOB_DESCRIPTION_PATH: Path = None + self.STRINGS_MODULE_COVER_LETTER_JOB_DESCRIPTION_PATH: Path = None + self.STRINGS_MODULE_NAME: str = None + self.STYLES_DIRECTORY: Path = None + self.LOG_OUTPUT_FILE_PATH: Path = None + self.API_KEY: str = None + self.html_template = """ + + + + + + Resume + + + + + + + $body + + + """ + +global_config = GlobalConfig() diff --git a/src/libs/resume_and_cover_builder/cover_letter_prompt/__init__.py b/src/libs/resume_and_cover_builder/cover_letter_prompt/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/libs/resume_and_cover_builder/cover_letter_prompt/strings_feder-cr.py b/src/libs/resume_and_cover_builder/cover_letter_prompt/strings_feder-cr.py new file mode 100644 index 00000000..9568da2d --- /dev/null +++ b/src/libs/resume_and_cover_builder/cover_letter_prompt/strings_feder-cr.py @@ -0,0 +1,52 @@ +from src.libs.resume_and_cover_builder.template_base import prompt_cover_letter_template + + +cover_letter_template = """ +Compose a brief and impactful cover letter based on the provided job description and resume. The letter should be no longer than three paragraphs and should be written in a professional, yet conversational tone. Avoid using any placeholders, and ensure that the letter flows naturally and is tailored to the job. + +Analyze the job description to identify key qualifications and requirements. Introduce the candidate succinctly, aligning their career objectives with the role. Highlight relevant skills and experiences from the resume that directly match the job’s demands, using specific examples to illustrate these qualifications. Reference notable aspects of the company, such as its mission or values, that resonate with the candidate’s professional goals. Conclude with a strong statement of why the candidate is a good fit for the position, expressing a desire to discuss further. + +Please write the cover letter in a way that directly addresses the job role and the company’s characteristics, ensuring it remains concise and engaging without unnecessary embellishments. The letter should be formatted into paragraphs and should not include a greeting or signature. + +## Rules: +- Do not include any introductions, explanations, or additional information. + +## Details : +- **Job Description:** +``` +{job_description} +``` +- **My resume:** +``` +{resume} +``` +"""+ prompt_cover_letter_template + + +summarize_prompt_template = """ +As a seasoned HR expert, your task is to identify and outline the key skills and requirements necessary for the position of this job. Use the provided job description as input to extract all relevant information. This will involve conducting a thorough analysis of the job's responsibilities and the industry standards. You should consider both the technical and soft skills needed to excel in this role. Additionally, specify any educational qualifications, certifications, or experiences that are essential. Your analysis should also reflect on the evolving nature of this role, considering future trends and how they might affect the required competencies. + +Rules: +Remove boilerplate text +Include only relevant information to match the job description against the resume + +# Analysis Requirements +Your analysis should include the following sections: +Technical Skills: List all the specific technical skills required for the role based on the responsibilities described in the job description. +Soft Skills: Identify the necessary soft skills, such as communication abilities, problem-solving, time management, etc. +Educational Qualifications and Certifications: Specify the essential educational qualifications and certifications for the role. +Professional Experience: Describe the relevant work experiences that are required or preferred. +Role Evolution: Analyze how the role might evolve in the future, considering industry trends and how these might influence the required skills. + +# Final Result: +Your analysis should be structured in a clear and organized document with distinct sections for each of the points listed above. Each section should contain: +This comprehensive overview will serve as a guideline for the recruitment process, ensuring the identification of the most qualified candidates. + +# Job Description: +``` +{text} +``` + +--- + +# Job Description Summary""" \ No newline at end of file diff --git a/src/libs/resume_and_cover_builder/llm/llm_generate_cover_letter_from_job.py b/src/libs/resume_and_cover_builder/llm/llm_generate_cover_letter_from_job.py new file mode 100644 index 00000000..071c45bb --- /dev/null +++ b/src/libs/resume_and_cover_builder/llm/llm_generate_cover_letter_from_job.py @@ -0,0 +1,91 @@ +""" +This creates the cover letter (in html, utils will then convert in PDF) matching with job description and plain-text resume +""" +# app/libs/resume_and_cover_builder/llm_generate_cover_letter_from_job.py +import os +import textwrap +from ..utils import LoggerChatModel +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate +from langchain_openai import ChatOpenAI, OpenAIEmbeddings +from pathlib import Path +from dotenv import load_dotenv +from requests.exceptions import HTTPError as HTTPStatusError +from pathlib import Path +from loguru import logger + +# Load environment variables from .env file +load_dotenv() + +# Configure log file +log_folder = 'log/cover_letter/gpt_cover_letter_job_descr' +if not os.path.exists(log_folder): + os.makedirs(log_folder) +log_path = Path(log_folder).resolve() +logger.add(log_path / "gpt_cover_letter_job_descr.log", rotation="1 day", compression="zip", retention="7 days", level="DEBUG") + +class LLMCoverLetterJobDescription: + def __init__(self, openai_api_key, strings): + self.llm_cheap = LoggerChatModel(ChatOpenAI(model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.4)) + self.llm_embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) + self.strings = strings + + @staticmethod + def _preprocess_template_string(template: str) -> str: + """ + Preprocess the template string by removing leading whitespace and indentation. + Args: + template (str): The template string to preprocess. + Returns: + str: The preprocessed template string. + """ + return textwrap.dedent(template) + + def set_resume(self, resume) -> None: + """ + Set the resume text to be used for generating the cover letter. + Args: + resume (str): The plain text resume to be used. + """ + self.resume = resume + + def set_job_description_from_text(self, job_description_text) -> None: + """ + Set the job description text to be used for generating the cover letter. + Args: + job_description_text (str): The plain text job description to be used. + """ + logger.debug("Starting job description summarization...") + prompt = ChatPromptTemplate.from_template(self.strings.summarize_prompt_template) + chain = prompt | self.llm_cheap | StrOutputParser() + output = chain.invoke({"text": job_description_text}) + self.job_description = output + logger.debug(f"Job description summarization complete: {self.job_description}") + + def generate_cover_letter(self) -> str: + """ + Generate the cover letter based on the job description and resume. + Returns: + str: The generated cover letter + """ + logger.debug("Starting cover letter generation...") + prompt_template = self._preprocess_template_string(self.strings.cover_letter_template) + logger.debug(f"Cover letter template after preprocessing: {prompt_template}") + + prompt = ChatPromptTemplate.from_template(prompt_template) + logger.debug(f"Prompt created: {prompt}") + + chain = prompt | self.llm_cheap | StrOutputParser() + logger.debug(f"Chain created: {chain}") + + input_data = { + "job_description": self.job_description, + "resume": self.resume + } + logger.debug(f"Input data: {input_data}") + + output = chain.invoke(input_data) + logger.debug(f"Cover letter generation result: {output}") + + logger.debug("Cover letter generation completed") + return output diff --git a/src/libs/resume_and_cover_builder/llm/llm_generate_resume.py b/src/libs/resume_and_cover_builder/llm/llm_generate_resume.py new file mode 100644 index 00000000..6b0e4412 --- /dev/null +++ b/src/libs/resume_and_cover_builder/llm/llm_generate_resume.py @@ -0,0 +1,322 @@ +""" +Create a class that generates a resume based on a resume and a resume template. +""" +# app/libs/resume_and_cover_builder/gpt_resume.py +import os +import textwrap +from src.libs.resume_and_cover_builder.utils import LoggerChatModel +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate +from langchain_openai import ChatOpenAI +from dotenv import load_dotenv +from concurrent.futures import ThreadPoolExecutor, as_completed +from loguru import logger +from pathlib import Path + +# Load environment variables from .env file +load_dotenv() + +# Configure log file +log_folder = 'log/resume/gpt_resume' +if not os.path.exists(log_folder): + os.makedirs(log_folder) +log_path = Path(log_folder).resolve() +logger.add(log_path / "gpt_resume.log", rotation="1 day", compression="zip", retention="7 days", level="DEBUG") + +class LLMResumer: + def __init__(self, openai_api_key, strings): + self.llm_cheap = LoggerChatModel( + ChatOpenAI( + model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.4 + ) + ) + self.strings = strings + + @staticmethod + def _preprocess_template_string(template: str) -> str: + """ + Preprocess the template string by removing leading whitespace and indentation. + Args: + template (str): The template string to preprocess. + Returns: + str: The preprocessed template string. + """ + return textwrap.dedent(template) + + def set_resume(self, resume) -> None: + """ + Set the resume object to be used for generating the resume. + Args: + resume (Resume): The resume object to be used. + """ + self.resume = resume + + def generate_header(self, data = None) -> str: + """ + Generate the header section of the resume. + Args: + data (dict): The personal information to use for generating the header. + Returns: + str: The generated header section. + """ + header_prompt_template = self._preprocess_template_string( + self.strings.prompt_header + ) + prompt = ChatPromptTemplate.from_template(header_prompt_template) + chain = prompt | self.llm_cheap | StrOutputParser() + input_data = { + "personal_information": self.resume.personal_information + } if data is None else data + output = chain.invoke(input_data) + return output + + def generate_education_section(self, data = None) -> str: + """ + Generate the education section of the resume. + Args: + data (dict): The education details to use for generating the education section. + Returns: + str: The generated education section. + """ + logger.debug("Starting education section generation") + + education_prompt_template = self._preprocess_template_string(self.strings.prompt_education) + logger.debug(f"Education template: {education_prompt_template}") + + prompt = ChatPromptTemplate.from_template(education_prompt_template) + logger.debug(f"Prompt: {prompt}") + + chain = prompt | self.llm_cheap | StrOutputParser() + logger.debug(f"Chain created: {chain}") + + input_data = { + "education_details": self.resume.education_details + } if data is None else data + output = chain.invoke(input_data) + logger.debug(f"Chain invocation result: {output}") + + logger.debug("Education section generation completed") + return output + + def generate_work_experience_section(self, data = None) -> str: + """ + Generate the work experience section of the resume. + Args: + data (dict): The work experience details to use for generating the work experience section. + Returns: + str: The generated work experience section. + """ + logger.debug("Starting work experience section generation") + + work_experience_prompt_template = self._preprocess_template_string(self.strings.prompt_working_experience) + logger.debug(f"Work experience template: {work_experience_prompt_template}") + + prompt = ChatPromptTemplate.from_template(work_experience_prompt_template) + logger.debug(f"Prompt: {prompt}") + + chain = prompt | self.llm_cheap | StrOutputParser() + logger.debug(f"Chain created: {chain}") + + input_data = { + "experience_details": self.resume.experience_details + } if data is None else data + output = chain.invoke(input_data) + logger.debug(f"Chain invocation result: {output}") + + logger.debug("Work experience section generation completed") + return output + + def generate_projects_section(self, data = None) -> str: + """ + Generate the side projects section of the resume. + Args: + data (dict): The side projects to use for generating the side projects section. + Returns: + str: The generated side projects section. + """ + logger.debug("Starting side projects section generation") + + projects_prompt_template = self._preprocess_template_string(self.strings.prompt_projects) + logger.debug(f"Side projects template: {projects_prompt_template}") + + prompt = ChatPromptTemplate.from_template(projects_prompt_template) + logger.debug(f"Prompt: {prompt}") + + chain = prompt | self.llm_cheap | StrOutputParser() + logger.debug(f"Chain created: {chain}") + + input_data = { + "projects": self.resume.projects + } if data is None else data + output = chain.invoke(input_data) + logger.debug(f"Chain invocation result: {output}") + + logger.debug("Side projects section generation completed") + return output + + def generate_achievements_section(self, data = None) -> str: + """ + Generate the achievements section of the resume. + Args: + data (dict): The achievements to use for generating the achievements section. + Returns: + str: The generated achievements section. + """ + logger.debug("Starting achievements section generation") + + achievements_prompt_template = self._preprocess_template_string(self.strings.prompt_achievements) + logger.debug(f"Achievements template: {achievements_prompt_template}") + + prompt = ChatPromptTemplate.from_template(achievements_prompt_template) + logger.debug(f"Prompt: {prompt}") + + chain = prompt | self.llm_cheap | StrOutputParser() + logger.debug(f"Chain created: {chain}") + + input_data = { + "achievements": self.resume.achievements, + "certifications": self.resume.certifications, + } if data is None else data + logger.debug(f"Input data for the chain: {input_data}") + + output = chain.invoke(input_data) + logger.debug(f"Chain invocation result: {output}") + + logger.debug("Achievements section generation completed") + return output + + def generate_certifications_section(self, data = None) -> str: + """ + Generate the certifications section of the resume. + Returns: + str: The generated certifications section. + """ + logger.debug("Starting Certifications section generation") + + certifications_prompt_template = self._preprocess_template_string(self.strings.prompt_certifications) + logger.debug(f"Certifications template: {certifications_prompt_template}") + + prompt = ChatPromptTemplate.from_template(certifications_prompt_template) + logger.debug(f"Prompt: {prompt}") + + chain = prompt | self.llm_cheap | StrOutputParser() + logger.debug(f"Chain created: {chain}") + + input_data = { + "certifications": self.resume.certifications + } if data is None else data + logger.debug(f"Input data for the chain: {input_data}") + + output = chain.invoke(input_data) + logger.debug(f"Chain invocation result: {output}") + + logger.debug("Certifications section generation completed") + return output + + def generate_additional_skills_section(self, data = None) -> str: + """ + Generate the additional skills section of the resume. + Returns: + str: The generated additional skills section. + """ + additional_skills_prompt_template = self._preprocess_template_string(self.strings.prompt_additional_skills) + + skills = set() + if self.resume.experience_details: + for exp in self.resume.experience_details: + if exp.skills_acquired: + skills.update(exp.skills_acquired) + + if self.resume.education_details: + for edu in self.resume.education_details: + if edu.exam: + for exam in edu.exam: + skills.update(exam.keys()) + prompt = ChatPromptTemplate.from_template(additional_skills_prompt_template) + chain = prompt | self.llm_cheap | StrOutputParser() + input_data = { + "languages": self.resume.languages, + "interests": self.resume.interests, + "skills": skills, + } if data is None else data + output = chain.invoke(input_data) + + return output + + def generate_html_resume(self) -> str: + """ + Generate the full HTML resume based on the resume object. + Returns: + str: The generated HTML resume. + """ + def header_fn(): + if self.resume.personal_information: + return self.generate_header() + return "" + + def education_fn(): + if self.resume.education_details: + return self.generate_education_section() + return "" + + def work_experience_fn(): + if self.resume.experience_details: + return self.generate_work_experience_section() + return "" + + def projects_fn(): + if self.resume.projects: + return self.generate_projects_section() + return "" + + def achievements_fn(): + if self.resume.achievements: + return self.generate_achievements_section() + return "" + + def certifications_fn(): + if self.resume.certifications: + return self.generate_certifications_section() + return "" + + def additional_skills_fn(): + if (self.resume.experience_details or self.resume.education_details or + self.resume.languages or self.resume.interests): + return self.generate_additional_skills_section() + return "" + + # Create a dictionary to map the function names to their respective callables + functions = { + "header": header_fn, + "education": education_fn, + "work_experience": work_experience_fn, + "projects": projects_fn, + "achievements": achievements_fn, + "certifications": certifications_fn, + "additional_skills": additional_skills_fn, + } + + # Use ThreadPoolExecutor to run the functions in parallel + with ThreadPoolExecutor() as executor: + future_to_section = {executor.submit(fn): section for section, fn in functions.items()} + results = {} + for future in as_completed(future_to_section): + section = future_to_section[future] + try: + result = future.result() + if result: + results[section] = result + except Exception as exc: + logger.error(f'{section} raised an exception: {exc}') + full_resume = "\n" + full_resume += f" {results.get('header', '')}\n" + full_resume += "
\n" + full_resume += f" {results.get('education', '')}\n" + full_resume += f" {results.get('work_experience', '')}\n" + full_resume += f" {results.get('projects', '')}\n" + full_resume += f" {results.get('achievements', '')}\n" + full_resume += f" {results.get('certifications', '')}\n" + full_resume += f" {results.get('additional_skills', '')}\n" + full_resume += "
\n" + full_resume += "" + return full_resume diff --git a/src/libs/resume_and_cover_builder/llm/llm_generate_resume_from_job.py b/src/libs/resume_and_cover_builder/llm/llm_generate_resume_from_job.py new file mode 100644 index 00000000..1cff9206 --- /dev/null +++ b/src/libs/resume_and_cover_builder/llm/llm_generate_resume_from_job.py @@ -0,0 +1,134 @@ +""" +Create a class that generates a job description based on a resume and a job description template. +""" +# app/libs/resume_and_cover_builder/llm_generate_resume_from_job.py +import os +from src.libs.resume_and_cover_builder.llm.llm_generate_resume import LLMResumer +from src.libs.resume_and_cover_builder.utils import LoggerChatModel +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate +from langchain_openai import ChatOpenAI +from dotenv import load_dotenv +from loguru import logger +from pathlib import Path + +# Load environment variables from .env file +load_dotenv() + +log_folder = 'log/resume/gpt_resum_job_descr' +if not os.path.exists(log_folder): + os.makedirs(log_folder) +log_path = Path(log_folder).resolve() +logger.add(log_path / "gpt_resum_job_descr.log", rotation="1 day", compression="zip", retention="7 days", level="DEBUG") + +class LLMResumeJobDescription(LLMResumer): + def __init__(self, openai_api_key, strings): + super().__init__(openai_api_key, strings) + + def set_job_description_from_text(self, job_description_text) -> None: + """ + Set the job description text to be used for generating the resume. + Args: + job_description_text (str): The plain text job description to be used. + """ + prompt = ChatPromptTemplate.from_template(self.strings.summarize_prompt_template) + chain = prompt | self.llm_cheap | StrOutputParser() + output = chain.invoke({"text": job_description_text}) + self.job_description = output + + def generate_header(self) -> str: + """ + Generate the header section of the resume. + Returns: + str: The generated header section. + """ + return super().generate_header(data={ + "personal_information": self.resume.personal_information, + "job_description": self.job_description + }) + + def generate_education_section(self) -> str: + """ + Generate the education section of the resume. + Returns: + str: The generated education section. + """ + return super().generate_education_section(data={ + "education_details": self.resume.education_details, + "job_description": self.job_description + }) + + def generate_work_experience_section(self) -> str: + """ + Generate the work experience section of the resume. + Returns: + str: The generated work experience section. + """ + return super().generate_work_experience_section(data={ + "experience_details": self.resume.experience_details, + "job_description": self.job_description + }) + + def generate_projects_section(self) -> str: + """ + Generate the side projects section of the resume. + Returns: + str: The generated side projects section. + """ + return super().generate_projects_section(data={ + "projects": self.resume.projects, + "job_description": self.job_description + }) + + def generate_achievements_section(self) -> str: + """ + Generate the achievements section of the resume. + Returns: + str: The generated achievements section. + """ + return super().generate_achievements_section(data={ + "achievements": self.resume.achievements, + "job_description": self.job_description + }) + + + def generate_certifications_section(self) -> str: + """ + Generate the certifications section of the resume. + Returns: + str: The generated certifications section. + """ + return super().generate_certifications_section(data={ + "certifications": self.resume.certifications, + "job_description": self.job_description + }) + + def generate_additional_skills_section(self) -> str: + """ + Generate the additional skills section of the resume. + Returns: + str: The generated additional skills section. + """ + additional_skills_prompt_template = self._preprocess_template_string( + self.strings.prompt_additional_skills + ) + skills = set() + if self.resume.experience_details: + for exp in self.resume.experience_details: + if exp.skills_acquired: + skills.update(exp.skills_acquired) + + if self.resume.education_details: + for edu in self.resume.education_details: + if edu.exam: + for exam in edu.exam: + skills.update(exam.keys()) + prompt = ChatPromptTemplate.from_template(additional_skills_prompt_template) + chain = prompt | self.llm_cheap | StrOutputParser() + output = chain.invoke({ + "languages": self.resume.languages, + "interests": self.resume.interests, + "skills": skills, + "job_description": self.job_description + }) + return output diff --git a/src/libs/resume_and_cover_builder/llm/llm_job_parser.py b/src/libs/resume_and_cover_builder/llm/llm_job_parser.py new file mode 100644 index 00000000..08302ff5 --- /dev/null +++ b/src/libs/resume_and_cover_builder/llm/llm_job_parser.py @@ -0,0 +1,208 @@ +import os +import tempfile +import textwrap +import time +import re # For email validation +from src.libs.resume_and_cover_builder.utils import LoggerChatModel +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate, PromptTemplate +from langchain_openai import ChatOpenAI +from dotenv import load_dotenv +from concurrent.futures import ThreadPoolExecutor, as_completed +from loguru import logger +from pathlib import Path +from langchain_core.prompt_values import StringPromptValue +from langchain_core.runnables import RunnablePassthrough +from langchain_text_splitters import TokenTextSplitter +from langchain_community.embeddings import OpenAIEmbeddings +from langchain_community.vectorstores import FAISS +from lib_resume_builder_AIHawk.config import global_config +from langchain_community.document_loaders import TextLoader +from requests.exceptions import HTTPError as HTTPStatusError # HTTP error handling +import openai + +# Load environment variables from the .env file +load_dotenv() + +# Configure the log file +log_folder = 'log/resume/gpt_resume' +if not os.path.exists(log_folder): + os.makedirs(log_folder) +log_path = Path(log_folder).resolve() +logger.add(log_path / "gpt_resume.log", rotation="1 day", compression="zip", retention="7 days", level="DEBUG") + + +class LLMParser: + def __init__(self, openai_api_key): + self.llm = LoggerChatModel( + ChatOpenAI( + model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.4 + ) + ) + self.llm_embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) # Initialize embeddings + self.vectorstore = None # Will be initialized after document loading + + @staticmethod + def _preprocess_template_string(template: str) -> str: + """ + Preprocess the template string by removing leading whitespaces and indentation. + Args: + template (str): The template string to preprocess. + Returns: + str: The preprocessed template string. + """ + return textwrap.dedent(template) + + def set_body_html(self, body_html): + """ + Retrieves the job description from HTML, processes it, and initializes the vectorstore. + Args: + body_html (str): The HTML content to process. + """ + + # Save the HTML content to a temporary file + with tempfile.NamedTemporaryFile(delete=False, suffix=".html", mode="w", encoding="utf-8") as temp_file: + temp_file.write(body_html) + temp_file_path = temp_file.name + try: + loader = TextLoader(temp_file_path, encoding="utf-8", autodetect_encoding=True) + document = loader.load() + logger.debug("Document successfully loaded.") + except Exception as e: + logger.error(f"Error during document loading: {e}") + raise + finally: + os.remove(temp_file_path) + logger.debug(f"Temporary file removed: {temp_file_path}") + + # Split the text into chunks + text_splitter = TokenTextSplitter(chunk_size=500, chunk_overlap=50) + all_splits = text_splitter.split_documents(document) + logger.debug(f"Text split into {len(all_splits)} fragments.") + + # Create the vectorstore using FAISS + try: + self.vectorstore = FAISS.from_documents(documents=all_splits, embedding=self.llm_embeddings) + logger.debug("Vectorstore successfully initialized.") + except Exception as e: + logger.error(f"Error during vectorstore creation: {e}") + raise + + def _retrieve_context(self, query: str, top_k: int = 3) -> str: + """ + Retrieves the most relevant text fragments using the retriever. + Args: + query (str): The search query. + top_k (int): Number of fragments to retrieve. + Returns: + str: Concatenated text fragments. + """ + if not self.vectorstore: + raise ValueError("Vectorstore not initialized. Run extract_job_description first.") + + retriever = self.vectorstore.as_retriever() + retrieved_docs = retriever.get_relevant_documents(query)[:top_k] + context = "\n\n".join(doc.page_content for doc in retrieved_docs) + logger.debug(f"Context retrieved for query '{query}': {context[:200]}...") # Log the first 200 characters + return context + + def _extract_information(self, question: str, retrieval_query: str) -> str: + """ + Generic method to extract specific information using the retriever and LLM. + Args: + question (str): The question to ask the LLM for extraction. + retrieval_query (str): The query to use for retrieving relevant context. + Returns: + str: The extracted information. + """ + context = self._retrieve_context(retrieval_query) + + prompt = ChatPromptTemplate.from_template( + template=""" + You are an expert in extracting specific information from job descriptions. + Carefully read the job description context below and provide a clear and concise answer to the question. + + Context: {context} + + Question: {question} + Answer: + """ + ) + + formatted_prompt = prompt.format(context=context, question=question) + logger.debug(f"Formatted prompt for extraction: {formatted_prompt[:200]}...") # Log the first 200 characters + + try: + chain = prompt | self.llm | StrOutputParser() + result = chain.invoke({"context": context, "question": question}) + extracted_info = result.strip() + logger.debug(f"Extracted information: {extracted_info}") + return extracted_info + except Exception as e: + logger.error(f"Error during information extraction: {e}") + return "" + + def extract_job_description(self) -> str: + """ + Extracts the company name from the job description. + Returns: + str: The extracted job description. + """ + question = "What is the job description of the company?" + retrieval_query = "Job description" + logger.debug("Starting job description extraction.") + return self._extract_information(question, retrieval_query) + + def extract_company_name(self) -> str: + """ + Extracts the company name from the job description. + Returns: + str: The extracted company name. + """ + question = "What is the company's name?" + retrieval_query = "Company name" + logger.debug("Starting company name extraction.") + return self._extract_information(question, retrieval_query) + + def extract_role(self) -> str: + """ + Extracts the sought role/title from the job description. + Returns: + str: The extracted role/title. + """ + question = "What is the role or title sought in this job description?" + retrieval_query = "Job title" + logger.debug("Starting role/title extraction.") + return self._extract_information(question, retrieval_query) + + def extract_location(self) -> str: + """ + Extracts the location from the job description. + Returns: + str: The extracted location. + """ + question = "What is the location mentioned in this job description?" + retrieval_query = "Location" + logger.debug("Starting location extraction.") + return self._extract_information(question, retrieval_query) + + def extract_recruiter_email(self) -> str: + """ + Extracts the recruiter's email from the job description. + Returns: + str: The extracted recruiter's email. + """ + question = "What is the recruiter's email address in this job description?" + retrieval_query = "Recruiter email" + logger.debug("Starting recruiter email extraction.") + email = self._extract_information(question, retrieval_query) + + # Validate the extracted email using regex + email_regex = r'[\w\.-]+@[\w\.-]+\.\w+' + if re.match(email_regex, email): + logger.debug("Valid recruiter's email.") + return email + else: + logger.warning("Invalid or not found recruiter's email.") + return "" + diff --git a/src/libs/resume_and_cover_builder/module_loader.py b/src/libs/resume_and_cover_builder/module_loader.py new file mode 100644 index 00000000..a918a109 --- /dev/null +++ b/src/libs/resume_and_cover_builder/module_loader.py @@ -0,0 +1,13 @@ +""" +This module is used to store the global configuration of the application. +""" +# app/libs/resume_and_cover_builder/module_loader.py +import importlib +import sys + +def load_module(module_path: str, module_name: str): + spec = importlib.util.spec_from_file_location(module_name, module_path) + module = importlib.util.module_from_spec(spec) + sys.modules[module_name] = module + spec.loader.exec_module(module) + return module \ No newline at end of file diff --git a/src/libs/resume_and_cover_builder/resume_facade.py b/src/libs/resume_and_cover_builder/resume_facade.py new file mode 100644 index 00000000..0119814d --- /dev/null +++ b/src/libs/resume_and_cover_builder/resume_facade.py @@ -0,0 +1,165 @@ +""" +This module contains the FacadeManager class, which is responsible for managing the interaction between the user and other components of the application. +""" +# app/libs/resume_and_cover_builder/manager_facade.py +import hashlib +import inquirer +from pathlib import Path + +from loguru import logger + +from src.libs.resume_and_cover_builder.llm.llm_job_parser import LLMParser +from src.job import Job +from src.utils.chrome_utils import HTML_to_PDF +from .config import global_config + +class ResumeFacade: + def __init__(self, api_key, style_manager, resume_generator, resume_object, output_path): + """ + Initialize the FacadeManager with the given API key, style manager, resume generator, resume object, and log path. + Args: + api_key (str): The OpenAI API key to be used for generating text. + style_manager (StyleManager): The StyleManager instance to manage the styles. + resume_generator (ResumeGenerator): The ResumeGenerator instance to generate resumes and cover letters. + resume_object (str): The resume object to be used for generating resumes and cover letters. + output_path (str): The path to the log file. + """ + lib_directory = Path(__file__).resolve().parent + global_config.STRINGS_MODULE_RESUME_PATH = lib_directory / "resume_prompt/strings_feder-cr.py" + global_config.STRINGS_MODULE_RESUME_JOB_DESCRIPTION_PATH = lib_directory / "resume_job_description_prompt/strings_feder-cr.py" + global_config.STRINGS_MODULE_COVER_LETTER_JOB_DESCRIPTION_PATH = lib_directory / "cover_letter_prompt/strings_feder-cr.py" + global_config.STRINGS_MODULE_NAME = "strings_feder_cr" + global_config.STYLES_DIRECTORY = lib_directory / "resume_style" + global_config.LOG_OUTPUT_FILE_PATH = output_path + global_config.API_KEY = api_key + self.style_manager = style_manager + self.resume_generator = resume_generator + self.resume_generator.set_resume_object(resume_object) + self.selected_style = None # Property to store the selected style + + def set_driver(self, driver): + self.driver = driver + + def prompt_user(self, choices: list[str], message: str) -> str: + """ + Prompt the user with the given message and choices. + Args: + choices (list[str]): The list of choices to present to the user. + message (str): The message to display to the user. + Returns: + str: The choice selected by the user. + """ + questions = [ + inquirer.List('selection', message=message, choices=choices), + ] + return inquirer.prompt(questions)['selection'] + + def prompt_for_text(self, message: str) -> str: + """ + Prompt the user to enter text with the given message. + Args: + message (str): The message to display to the user. + Returns: + str: The text entered by the user. + """ + questions = [ + inquirer.Text('text', message=message), + ] + return inquirer.prompt(questions)['text'] + + def choose_style(self) -> None: + """ + Prompt the user to choose a style for the resume. + """ + styles = self.style_manager.get_styles() + if not styles: + print("No styles available") + return None + formatted_choices = self.style_manager.format_choices(styles) + selected_choice = self.prompt_user(formatted_choices, "Which style would you like to adopt?") + self.selected_style = selected_choice.split(' (')[0] + + def link_to_job(self, job_url): + self.driver.get(job_url) + self.driver.implicitly_wait(10) + body_element = self.driver.find_element("tag name", "body") + body_element = body_element.get_attribute("outerHTML") + self.llm_job_parser = LLMParser(openai_api_key=global_config.API_KEY) + self.llm_job_parser.set_body_html(body_element) + + self.job = Job() + self.job.role = self.llm_job_parser.extract_role() + self.job.company = self.llm_job_parser.extract_company_name() + self.job.description = self.llm_job_parser.extract_job_description() + self.job.location = self.llm_job_parser.extract_location() + self.job.link = job_url + logger.info(f"Extracting job details from URL: {job_url}") + + + def create_resume_pdf_job_tailored(self) -> tuple[bytes, str]: + """ + Create a resume PDF using the selected style and the given job description text. + Args: + job_url (str): The job URL to generate the hash for. + job_description_text (str): The job description text to include in the resume. + Returns: + tuple: A tuple containing the PDF content as bytes and the unique filename. + """ + if self.selected_style is None: + raise ValueError("You must choose a style before generating the PDF.") + + style_path = self.style_manager.get_style_path(self.selected_style) + + html_resume = self.resume_generator.create_resume_job_description_text(style_path, self.job.description) + + # Generate a unique name using the job URL hash + suggested_name = hashlib.md5(self.job.link.encode()).hexdigest()[:10] + + result = HTML_to_PDF(html_resume, self.driver) + self.driver.quit() + return result, suggested_name + + + + def create_resume_pdf(self) -> tuple[bytes, str]: + """ + Create a resume PDF using the selected style and the given job description text. + Args: + job_url (str): The job URL to generate the hash for. + job_description_text (str): The job description text to include in the resume. + Returns: + tuple: A tuple containing the PDF content as bytes and the unique filename. + """ + + if self.selected_style is None: + raise ValueError("You must choose a style before generating the PDF.") + + style_path = self.style_manager.get_style_path(self.selected_style) + html_resume = self.resume_generator.create_resume(style_path) + suggested_name = hashlib.md5(self.job.link.encode()).hexdigest()[:10] + result = HTML_to_PDF(html_resume, self.driver) + self.driver.quit() + return result, suggested_name + + def create_cover_letter(self) -> tuple[bytes, str]: + """ + Create a cover letter based on the given job description text and job URL. + Args: + job_url (str): The job URL to generate the hash for. + job_description_text (str): The job description text to include in the cover letter. + Returns: + tuple: A tuple containing the PDF content as bytes and the unique filename. + """ + if self.selected_style is None: + raise ValueError("You must choose a style before generating the PDF.") + + style_path = self.style_manager.get_style_path() + cover_letter_html = self.resume_generator.create_cover_letter_job_description(style_path, self.job.description) + + # Generate a unique name using the job URL hash + suggested_name = hashlib.md5(self.job.link.encode()).hexdigest()[:10] + + + result = HTML_to_PDF(cover_letter_html, self.driver) + self.driver.quit() + return result, suggested_name \ No newline at end of file diff --git a/src/libs/resume_and_cover_builder/resume_generator.py b/src/libs/resume_and_cover_builder/resume_generator.py new file mode 100644 index 00000000..8844b287 --- /dev/null +++ b/src/libs/resume_and_cover_builder/resume_generator.py @@ -0,0 +1,47 @@ +""" +This module is responsible for generating resumes and cover letters using the LLM model. +""" +# app/libs/resume_and_cover_builder/resume_generator.py +from string import Template +from typing import Any +from src.libs.resume_and_cover_builder.llm.llm_generate_resume import LLMResumer +from src.libs.resume_and_cover_builder.llm.llm_generate_resume_from_job import LLMResumeJobDescription +from src.libs.resume_and_cover_builder.llm.llm_generate_cover_letter_from_job import LLMCoverLetterJobDescription +from .module_loader import load_module +from .config import global_config + +class ResumeGenerator: + def __init__(self): + pass + + def set_resume_object(self, resume_object): + self.resume_object = resume_object + + + def _create_resume(self, gpt_answerer: Any, style_path): + gpt_answerer.set_resume(self.resume_object) + template = Template(global_config.html_template) + return template.substitute(body=gpt_answerer.generate_html_resume(), style_path=style_path) + + def create_resume(self, style_path): + strings = load_module(global_config.STRINGS_MODULE_RESUME_PATH, global_config.STRINGS_MODULE_NAME) + gpt_answerer = LLMResumer(global_config.API_KEY, strings) + return self._create_resume(gpt_answerer, style_path) + + def create_resume_job_description_text(self, style_path: str, job_description_text: str): + strings = load_module(global_config.STRINGS_MODULE_RESUME_JOB_DESCRIPTION_PATH, global_config.STRINGS_MODULE_NAME) + gpt_answerer = LLMResumeJobDescription(global_config.API_KEY, strings) + gpt_answerer.set_job_description_from_text(job_description_text) + return self._create_resume(gpt_answerer, style_path) + + def create_cover_letter_job_description(self, style_path: str, job_description_text: str): + strings = load_module(global_config.STRINGS_MODULE_COVER_LETTER_JOB_DESCRIPTION_PATH, global_config.STRINGS_MODULE_NAME) + gpt_answerer = LLMCoverLetterJobDescription(global_config.API_KEY, strings) + gpt_answerer.set_resume(self.resume_object) + gpt_answerer.set_job_description_from_text(job_description_text) + cover_letter_html = gpt_answerer.generate_cover_letter() + template = Template(global_config.html_template) + return template.substitute(body=cover_letter_html, style_path=style_path) + + + \ No newline at end of file diff --git a/src/libs/resume_and_cover_builder/resume_job_description_prompt/__init__.py b/src/libs/resume_and_cover_builder/resume_job_description_prompt/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/libs/resume_and_cover_builder/resume_job_description_prompt/strings_feder-cr.py b/src/libs/resume_and_cover_builder/resume_job_description_prompt/strings_feder-cr.py new file mode 100644 index 00000000..28420e1a --- /dev/null +++ b/src/libs/resume_and_cover_builder/resume_job_description_prompt/strings_feder-cr.py @@ -0,0 +1,169 @@ +from libs.resume_and_cover_builder.template_base import prompt_header_template, prompt_education_template, prompt_working_experience_template, prompt_projects_template, prompt_additional_skills_template, prompt_certifications_template, prompt_achievements_template + +prompt_header = """ +Act as an HR expert and resume writer specializing in ATS-friendly resumes. Your task is to create a professional and polished header for the resume. The header should: + +1. **Contact Information**: Include your full name, city and country, phone number, email address, LinkedIn profile, and GitHub profile. +2. **Formatting**: Ensure the contact details are presented clearly and are easy to read. + +To implement this: +- If any of the contact information fields (e.g., LinkedIn profile, GitHub profile) are not provided (i.e., `None`), omit them from the header. + +- **My information:** + {personal_information} +""" + prompt_header_template + +prompt_education = """ +Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to articulate the educational background for a resume, ensuring it aligns with the provided job description. For each educational entry, ensure you include: + +1. **Institution Name and Location**: Specify the university or educational institution’s name and location. +2. **Degree and Field of Study**: Clearly indicate the degree earned and the field of study. +3. **Grade**: Include your Grade if it is strong and relevant. +4. **Relevant Coursework**: List key courses with their grades to showcase your academic strengths. If no coursework is provided, omit this section from the template. + +To implement this, follow these steps: +- If the exam details are not provided (i.e., `None`), skip the coursework section when filling out the template. +- If the exam details are available, fill out the coursework section accordingly. + + +- **My information:** + {education_details} + +- **Job Description:** + {job_description} +"""+ prompt_education_template + + +prompt_working_experience = """ +Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to detail the work experience for a resume, ensuring it aligns with the provided job description. For each job entry, ensure you include: + +1. **Company Name and Location**: Provide the name of the company and its location. +2. **Job Title**: Clearly state your job title. +3. **Dates of Employment**: Include the start and end dates of your employment. +4. **Responsibilities and Achievements**: Describe your key responsibilities and notable achievements, emphasizing measurable results and specific contributions. + +Ensure that the descriptions highlight relevant experience and align with the job description. + +To implement this: +- If any of the work experience details (e.g., responsibilities, achievements) are not provided (i.e., `None`), omit those sections when filling out the template. + + +- **My information:** + {experience_details} + +- **Job Description:** + {job_description} +"""+ prompt_working_experience_template + + +prompt_projects = """ +Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to highlight notable side projects based on the provided job description. For each project, ensure you include: + +1. **Project Name and Link**: Provide the name of the project and include a link to the GitHub repository or project page. +2. **Project Details**: Describe any notable recognition or achievements related to the project, such as GitHub stars or community feedback. +3. **Technical Contributions**: Highlight your specific contributions and the technologies used in the project. + +Ensure that the project descriptions demonstrate your skills and achievements relevant to the job description. + +To implement this: +- If any of the project details (e.g., link, achievements) are not provided (i.e., `None`), omit those sections when filling out the template. + + +- **My information:** + {projects} + +- **Job Description:** + {job_description} +"""+ prompt_projects_template + + +prompt_achievements = """ +Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to list significant achievements based on the provided job description. For each achievement, ensure you include: + +1. **Award or Recognition**: Clearly state the name of the award, recognition, scholarship, or honor. +2. **Description**: Provide a brief description of the achievement and its relevance to your career or academic journey. + +Ensure that the achievements are clearly presented and effectively highlight your accomplishments. + +To implement this: +- If any of the achievement details (e.g., certifications, descriptions) are not provided (i.e., `None`), omit those sections when filling out the template. + + +- **My information:** + {achievements} + +- **Job Description:** + {job_description} +"""+ prompt_achievements_template + + +prompt_certifications = """ +Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to list significant certifications based on the provided details. For each certification, ensure you include: + +1. **Certification Name**: Clearly state the name of the certification. +2. **Description**: Provide a brief description of the certification and its relevance to your professional or academic career. + +Ensure that the certifications are clearly presented and effectively highlight your qualifications. + +To implement this: + +If any of the certification details (e.g., descriptions) are not provided (i.e., None), omit those sections when filling out the template. + +- **My information:** + {certifications} + +- **Job Description:** + {job_description} +"""+ prompt_certifications_template + + +prompt_additional_skills = """ +Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to list additional skills relevant to the job. For each skill, ensure you include: +Do not add any information beyond what is listed in the provided data fields. Only use the information provided in the 'languages', 'interests', and 'skills' fields to formulate your responses. Avoid extrapolating or incorporating details from the job description or other external sources. + +1. **Skill Category**: Clearly state the category or type of skill. +2. **Specific Skills**: List the specific skills or technologies within each category. +3. **Proficiency and Experience**: Briefly describe your experience and proficiency level. + +Ensure that the skills listed are relevant and accurately reflect your expertise in the field. + +To implement this: +- If any of the skill details (e.g., languages, interests, skills) are not provided (i.e., `None`), omit those sections when filling out the template. + + +- **My information:** + {languages} + {interests} + {skills} + +- **Job Description:** + {job_description} +"""+ prompt_additional_skills_template + +summarize_prompt_template = """ +As a seasoned HR expert, your task is to identify and outline the key skills and requirements necessary for the position of this job. Use the provided job description as input to extract all relevant information. This will involve conducting a thorough analysis of the job's responsibilities and the industry standards. You should consider both the technical and soft skills needed to excel in this role. Additionally, specify any educational qualifications, certifications, or experiences that are essential. Your analysis should also reflect on the evolving nature of this role, considering future trends and how they might affect the required competencies. + +Rules: +Remove boilerplate text +Include only relevant information to match the job description against the resume + +# Analysis Requirements +Your analysis should include the following sections: +Technical Skills: List all the specific technical skills required for the role based on the responsibilities described in the job description. +Soft Skills: Identify the necessary soft skills, such as communication abilities, problem-solving, time management, etc. +Educational Qualifications and Certifications: Specify the essential educational qualifications and certifications for the role. +Professional Experience: Describe the relevant work experiences that are required or preferred. +Role Evolution: Analyze how the role might evolve in the future, considering industry trends and how these might influence the required skills. + +# Final Result: +Your analysis should be structured in a clear and organized document with distinct sections for each of the points listed above. Each section should contain: +This comprehensive overview will serve as a guideline for the recruitment process, ensuring the identification of the most qualified candidates. + +# Job Description: +``` +{text} +``` + +--- + +# Job Description Summary""" diff --git a/src/libs/resume_and_cover_builder/resume_prompt/__init__.py b/src/libs/resume_and_cover_builder/resume_prompt/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/libs/resume_and_cover_builder/resume_prompt/strings_feder-cr.py b/src/libs/resume_and_cover_builder/resume_prompt/strings_feder-cr.py new file mode 100644 index 00000000..53b16189 --- /dev/null +++ b/src/libs/resume_and_cover_builder/resume_prompt/strings_feder-cr.py @@ -0,0 +1,92 @@ +from libs.resume_and_cover_builder.template_base import * + +prompt_header = """ +Act as an HR expert and resume writer specializing in ATS-friendly resumes. Your task is to create a professional and polished header for the resume. The header should: + +1. **Contact Information**: Include your full name, city and country, phone number, email address, LinkedIn profile, and GitHub profile. Exclude any information that is not provided. +2. **Formatting**: Ensure the contact details are presented clearly and are easy to read. + +- **My information:** + {personal_information} +""" + prompt_header_template + + +prompt_education = """ +Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to articulate the educational background for a resume. For each educational entry, ensure you include: + +1. **Institution Name and Location**: Specify the university or educational institution’s name and location. +2. **Degree and Field of Study**: Clearly indicate the degree earned and the field of study. +3. **Grade**: Include your Grade if it is strong and relevant. +4. **Relevant Coursework**: List key courses with their grades to showcase your academic strengths. + +- **My information:** + {education_details} +"""+ prompt_education_template + + +prompt_working_experience = """ +Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to detail the work experience for a resume. For each job entry, ensure you include: + +1. **Company Name and Location**: Provide the name of the company and its location. +2. **Job Title**: Clearly state your job title. +3. **Dates of Employment**: Include the start and end dates of your employment. +4. **Responsibilities and Achievements**: Describe your key responsibilities and notable achievements, emphasizing measurable results and specific contributions. + +- **My information:** + {experience_details} +"""+ prompt_working_experience_template + + +prompt_projects = """ +Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to highlight notable side projects. For each project, ensure you include: + +1. **Project Name and Link**: Provide the name of the project and include a link to the GitHub repository or project page. +2. **Project Details**: Describe any notable recognition or achievements related to the project, such as GitHub stars or community feedback. +3. **Technical Contributions**: Highlight your specific contributions and the technologies used in the project. + +- **My information:** + {projects} +"""+ prompt_projects_template + + +prompt_achievements = """ +Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to list significant achievements. For each achievement, ensure you include: + +1. **Award or Recognition**: Clearly state the name of the award, recognition, scholarship, or honor. +2. **Description**: Provide a brief description of the achievement and its relevance to your career or academic journey. + +- **My information:** + {achievements} +"""+ prompt_achievements_template + + +prompt_certifications = """ +Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to list significant certifications based on the provided details. For each certification, ensure you include: + +1. **Certification Name**: Clearly state the name of the certification. +2. **Description**: Provide a brief description of the certification and its relevance to your professional or academic career. + +Ensure that the certifications are clearly presented and effectively highlight your qualifications. + +To implement this: + +If any of the certification details (e.g., descriptions) are not provided (i.e., None), omit those sections when filling out the template. + +- **My information:** + {certifications} + +"""+ prompt_certifications_template + + +prompt_additional_skills = """ +Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to list additional skills relevant to the job. For each skill, ensure you include: + +1. **Skill Category**: Clearly state the category or type of skill. +2. **Specific Skills**: List the specific skills or technologies within each category. +3. **Proficiency and Experience**: Briefly describe your experience and proficiency level. + +- **My information:** + {languages} + {interests} + {skills} +"""+ prompt_additional_skills_template diff --git a/src/libs/resume_and_cover_builder/resume_style/__init__.py b/src/libs/resume_and_cover_builder/resume_style/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/libs/resume_and_cover_builder/resume_style/style_cloyola.css b/src/libs/resume_and_cover_builder/resume_style/style_cloyola.css new file mode 100644 index 00000000..58cef557 --- /dev/null +++ b/src/libs/resume_and_cover_builder/resume_style/style_cloyola.css @@ -0,0 +1,149 @@ +/*Cloyola Grey $https://github.com/cloyola*/ +@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600;700&display=swap'); +@import url('https://fonts.googleapis.com/css2?family=Roboto:ital,wght@0,100;0,300;0,400;0,500;0,700;0,900;1,100;1,300;1,400;1,500;1,700;1,900&display=swap'); + +body { + font-family: 'Roboto', sans-serif; + line-height: 1.4; + color: #333; + max-width: 700px; + margin: 0 auto; + padding: 10px; + font-size: 9pt; +} + +header { + text-align: left; + margin-bottom: 20px; + background-color: #7c7c7c40; + padding: 20px; + border-radius: 8px; + box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); +} + +h1 { + font-size: 18pt; + font-weight: 700; + margin: 0 0 5px 0; +} + +.contact-info { + display: flex; + justify-content: left; + flex-wrap: wrap; + gap: 10px; + font-size: 9pt; + font-weight: normal; +} + +.contact-info p { + margin: 0; +} + +.contact-info a { + color: #0077b5; + text-decoration: none; +} + +.fab, +.fas { + margin-right: 3px; +} + +span.entry-location { + font-weight: normal; +} + +h2 { + font-size: 14pt; + font-weight: 600; + border-bottom: 1px dotted #4c4c4c; + padding-bottom: 2px; + margin: 10px 0 5px 0; + text-align: left; +} + +.entry { + margin-bottom: 15px; /*margin-bottom: 8px;*/ + background-color: #fff; + padding: 15px; + border-radius: 8px; + box-shadow: 3px 3px 5px 2px rgba(0, 0, 0, 0.2); +} + +.entry-header { + display: flex; + justify-content: space-between; + font-weight: 600; + font-size: 10pt; +} + +.entry-details { + display: flex; + justify-content: space-between; + font-style: italic; + margin-bottom: 2px; + font-size: 9pt; +} + +.compact-list { + margin: 2px 0; + padding-left: 15px; +} + +.compact-list li { + margin-bottom: 2px; +} + +.two-column { + display: flex; + justify-content: space-between; +} + +.two-column ul { + width: 48%; + margin: 0; + padding-left: 15px; + list-style-type: circle; +} + +a { + color: #0077b5; + text-decoration: none; +} + +a:hover { + text-decoration: underline; +} + +@media print { + body { + padding: 0; + margin: 0; + font-size: 9pt; + } + + @page { + margin: 0.5cm; + } + + h1 { + font-size: 18pt; + } + + h2 { + font-size: 11pt; + } + + .contact-info { + font-size: 8pt; + } + + .entry-details { + font-size: 7pt; + } + + .compact-list { + padding-left: 12px; + } +} diff --git a/src/libs/resume_and_cover_builder/resume_style/style_josylad_blue.css b/src/libs/resume_and_cover_builder/resume_style/style_josylad_blue.css new file mode 100644 index 00000000..ae5091e0 --- /dev/null +++ b/src/libs/resume_and_cover_builder/resume_style/style_josylad_blue.css @@ -0,0 +1,166 @@ +/*Modern Blue$https://github.com/josylad*/ + +@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600;700&display=swap'); + +body { + font-family: 'Poppins', sans-serif; + line-height: 1.6; + color: #2c3e50; + max-width: 850px; + margin: 0 auto; + padding: 20px; + font-size: 10pt; + background-color: #f9f9f9; +} + +header { + text-align: center; + margin-bottom: 20px; + background-color: #3498db; + padding: 20px; + border-radius: 8px; + box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); +} + +h1 { + font-size: 28pt; + font-weight: 700; + margin: 0 0 10px 0; + color: #fff; +} + +.contact-info { + display: flex; + justify-content: center; + flex-wrap: wrap; + gap: 15px; + font-size: 10pt; + font-weight: 300; + color: #ecf0f1; +} + +.contact-info p { + margin: 0; +} + +.contact-info a { + color: #ecf0f1; + text-decoration: none; + transition: color 0.3s ease; +} + +.contact-info a:hover { + color: #2c3e50; +} + +.fab, +.fas { + margin-right: 5px; +} + +h2 { + font-size: 18pt; + font-weight: 600; + border-bottom: 2px solid #3498db; + padding-bottom: 5px; + margin: 20px 0 15px 0; + color: #2c3e50; +} + +.entry { + margin-bottom: 15px; + background-color: #fff; + padding: 15px; + border-radius: 8px; + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); +} + +.entry-header { + display: flex; + justify-content: space-between; + font-weight: 600; + color: #3498db; +} + +.entry-details { + display: flex; + justify-content: space-between; + font-style: italic; + margin-bottom: 8px; + font-size: 9pt; + color: #7f8c8d; +} + +.compact-list { + margin: 5px 0; + padding-left: 20px; +} + +.compact-list li { + margin-bottom: 5px; +} + +.two-column { + display: flex; + justify-content: space-between; + flex-wrap: wrap; +} + +.two-column ul { + width: 48%; + margin: 0; + padding-left: 20px; +} + +a { + color: #3498db; + text-decoration: none; + transition: color 0.3s ease; +} + +a:hover { + color: #2980b9; + text-decoration: underline; +} + +@media print { + body { + padding: 0; + margin: 0; + font-size: 9pt; + background-color: #fff; + } + + @page { + margin: 1cm; + } + + h1 { + font-size: 24pt; + } + + h2 { + font-size: 16pt; + } + + .contact-info { + font-size: 9pt; + } + + .entry-details { + font-size: 8pt; + } + + .compact-list { + padding-left: 15px; + } + + header { + box-shadow: none; + } + + .entry { + box-shadow: none; + padding: 10px 0; + } +} \ No newline at end of file diff --git a/src/libs/resume_and_cover_builder/resume_style/style_josylad_grey.css b/src/libs/resume_and_cover_builder/resume_style/style_josylad_grey.css new file mode 100644 index 00000000..b56692f3 --- /dev/null +++ b/src/libs/resume_and_cover_builder/resume_style/style_josylad_grey.css @@ -0,0 +1,208 @@ +/*Modern Grey$https://github.com/josylad*/ +@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600;700&display=swap'); + +body { + font-family: 'Poppins', sans-serif; + line-height: 1.6; + color: #333; + max-width: 850px; + margin: 0 auto; + padding: 20px; + font-size: 10pt; + background-color: #f9f9f9; +} + +header { + text-align: center; + margin-bottom: 20px; + background-color: #4a4a4a; + padding: 20px; + border-radius: 8px; + box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); +} + +h1 { + font-size: 28pt; + font-weight: 700; + margin: 0 0 10px 0; + color: #fff; +} + +.contact-info { + display: flex; + justify-content: center; + flex-wrap: wrap; + gap: 15px; + font-size: 10pt; + font-weight: 300; + color: #e0e0e0; +} + +.contact-info p { + margin: 0; +} + +.contact-info a { + color: #e0e0e0; + text-decoration: none; + transition: color 0.3s ease; +} + +.contact-info a:hover { + color: #fff; +} + +.fab, +.fas { + margin-right: 5px; +} + +h2 { + font-size: 18pt; + font-weight: 600; + border-bottom: 2px solid #4a4a4a; + padding-bottom: 5px; + margin: 20px 0 15px 0; + color: #333; +} + +.entry { + margin-bottom: 15px; + background-color: #fff; + padding: 15px; + border-radius: 8px; + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); +} + +.entry-header { + display: flex; + justify-content: space-between; + font-weight: 600; + color: #4a4a4a; +} + +.entry-details { + display: flex; + justify-content: space-between; + font-style: italic; + margin-bottom: 8px; + font-size: 9pt; + color: #777; +} + +.compact-list { + margin: 5px 0; + padding-left: 20px; +} + +.compact-list li { + margin-bottom: 5px; +} + +.skills-section { + margin-top: 20px; +} + +.skills-section h2 { + font-size: 18pt; + font-weight: 600; + border-bottom: 2px solid #4a4a4a; + padding-bottom: 5px; + margin: 0 0 15px 0; + color: #333; + text-align: center; +} + +.skills-container { + display: flex; + justify-content: space-between; +} + +.skills-column { + width: 48%; +} + +.skills-list { + list-style-type: none; + padding: 0; + margin: 0; +} + +.skills-list li { + margin-bottom: 8px; + display: flex; + align-items: center; +} + +.skills-list li::before { + content: "•"; + color: #4a4a4a; + font-weight: bold; + display: inline-block; + width: 1em; + margin-right: 0.5em; +} + +a { + color: #4a4a4a; + text-decoration: none; + transition: color 0.3s ease; +} + +a:hover { + color: #333; + text-decoration: underline; +} + +@media print { + body { + padding: 0; + margin: 0; + font-size: 9pt; + background-color: #fff; + } + + @page { + margin: 1cm; + } + + h1 { + font-size: 24pt; + } + + h2 { + font-size: 16pt; + } + + .contact-info { + font-size: 9pt; + } + + .entry-details { + font-size: 8pt; + } + + .compact-list, + .skills-list { + padding-left: 15px; + } + + header { + box-shadow: none; + } + + .entry { + box-shadow: none; + padding: 10px 0; + } +} + +@media (max-width: 600px) { + .skills-container { + flex-direction: column; + } + + .skills-column { + width: 100%; + } +} \ No newline at end of file diff --git a/src/libs/resume_and_cover_builder/resume_style/style_krishnavalliappan.css b/src/libs/resume_and_cover_builder/resume_style/style_krishnavalliappan.css new file mode 100644 index 00000000..541be553 --- /dev/null +++ b/src/libs/resume_and_cover_builder/resume_style/style_krishnavalliappan.css @@ -0,0 +1,136 @@ +/*Default$https://github.com/krishnavalliappan*/ +body { + font-family: "Barlow", Arial, sans-serif; + line-height: 1.2; + color: #333; + max-width: 700px; + margin: 0 auto; + padding: 10px; + font-size: 9pt; +} + +header { + text-align: center; + margin-bottom: 10px; +} + +h1 { + font-size: 24pt; + font-weight: 700; + margin: 0 0 5px 0; +} + +.contact-info { + display: flex; + justify-content: center; + flex-wrap: wrap; + gap: 10px; + font-size: 9pt; + font-weight: normal; +} + +.contact-info p { + margin: 0; +} + +.contact-info a { + color: #0077b5; + text-decoration: none; +} + +.fab, +.fas { + margin-right: 3px; +} + +span { + font-weight: normal; +} + +h2 { + font-size: 16pt; + font-weight: 600; + border-bottom: 1px solid #333; + padding-bottom: 2px; + margin: 10px 0 5px 0; + text-align: center; +} + +.entry { + margin-bottom: 8px; +} + +.entry-header { + display: flex; + justify-content: space-between; + font-weight: 600; +} + +.entry-details { + display: flex; + justify-content: space-between; + font-style: italic; + margin-bottom: 2px; + font-size: 8pt; +} + +.compact-list { + margin: 2px 0; + padding-left: 15px; +} + +.compact-list li { + margin-bottom: 2px; +} + +.two-column { + display: flex; + justify-content: space-between; +} + +.two-column ul { + width: 48%; + margin: 0; + padding-left: 15px; +} + +a { + color: #0077b5; + text-decoration: none; +} + +a:hover { + text-decoration: underline; +} + +@media print { + body { + padding: 0; + margin: 0; + font-size: 9pt; + } + + @page { + margin: 0.5cm; + } + + h1 { + font-size: 18pt; + } + + h2 { + font-size: 11pt; + } + + .contact-info { + font-size: 8pt; + } + + .entry-details { + font-size: 7pt; + } + + .compact-list { + padding-left: 12px; + } +} diff --git a/src/libs/resume_and_cover_builder/resume_style/style_samodum_bold.css b/src/libs/resume_and_cover_builder/resume_style/style_samodum_bold.css new file mode 100644 index 00000000..05a4d319 --- /dev/null +++ b/src/libs/resume_and_cover_builder/resume_style/style_samodum_bold.css @@ -0,0 +1,191 @@ +/*Clean Blue$https://github.com/samodum*/ +@import url("https://fonts.googleapis.com/css2?family=Josefin+Sans&family=Kaisei+HarunoUmi&family=Open+Sans:ital,wght@0,400;0,600;1,400&display=swap"); + +:root { + --pageWidth: 49.62rem; + --textColor: #383838; + --lineColorA: #b8b8b8; + --accentColor: blue; + --HFont: "Josefin Sans", sans-serif; + --PFont: "Open Sans", sans-serif; + --BText: "Kaisei HarunoUmi", serif; + --sectionSpacing: 1.5rem; + --bodyFontSize: 0.875rem; + --KeyColumn: 9.375rem; +} + +* { + margin: 0; + padding: 0; + box-sizing: border-box; + color: var(--textColor); + font-size: var(--bodyFontSize); +} + +body { + /* border: 1px solid var(--accentColor); page guidelines*/ + max-width: var(--pageWidth); + padding: 3.375rem 1.5rem; + display: flex; + font-family: var(--PFont); + flex-direction: column; + gap: 1.5rem; + margin: 0 auto; +} + +main { + display: flex; + flex-direction: column; + gap: 1.5rem; + order: 2; +} + +a { + text-decoration: none; +} + +a:hover { + color: var(--accentColor); + transition: color 0.3s ease; +} + +header { + order: 0; + display: flex; + flex-direction: column; + justify-content: space-between; + align-items: start; + gap: 1.5rem; +} + +h1 { + font-family: var(--HFont); + font-size: 1.5rem; + font-weight: 400; + margin-bottom: -0.125rem; + color: var(--accentColor); +} + +.contact-info { + display: flex; + flex-direction: column; + gap: 0.125rem; +} + +.contact-info p { + font-family: var(--PFont); +} + +.contact-info p::before { + margin-right: 0.25rem; + text-transform: capitalize; + font-family: var(--HFont); + font-weight: 600; +} + +.contact-info p:nth-child(1)::before { + content: "address:"; +} +.contact-info p:nth-child(2)::before { + content: "phone:"; +} +.contact-info p:nth-child(3)::before { + content: "email:"; +} +.contact-info p:nth-child(4)::before { + content: "linkedin:"; +} +.contact-info p:nth-child(5)::before { + content: "github:"; +} + +section h2 { + font-family: var(--HFont); + font-size: 1.125rem; + font-weight: bold; + color: var(--accentColor); + padding-bottom: 0.25rem; + margin-bottom: 0.5rem; + border-bottom: 1px solid var(--lineColorA); +} + +.entry { + padding-top: 1rem; + display: grid; + grid-template-columns: 1fr 4fr; + column-gap: 10px; +} + +.entry:first-of-type { + padding-top: 0.5rem; +} + +.entry-header { + grid-column: 1; + font-family: var(--HFont); + font-weight: 600; + display: flex; + flex-direction: column; + gap: 0.25rem; +} + +.entry-details, +#side-projects .compact-list { + margin-top: -4px; +} + +.entry-details, +.compact-list { + grid-column: 2; +} + +.entry-title { + font-family: var(--HFont); + font-weight: 600; + margin-right: 0.25rem; +} + +.entry-year { + font-style: italic; +} + +.compact-list { + padding-left: 10px; + list-style-type: circle; + margin: 0; +} + +.compact-list li { + margin-left: 5px; +} + +#achievements .compact-list { + padding-top: 0.25rem; +} + +.two-column { + padding-top: 0.25rem; + display: grid; + grid-template-columns: 1fr 1fr; + column-gap: 10px; +} + +.two-column .compact-list:first-child { + grid-column: 1; +} + +#work-experience { + order: 1; +} +#education { + order: 2; +} +#achievements { + order: 3; +} +#side-projects { + order: 4; +} +#skills-languages { + order: 5; +} diff --git a/src/libs/resume_and_cover_builder/style_manager.py b/src/libs/resume_and_cover_builder/style_manager.py new file mode 100644 index 00000000..d6aa23e8 --- /dev/null +++ b/src/libs/resume_and_cover_builder/style_manager.py @@ -0,0 +1,126 @@ +# src/ai_hawk/libs/resume_and_cover_builder/style_manager.py +import os +from pathlib import Path +from typing import Dict, List, Tuple, Optional +import inquirer +import webbrowser +import sys +import logging + +# Configura il logging +logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s') + +class StyleManager: + + def __init__(self): + self.styles_directory: Optional[Path] = None + self.selected_style: Optional[str] = None + current_file = Path(__file__).resolve() + # Salire di 4 livelli per raggiungere la radice del progetto + project_root = current_file.parent.parent.parent.parent # Adatta se la struttura cambia + + # Imposta la directory degli stili in modo robusto + self.styles_directory = project_root / "src" / "libs" / "resume_and_cover_builder" / "resume_style" + + logging.debug(f"Project root determinato come: {project_root}") + logging.debug(f"Directory degli stili impostata su: {self.styles_directory}") + + def get_styles(self) -> Dict[str, Tuple[str, str]]: + """ + Ottiene gli stili disponibili nella directory degli stili. + Returns: + Dict[str, Tuple[str, str]]: Un dizionario che mappa i nomi degli stili ai loro file e link degli autori. + """ + styles_to_files = {} + if not self.styles_directory: + logging.warning("Directory degli stili non impostata.") + return styles_to_files + logging.debug(f"Leggendo la directory degli stili: {self.styles_directory}") + try: + files = [f for f in self.styles_directory.iterdir() if f.is_file()] + logging.debug(f"Files trovati: {[f.name for f in files]}") + for file_path in files: + logging.debug(f"Processando file: {file_path}") + with file_path.open('r', encoding='utf-8') as file: + first_line = file.readline().strip() + logging.debug(f"Prima linea del file {file_path.name}: {first_line}") + if first_line.startswith("/*") and first_line.endswith("*/"): + content = first_line[2:-2].strip() + if '$' in content: + style_name, author_link = content.split('$', 1) + style_name = style_name.strip() + author_link = author_link.strip() + styles_to_files[style_name] = (file_path.name, author_link) + logging.info(f"Aggiunto stile: {style_name} da {author_link}") + except FileNotFoundError: + logging.error(f"Directory {self.styles_directory} non trovata.") + except PermissionError: + logging.error(f"Permesso negato per accedere a {self.styles_directory}.") + except Exception as e: + logging.error(f"Errore imprevisto durante la lettura degli stili: {e}") + return styles_to_files + + def format_choices(self, styles_to_files: Dict[str, Tuple[str, str]]) -> List[str]: + """ + Format the style choices for the user. + Args: + styles_to_files (Dict[str, Tuple[str, str]]): A dictionary mapping style names to their file names and author links. + Returns: + List[str]: A list of formatted style choices. + """ + return [f"{style_name} (style author -> {author_link})" for style_name, (file_name, author_link) in styles_to_files.items()] + + def get_style_path(self) -> Path: + """ + Get the path to the selected style. + Args: + selected_style (str): The selected style. + Returns: + Path: a Path object representing the path to the selected style file. + """ + styles = self.get_styles() + if self.selected_style not in styles: + raise ValueError(f"Style '{self.selected_style}' not found.") + file_name, _ = styles[self.selected_style] + return self.styles_directory / file_name + + def choose_style(self) -> Optional[str]: + """ + Prompt the user to select a style using inquirer. + Returns: + Optional[str]: The name of the selected style, or None if selection was canceled. + """ + styles = self.get_styles() + if not styles: + logging.warning("Nessuno stile disponibile per la selezione.") + return None + + final_style_choice = "Crea il tuo stile di resume in CSS" + formatted_choices = self.format_choices(styles) + formatted_choices.append(final_style_choice) + + questions = [ + inquirer.List( + 'selected_style', + message="Quale stile vorresti adottare?", + choices=formatted_choices + ) + ] + + answers = inquirer.prompt(questions) + if answers and 'selected_style' in answers: + selected_display = answers['selected_style'] + if selected_display == final_style_choice: + tutorial_url = "https://github.com/feder-cr/lib_resume_builder_AIHawk/blob/main/how_to_contribute/web_designer.md" + logging.info("\nApro il tutorial nel tuo browser...") + webbrowser.open(tutorial_url) + sys.exit(0) + else: + # Estrai il nome dello stile dal formato "style_name (style author -> author_link)" + style_name = selected_display.split(' (')[0] + logging.info(f"Hai selezionato lo stile: {style_name}") + self.selected_style = style_name + return style_name + else: + logging.warning("Selezione annullata.") + return None diff --git a/src/libs/resume_and_cover_builder/template_base.py b/src/libs/resume_and_cover_builder/template_base.py new file mode 100644 index 00000000..6b379da1 --- /dev/null +++ b/src/libs/resume_and_cover_builder/template_base.py @@ -0,0 +1,239 @@ +""" +This module is used to store the global configuration of the application. +""" +# app/libs/resume_and_cover_builder/template_base.py + + + +prompt_cover_letter_template = """ +- **Template to Use** +``` +
+
+
+

[Your Name]

+

[Your Address]

+

[City, State ZIP]

+

[Your Email]

+

[Your Phone Number]

+
+
+

[Company Name]

+
+
+
+

Dear [Recipient Team],

+

[Opening paragraph: Introduce yourself and state the position you are applying for.]

+

[Body paragraphs: Highlight your qualifications, experiences, and how they align with the job requirements.]

+

[Closing paragraph: Express your enthusiasm for the position and thank the recipient for their consideration.]

+

Sincerely,

+

[Your Name]

+

[Date]

+
+
+``` +The results should be provided in html format, Provide only the html code for the cover letter, without any explanations or additional text and also without ```html ``` +""" +prompt_header_template = """ +- **Template to Use** +``` +
+

[Name and Surname]

+
+

+ [Your City, Your Country] +

+

+ [Your Prefix Phone number] +

+

+ [Your Email] +

+

+ LinkedIn +

+

+ GitHub +

+
+
+``` +The results should be provided in html format, Provide only the html code for the resume, without any explanations or additional text and also without ```html ``` +""" + +prompt_education_template = """ +- **Template to Use** +``` +
+

Education

+
+
+ [University Name] + [Location] +
+
+ [Degree] in [Field of Study] | Grade: [Your Grade] + [Start Year] – [End Year] +
+
    +
  • [Course Name] → Grade: [Grade]
    • +
  • [Course Name] → Grade: [Grade]
    • +
  • [Course Name] → Grade: [Grade]
    • +
  • [Course Name] → Grade: [Grade]
    • +
  • [Course Name] → Grade: [Grade]
    • +
+
+
+``` +The results should be provided in html format, Provide only the html code for the resume, without any explanations or additional text and also without ```html ```""" + + +prompt_working_experience_template = """ +- **Template to Use** +``` +
+

Work Experience

+
+
+ [Company Name] + [Location] +
+
+ [Your Job Title] + [Start Date] – [End Date] +
+
    +
  • [Describe your responsibilities and achievements in this role]
    • +
  • [Describe any key projects or technologies you worked with]
    • +
  • [Mention any notable accomplishments or results]
    • +
+
+
+
+ [Company Name] + [Location] +
+
+ [Your Job Title] + [Start Date] – [End Date] +
+
    +
  • [Describe your responsibilities and achievements in this role]
    • +
  • [Describe any key projects or technologies you worked with]
    • +
  • [Mention any notable accomplishments or results]
    • +
+
+
+
+ [Company Name] + [Location] +
+
+ [Your Job Title] + [Start Date] – [End Date] +
+
    +
  • [Describe your responsibilities and achievements in this role]
    • +
  • [Describe any key projects or technologies you worked with]
    • +
  • [Mention any notable accomplishments or results]
    • +
+
+
+``` +The results should be provided in html format, Provide only the html code for the resume, without any explanations or additional text and also without ```html ```""" + + +prompt_projects_template = """ +- **Template to Use** +``` +
+

Side Projects

+
+
+ [Project Name] +
+
    +
  • [Describe any notable recognition or reception]
    • +
  • [Describe any notable recognition or reception]
    • +
+
+
+
+ [Project Name] +
+
    +
  • [Describe any notable recognition or reception]
    • +
  • [Describe any notable recognition or reception]
    • +
+
+
+
+ [Project Name] +
+
    +
  • [Describe any notable recognition or reception]
    • +
  • [Describe any notable recognition or reception]
    • +
+
+
+``` +The results should be provided in html format, Provide only the html code for the resume, without any explanations or additional text and also without ```html ``` +""" + + +prompt_achievements_template = """ +- **Template to Use** +``` +
+

Achievements

+
    +
  • [Award or Recognition or Scholarship or Honor]: [Describe]
    • +
  • [Award or Recognition or Scholarship or Honor]: [Describe]
    • +
  • [Award or Recognition or Scholarship or Honor]: [Describe]
    • +
+
+``` +The results should be provided in html format, Provide only the html code for the resume, without any explanations or additional text and also without ```html ``` +""" + +prompt_certifications_template = """ +- **Template to Use** +``` +
+

Certifications

+
    +
  • [Certification Name]: [Describe]
    • +
  • [Certification Name]: [Describe]
    • +
+
+``` +The results should be provided in html format, Provide only the html code for the resume, without any explanations or additional text and also without ```html ``` +""" + +prompt_additional_skills_template = """ +- **Template to Use** +''' +
+

Additional Skills

+
+
    +
  • [Specific Skill or Technology]
    • +
  • [Specific Skill or Technology]
    • +
  • [Specific Skill or Technology]
    • +
  • [Specific Skill or Technology]
    • +
  • [Specific Skill or Technology]
    • +
  • [Specific Skill or Technology]
    • +
+
    +
  • [Specific Skill or Technology]
    • +
  • [Specific Skill or Technology]
    • +
  • [Specific Skill or Technology]
    • +
  • [Specific Skill or Technology]
    • +
  • [Specific Skill or Technology]
    • +
  • Languages:
    • +
+
+
+''' +The results should be provided in html format, Provide only the html code for the resume, without any explanations or additional text and also without ```html ``` +""" diff --git a/src/libs/resume_and_cover_builder/utils.py b/src/libs/resume_and_cover_builder/utils.py new file mode 100644 index 00000000..9ae2a0e0 --- /dev/null +++ b/src/libs/resume_and_cover_builder/utils.py @@ -0,0 +1,131 @@ +""" +This module contains utility functions for the Resume and Cover Letter Builder service. +""" + +# app/libs/resume_and_cover_builder/utils.py +import json +import openai +import time +from datetime import datetime +from typing import Dict, List +from langchain_core.messages.ai import AIMessage +from langchain_core.prompt_values import StringPromptValue +from langchain_openai import ChatOpenAI +from .config import global_config +from loguru import logger +from requests.exceptions import HTTPError as HTTPStatusError + + +class LLMLogger: + + def __init__(self, llm: ChatOpenAI): + self.llm = llm + + @staticmethod + def log_request(prompts, parsed_reply: Dict[str, Dict]): + calls_log = global_config.LOG_OUTPUT_FILE_PATH / "open_ai_calls.json" + if isinstance(prompts, StringPromptValue): + prompts = prompts.text + elif isinstance(prompts, Dict): + # Convert prompts to a dictionary if they are not in the expected format + prompts = { + f"prompt_{i+1}": prompt.content + for i, prompt in enumerate(prompts.messages) + } + else: + prompts = { + f"prompt_{i+1}": prompt.content + for i, prompt in enumerate(prompts.messages) + } + + current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + + # Extract token usage details from the response + token_usage = parsed_reply["usage_metadata"] + output_tokens = token_usage["output_tokens"] + input_tokens = token_usage["input_tokens"] + total_tokens = token_usage["total_tokens"] + + # Extract model details from the response + model_name = parsed_reply["response_metadata"]["model_name"] + prompt_price_per_token = 0.00000015 + completion_price_per_token = 0.0000006 + + # Calculate the total cost of the API call + total_cost = (input_tokens * prompt_price_per_token) + ( + output_tokens * completion_price_per_token + ) + + # Create a log entry with all relevant information + log_entry = { + "model": model_name, + "time": current_time, + "prompts": prompts, + "replies": parsed_reply["content"], # Response content + "total_tokens": total_tokens, + "input_tokens": input_tokens, + "output_tokens": output_tokens, + "total_cost": total_cost, + } + + # Write the log entry to the log file in JSON format + with open(calls_log, "a", encoding="utf-8") as f: + json_string = json.dumps(log_entry, ensure_ascii=False, indent=4) + f.write(json_string + "\n") + + +class LoggerChatModel: + + def __init__(self, llm: ChatOpenAI): + self.llm = llm + + def __call__(self, messages: List[Dict[str, str]]) -> str: + max_retries = 15 + retry_delay = 10 + + for attempt in range(max_retries): + try: + reply = self.llm.invoke(messages) + parsed_reply = self.parse_llmresult(reply) + LLMLogger.log_request(prompts=messages, parsed_reply=parsed_reply) + return reply + except (openai.RateLimitError, HTTPStatusError) as err: + if isinstance(err, HTTPStatusError) and err.response.status_code == 429: + logger.warning(f"HTTP 429 Too Many Requests: Waiting for {retry_delay} seconds before retrying (Attempt {attempt + 1}/{max_retries})...") + time.sleep(retry_delay) + retry_delay *= 2 + else: + wait_time = self.parse_wait_time_from_error_message(str(err)) + logger.warning(f"Rate limit exceeded or API error. Waiting for {wait_time} seconds before retrying (Attempt {attempt + 1}/{max_retries})...") + time.sleep(wait_time) + except Exception as e: + logger.error(f"Unexpected error occurred: {str(e)}, retrying in {retry_delay} seconds... (Attempt {attempt + 1}/{max_retries})") + time.sleep(retry_delay) + retry_delay *= 2 + + logger.critical("Failed to get a response from the model after multiple attempts.") + raise Exception("Failed to get a response from the model after multiple attempts.") + + def parse_llmresult(self, llmresult: AIMessage) -> Dict[str, Dict]: + # Parse the LLM result into a structured format. + content = llmresult.content + response_metadata = llmresult.response_metadata + id_ = llmresult.id + usage_metadata = llmresult.usage_metadata + + parsed_result = { + "content": content, + "response_metadata": { + "model_name": response_metadata.get("model_name", ""), + "system_fingerprint": response_metadata.get("system_fingerprint", ""), + "finish_reason": response_metadata.get("finish_reason", ""), + "logprobs": response_metadata.get("logprobs", None), + }, + "id": id_, + "usage_metadata": { + "input_tokens": usage_metadata.get("input_tokens", 0), + "output_tokens": usage_metadata.get("output_tokens", 0), + "total_tokens": usage_metadata.get("total_tokens", 0), + }, + } + return parsed_result diff --git a/src/logging.py b/src/logging.py new file mode 100644 index 00000000..20b1448c --- /dev/null +++ b/src/logging.py @@ -0,0 +1,80 @@ +import logging.handlers +import os +import sys +import logging +from loguru import logger +from selenium.webdriver.remote.remote_connection import LOGGER as selenium_logger + +from config import LOG_LEVEL, LOG_SELENIUM_LEVEL, LOG_TO_CONSOLE, LOG_TO_FILE + + +def remove_default_loggers(): + """Remove default loggers from root logger.""" + root_logger = logging.getLogger() + if root_logger.hasHandlers(): + root_logger.handlers.clear() + if os.path.exists("log/app.log"): + os.remove("log/app.log") + +def init_loguru_logger(): + """Initialize and configure loguru logger.""" + + def get_log_filename(): + return f"log/app.log" + + log_file = get_log_filename() + + os.makedirs(os.path.dirname(log_file), exist_ok=True) + + logger.remove() + + # Add file logger if LOG_TO_FILE is True + if LOG_TO_FILE: + logger.add( + log_file, + level=LOG_LEVEL, + rotation="10 MB", + retention="1 week", + compression="zip", + format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <8} | {name}:{function}:{line} - {message}", + backtrace=True, + diagnose=True, + ) + + # Add console logger if LOG_TO_CONSOLE is True + if LOG_TO_CONSOLE: + logger.add( + sys.stderr, + level=LOG_LEVEL, + format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <8} | {name}:{function}:{line} - {message}", + backtrace=True, + diagnose=True, + ) + + +def init_selenium_logger(): + """Initialize and configure selenium logger to write to selenium.log.""" + log_file = "log/selenium.log" + os.makedirs(os.path.dirname(log_file), exist_ok=True) + + selenium_logger.handlers.clear() + + selenium_logger.setLevel(LOG_SELENIUM_LEVEL) + + # Create file handler for selenium logger + file_handler = logging.handlers.TimedRotatingFileHandler( + log_file, when="D", interval=1, backupCount=5 + ) + file_handler.setLevel(LOG_SELENIUM_LEVEL) + + # Define a simplified format for selenium logger entries + formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") + file_handler.setFormatter(formatter) + + # Add the file handler to selenium_logger + selenium_logger.addHandler(file_handler) + + +remove_default_loggers() +init_loguru_logger() +init_selenium_logger() diff --git a/src/resume_schemas/job_application_profile.py b/src/resume_schemas/job_application_profile.py new file mode 100644 index 00000000..8a74bdb7 --- /dev/null +++ b/src/resume_schemas/job_application_profile.py @@ -0,0 +1,186 @@ +from dataclasses import dataclass + +import yaml + +from src.logging import logger + + +@dataclass +class SelfIdentification: + gender: str + pronouns: str + veteran: str + disability: str + ethnicity: str + + +@dataclass +class LegalAuthorization: + eu_work_authorization: str + us_work_authorization: str + requires_us_visa: str + legally_allowed_to_work_in_us: str + requires_us_sponsorship: str + requires_eu_visa: str + legally_allowed_to_work_in_eu: str + requires_eu_sponsorship: str + canada_work_authorization: str + requires_canada_visa: str + legally_allowed_to_work_in_canada: str + requires_canada_sponsorship: str + uk_work_authorization: str + requires_uk_visa: str + legally_allowed_to_work_in_uk: str + requires_uk_sponsorship: str + + + +@dataclass +class WorkPreferences: + remote_work: str + in_person_work: str + open_to_relocation: str + willing_to_complete_assessments: str + willing_to_undergo_drug_tests: str + willing_to_undergo_background_checks: str + + +@dataclass +class Availability: + notice_period: str + + +@dataclass +class SalaryExpectations: + salary_range_usd: str + + +@dataclass +class JobApplicationProfile: + self_identification: SelfIdentification + legal_authorization: LegalAuthorization + work_preferences: WorkPreferences + availability: Availability + salary_expectations: SalaryExpectations + + def __init__(self, yaml_str: str): + logger.debug("Initializing JobApplicationProfile with provided YAML string") + try: + data = yaml.safe_load(yaml_str) + logger.debug(f"YAML data successfully parsed: {data}") + except yaml.YAMLError as e: + logger.error(f"Error parsing YAML file: {e}") + raise ValueError("Error parsing YAML file.") from e + except Exception as e: + logger.error(f"Unexpected error occurred while parsing the YAML file: {e}") + raise RuntimeError("An unexpected error occurred while parsing the YAML file.") from e + + if not isinstance(data, dict): + logger.error(f"YAML data must be a dictionary, received: {type(data)}") + raise TypeError("YAML data must be a dictionary.") + + # Process self_identification + try: + logger.debug("Processing self_identification") + self.self_identification = SelfIdentification(**data['self_identification']) + logger.debug(f"self_identification processed: {self.self_identification}") + except KeyError as e: + logger.error(f"Required field {e} is missing in self_identification data.") + raise KeyError(f"Required field {e} is missing in self_identification data.") from e + except TypeError as e: + logger.error(f"Error in self_identification data: {e}") + raise TypeError(f"Error in self_identification data: {e}") from e + except AttributeError as e: + logger.error(f"Attribute error in self_identification processing: {e}") + raise AttributeError("Attribute error in self_identification processing.") from e + except Exception as e: + logger.error(f"An unexpected error occurred while processing self_identification: {e}") + raise RuntimeError("An unexpected error occurred while processing self_identification.") from e + + # Process legal_authorization + try: + logger.debug("Processing legal_authorization") + self.legal_authorization = LegalAuthorization(**data['legal_authorization']) + logger.debug(f"legal_authorization processed: {self.legal_authorization}") + except KeyError as e: + logger.error(f"Required field {e} is missing in legal_authorization data.") + raise KeyError(f"Required field {e} is missing in legal_authorization data.") from e + except TypeError as e: + logger.error(f"Error in legal_authorization data: {e}") + raise TypeError(f"Error in legal_authorization data: {e}") from e + except AttributeError as e: + logger.error(f"Attribute error in legal_authorization processing: {e}") + raise AttributeError("Attribute error in legal_authorization processing.") from e + except Exception as e: + logger.error(f"An unexpected error occurred while processing legal_authorization: {e}") + raise RuntimeError("An unexpected error occurred while processing legal_authorization.") from e + + # Process work_preferences + try: + logger.debug("Processing work_preferences") + self.work_preferences = WorkPreferences(**data['work_preferences']) + logger.debug(f"Work_preferences processed: {self.work_preferences}") + except KeyError as e: + logger.error(f"Required field {e} is missing in work_preferences data.") + raise KeyError(f"Required field {e} is missing in work_preferences data.") from e + except TypeError as e: + logger.error(f"Error in work_preferences data: {e}") + raise TypeError(f"Error in work_preferences data: {e}") from e + except AttributeError as e: + logger.error(f"Attribute error in work_preferences processing: {e}") + raise AttributeError("Attribute error in work_preferences processing.") from e + except Exception as e: + logger.error(f"An unexpected error occurred while processing work_preferences: {e}") + raise RuntimeError("An unexpected error occurred while processing work_preferences.") from e + + # Process availability + try: + logger.debug("Processing availability") + self.availability = Availability(**data['availability']) + logger.debug(f"Availability processed: {self.availability}") + except KeyError as e: + logger.error(f"Required field {e} is missing in availability data.") + raise KeyError(f"Required field {e} is missing in availability data.") from e + except TypeError as e: + logger.error(f"Error in availability data: {e}") + raise TypeError(f"Error in availability data: {e}") from e + except AttributeError as e: + logger.error(f"Attribute error in availability processing: {e}") + raise AttributeError("Attribute error in availability processing.") from e + except Exception as e: + logger.error(f"An unexpected error occurred while processing availability: {e}") + raise RuntimeError("An unexpected error occurred while processing availability.") from e + + # Process salary_expectations + try: + logger.debug("Processing salary_expectations") + self.salary_expectations = SalaryExpectations(**data['salary_expectations']) + logger.debug(f"salary_expectations processed: {self.salary_expectations}") + except KeyError as e: + logger.error(f"Required field {e} is missing in salary_expectations data.") + raise KeyError(f"Required field {e} is missing in salary_expectations data.") from e + except TypeError as e: + logger.error(f"Error in salary_expectations data: {e}") + raise TypeError(f"Error in salary_expectations data: {e}") from e + except AttributeError as e: + logger.error(f"Attribute error in salary_expectations processing: {e}") + raise AttributeError("Attribute error in salary_expectations processing.") from e + except Exception as e: + logger.error(f"An unexpected error occurred while processing salary_expectations: {e}") + raise RuntimeError("An unexpected error occurred while processing salary_expectations.") from e + + logger.debug("JobApplicationProfile initialization completed successfully.") + + def __str__(self): + logger.debug("Generating string representation of JobApplicationProfile") + + def format_dataclass(obj): + return "\n".join(f"{field.name}: {getattr(obj, field.name)}" for field in obj.__dataclass_fields__.values()) + + formatted_str = (f"Self Identification:\n{format_dataclass(self.self_identification)}\n\n" + f"Legal Authorization:\n{format_dataclass(self.legal_authorization)}\n\n" + f"Work Preferences:\n{format_dataclass(self.work_preferences)}\n\n" + f"Availability: {self.availability.notice_period}\n\n" + f"Salary Expectations: {self.salary_expectations.salary_range_usd}\n\n") + logger.debug(f"String representation generated: {formatted_str}") + return formatted_str diff --git a/src/resume_schemas/resume.py b/src/resume_schemas/resume.py new file mode 100644 index 00000000..ee70e74a --- /dev/null +++ b/src/resume_schemas/resume.py @@ -0,0 +1,197 @@ +from dataclasses import dataclass, field +from typing import List, Dict, Any, Optional, Union +import yaml +from pydantic import BaseModel, EmailStr, HttpUrl, Field + + + +class PersonalInformation(BaseModel): + name: Optional[str] + surname: Optional[str] + date_of_birth: Optional[str] + country: Optional[str] + city: Optional[str] + address: Optional[str] + zip_code: Optional[str] = Field(None, min_length=5, max_length=10) + phone_prefix: Optional[str] + phone: Optional[str] + email: Optional[EmailStr] + github: Optional[HttpUrl] = None + linkedin: Optional[HttpUrl] = None + + +class EducationDetails(BaseModel): + education_level: Optional[str] + institution: Optional[str] + field_of_study: Optional[str] + final_evaluation_grade: Optional[str] + start_date: Optional[str] + year_of_completion: Optional[int] + exam: Optional[Union[List[Dict[str, str]], Dict[str, str]]] = None + + +class ExperienceDetails(BaseModel): + position: Optional[str] + company: Optional[str] + employment_period: Optional[str] + location: Optional[str] + industry: Optional[str] + key_responsibilities: Optional[List[Dict[str, str]]] = None + skills_acquired: Optional[List[str]] = None + + +class Project(BaseModel): + name: Optional[str] + description: Optional[str] + link: Optional[HttpUrl] = None + + +class Achievement(BaseModel): + name: Optional[str] + description: Optional[str] + + +class Certifications(BaseModel): + name: Optional[str] + description: Optional[str] + + +class Language(BaseModel): + language: Optional[str] + proficiency: Optional[str] + + +class Availability(BaseModel): + notice_period: Optional[str] + + +class SalaryExpectations(BaseModel): + salary_range_usd: Optional[str] + + +class SelfIdentification(BaseModel): + gender: Optional[str] + pronouns: Optional[str] + veteran: Optional[str] + disability: Optional[str] + ethnicity: Optional[str] + + +class LegalAuthorization(BaseModel): + eu_work_authorization: Optional[str] + us_work_authorization: Optional[str] + requires_us_visa: Optional[str] + requires_us_sponsorship: Optional[str] + requires_eu_visa: Optional[str] + legally_allowed_to_work_in_eu: Optional[str] + legally_allowed_to_work_in_us: Optional[str] + requires_eu_sponsorship: Optional[str] + + +class Resume(BaseModel): + personal_information: Optional[PersonalInformation] + education_details: Optional[List[EducationDetails]] = None + experience_details: Optional[List[ExperienceDetails]] = None + projects: Optional[List[Project]] = None + achievements: Optional[List[Achievement]] = None + certifications: Optional[List[Certifications]] = None + languages: Optional[List[Language]] = None + interests: Optional[List[str]] = None + + @staticmethod + def normalize_exam_format(exam): + if isinstance(exam, dict): + return [{k: v} for k, v in exam.items()] + return exam + + def __init__(self, yaml_str: str): + try: + # Parse the YAML string + data = yaml.safe_load(yaml_str) + + if 'education_details' in data: + for ed in data['education_details']: + if 'exam' in ed: + ed['exam'] = self.normalize_exam_format(ed['exam']) + + # Create an instance of Resume from the parsed data + super().__init__(**data) + except yaml.YAMLError as e: + raise ValueError("Error parsing YAML file.") from e + except Exception as e: + raise Exception(f"Unexpected error while parsing YAML: {e}") from e + + + def _process_personal_information(self, data: Dict[str, Any]) -> PersonalInformation: + try: + return PersonalInformation(**data) + except TypeError as e: + raise TypeError(f"Invalid data for PersonalInformation: {e}") from e + except AttributeError as e: + raise AttributeError(f"AttributeError in PersonalInformation: {e}") from e + except Exception as e: + raise Exception(f"Unexpected error in PersonalInformation processing: {e}") from e + + def _process_education_details(self, data: List[Dict[str, Any]]) -> List[EducationDetails]: + education_list = [] + for edu in data: + try: + exams = [Exam(name=k, grade=v) for k, v in edu.get('exam', {}).items()] + education = EducationDetails( + education_level=edu.get('education_level'), + institution=edu.get('institution'), + field_of_study=edu.get('field_of_study'), + final_evaluation_grade=edu.get('final_evaluation_grade'), + start_date=edu.get('start_date'), + year_of_completion=edu.get('year_of_completion'), + exam=exams + ) + education_list.append(education) + except KeyError as e: + raise KeyError(f"Missing field in education details: {e}") from e + except TypeError as e: + raise TypeError(f"Invalid data for Education: {e}") from e + except AttributeError as e: + raise AttributeError(f"AttributeError in Education: {e}") from e + except Exception as e: + raise Exception(f"Unexpected error in Education processing: {e}") from e + return education_list + + def _process_experience_details(self, data: List[Dict[str, Any]]) -> List[ExperienceDetails]: + experience_list = [] + for exp in data: + try: + key_responsibilities = [ + Responsibility(description=list(resp.values())[0]) + for resp in exp.get('key_responsibilities', []) + ] + skills_acquired = [str(skill) for skill in exp.get('skills_acquired', [])] + experience = ExperienceDetails( + position=exp['position'], + company=exp['company'], + employment_period=exp['employment_period'], + location=exp['location'], + industry=exp['industry'], + key_responsibilities=key_responsibilities, + skills_acquired=skills_acquired + ) + experience_list.append(experience) + except KeyError as e: + raise KeyError(f"Missing field in experience details: {e}") from e + except TypeError as e: + raise TypeError(f"Invalid data for Experience: {e}") from e + except AttributeError as e: + raise AttributeError(f"AttributeError in Experience: {e}") from e + except Exception as e: + raise Exception(f"Unexpected error in Experience processing: {e}") from e + return experience_list + + +@dataclass +class Exam: + name: str + grade: str + +@dataclass +class Responsibility: + description: str \ No newline at end of file diff --git a/src/utils/__init__.py b/src/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/utils/browser_utils.py b/src/utils/browser_utils.py new file mode 100644 index 00000000..6b150461 --- /dev/null +++ b/src/utils/browser_utils.py @@ -0,0 +1,86 @@ + +import random +import time + +from src.logging import logger + + +def is_scrollable(element): + scroll_height = element.get_attribute("scrollHeight") + client_height = element.get_attribute("clientHeight") + scrollable = int(scroll_height) > int(client_height) + logger.debug(f"Element scrollable check: scrollHeight={scroll_height}, clientHeight={client_height}, scrollable={scrollable}") + return scrollable + + +def scroll_slow(driver, scrollable_element, start=0, end=3600, step=300, reverse=False): + logger.debug(f"Starting slow scroll: start={start}, end={end}, step={step}, reverse={reverse}") + + if reverse: + start, end = end, start + step = -step + + if step == 0: + logger.error("Step value cannot be zero.") + raise ValueError("Step cannot be zero.") + + max_scroll_height = int(scrollable_element.get_attribute("scrollHeight")) + current_scroll_position = int(float(scrollable_element.get_attribute("scrollTop"))) + logger.debug(f"Max scroll height of the element: {max_scroll_height}") + logger.debug(f"Current scroll position: {current_scroll_position}") + + if reverse: + if current_scroll_position < start: + start = current_scroll_position + logger.debug(f"Adjusted start position for upward scroll: {start}") + else: + if end > max_scroll_height: + logger.warning(f"End value exceeds the scroll height. Adjusting end to {max_scroll_height}") + end = max_scroll_height + + script_scroll_to = "arguments[0].scrollTop = arguments[1];" + + try: + if scrollable_element.is_displayed(): + if not is_scrollable(scrollable_element): + logger.warning("The element is not scrollable.") + return + + if (step > 0 and start >= end) or (step < 0 and start <= end): + logger.warning("No scrolling will occur due to incorrect start/end values.") + return + + position = start + previous_position = None # Tracking the previous position to avoid duplicate scrolls + while (step > 0 and position < end) or (step < 0 and position > end): + if position == previous_position: + # Avoid re-scrolling to the same position + logger.debug(f"Stopping scroll as position hasn't changed: {position}") + break + + try: + driver.execute_script(script_scroll_to, scrollable_element, position) + logger.debug(f"Scrolled to position: {position}") + except Exception as e: + logger.error(f"Error during scrolling: {e}") + + previous_position = position + position += step + + # Decrease the step but ensure it doesn't reverse direction + step = max(10, abs(step) - 10) * (-1 if reverse else 1) + + time.sleep(random.uniform(0.6, 1.5)) + + # Ensure the final scroll position is correct + driver.execute_script(script_scroll_to, scrollable_element, end) + logger.debug(f"Scrolled to final position: {end}") + time.sleep(0.5) + else: + logger.warning("The element is not visible.") + except Exception as e: + logger.error(f"Exception occurred during scrolling: {e}") + +def remove_focus_active_element(driver): + driver.execute_script("document.activeElement.blur();") + logger.debug("Removed focus from active element.") \ No newline at end of file diff --git a/src/utils/chrome_utils.py b/src/utils/chrome_utils.py new file mode 100644 index 00000000..285f9fa6 --- /dev/null +++ b/src/utils/chrome_utils.py @@ -0,0 +1,91 @@ +import os +import time +from selenium import webdriver +from selenium.webdriver.chrome.service import Service as ChromeService +from selenium.webdriver.chrome.options import Options +from webdriver_manager.chrome import ChromeDriverManager # Import webdriver_manager +import urllib +from src.logging import logger + +def chrome_browser_options(): + logger.debug("Setting Chrome browser options") + options = Options() + options.add_argument("--start-maximized") + options.add_argument("--no-sandbox") + options.add_argument("--disable-dev-shm-usage") + options.add_argument("--ignore-certificate-errors") + options.add_argument("--disable-extensions") + options.add_argument("--disable-gpu") + options.add_argument("window-size=1200x800") + options.add_argument("--disable-background-timer-throttling") + options.add_argument("--disable-backgrounding-occluded-windows") + options.add_argument("--disable-translate") + options.add_argument("--disable-popup-blocking") + options.add_argument("--no-first-run") + options.add_argument("--no-default-browser-check") + options.add_argument("--disable-logging") + options.add_argument("--disable-autofill") + options.add_argument("--disable-plugins") + options.add_argument("--disable-animations") + options.add_argument("--disable-cache") + options.add_argument("--incognito") + logger.debug("Using Chrome in incognito mode") + + return options + +def init_browser() -> webdriver.Chrome: + try: + options = chrome_browser_options() + # Use webdriver_manager to handle ChromeDriver + driver = webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options) + logger.debug("Chrome browser initialized successfully.") + return driver + except Exception as e: + logger.error(f"Failed to initialize browser: {str(e)}") + raise RuntimeError(f"Failed to initialize browser: {str(e)}") + + + +def HTML_to_PDF(html_content, driver): + """ + Converte una stringa HTML in un PDF e restituisce il PDF come stringa base64. + + :param html_content: Stringa contenente il codice HTML da convertire. + :param driver: Istanza del WebDriver di Selenium. + :return: Stringa base64 del PDF generato. + :raises ValueError: Se l'input HTML non è una stringa valida. + :raises RuntimeError: Se si verifica un'eccezione nel WebDriver. + """ + # Validazione del contenuto HTML + if not isinstance(html_content, str) or not html_content.strip(): + raise ValueError("Il contenuto HTML deve essere una stringa non vuota.") + + # Codifica l'HTML in un URL di tipo data + encoded_html = urllib.parse.quote(html_content) + data_url = f"data:text/html;charset=utf-8,{encoded_html}" + + try: + driver.get(data_url) + # Attendi che la pagina si carichi completamente + time.sleep(2) # Potrebbe essere necessario aumentare questo tempo per HTML complessi + + # Esegue il comando CDP per stampare la pagina in PDF + pdf_base64 = driver.execute_cdp_cmd("Page.printToPDF", { + "printBackground": True, # Includi lo sfondo nella stampa + "landscape": False, # Stampa in verticale (False per ritratto) + "paperWidth": 8.27, # Larghezza del foglio in pollici (A4) + "paperHeight": 11.69, # Altezza del foglio in pollici (A4) + "marginTop": 0.8, # Margine superiore in pollici (circa 2 cm) + "marginBottom": 0.8, # Margine inferiore in pollici (circa 2 cm) + "marginLeft": 0.5, # Margine sinistro in pollici (circa 1.27 cm) + "marginRight": 0.5, # Margine destro in pollici (circa 1.27 cm) + "displayHeaderFooter": False, # Non visualizzare intestazioni e piè di pagina + "preferCSSPageSize": True, # Preferire le dimensioni della pagina CSS + "generateDocumentOutline": False, # Non generare un sommario del documento + "generateTaggedPDF": False, # Non generare PDF taggato + "transferMode": "ReturnAsBase64" # Restituire il PDF come stringa base64 + }) + return pdf_base64['data'] + except Exception as e: + logger.error(f"Si è verificata un'eccezione WebDriver: {e}") + raise RuntimeError(f"Si è verificata un'eccezione WebDriver: {e}") diff --git a/src/utils/constants.py b/src/utils/constants.py new file mode 100644 index 00000000..54f34214 --- /dev/null +++ b/src/utils/constants.py @@ -0,0 +1,77 @@ +DATE_ALL_TIME = "all_time" +DATE_MONTH = "month" +DATE_WEEK = "week" +DATE_24_HOURS = "24_hours" + + +# constants used in application +SECRETS_YAML = "secrets.yaml" +WORK_PREFERENCES_YAML = "work_preferences.yaml" +PLAIN_TEXT_RESUME_YAML = "plain_text_resume.yaml" + + +# String constants used in the application +DEBUG = "DEBUG" +INFO = "INFO" +WARNING = "WARNING" +ERROR = "ERROR" +CRITICAL = "CRITICAL" + +MINIMUM_LOG_LEVEL = "MINIMUM_LOG_LEVEL" + +# Constants in llm_manager.py +USAGE_METADATA = "usage_metadata" +OUTPUT_TOKENS = "output_tokens" +INPUT_TOKENS = "input_tokens" +TOTAL_TOKENS = "total_tokens" +TOKEN_USAGE = "token_usage" + +MODEL = "model" +TIME = "time" +PROMPTS = "prompts" +REPLIES = "replies" +CONTENT = "content" +TOTAL_COST = "total_cost" + +RESPONSE_METADATA = "response_metadata" +MODEL_NAME = "model_name" +SYSTEM_FINGERPRINT = "system_fingerprint" +FINISH_REASON = "finish_reason" +LOGPROBS = "logprobs" +ID = "id" +TEXT = "text" +PHRASE = "phrase" +QUESTION = "question" +OPTIONS = "options" +RESUME = "resume" +RESUME_SECTION = "resume_section" +JOB_DESCRIPTION = "job_description" +COMPANY = "company" +JOB_APPLICATION_PROFILE = "job_application_profile" +RESUME_EDUCATIONS = "resume_educations" +RESUME_JOBS = "resume_jobs" +RESUME_PROJECTS = "resume_projects" + +PERSONAL_INFORMATION = "personal_information" +SELF_IDENTIFICATION = "self_identification" +LEGAL_AUTHORIZATION = "legal_authorization" +WORK_PREFERENCES = "work_preferences" +EDUCATION_DETAILS = "education_details" +EXPERIENCE_DETAILS = "experience_details" +PROJECTS = "projects" +AVAILABILITY = "availability" +SALARY_EXPECTATIONS = "salary_expectations" +CERTIFICATIONS = "certifications" +LANGUAGES = "languages" +INTERESTS = "interests" +COVER_LETTER = "cover_letter" + +LLM_MODEL_TYPE = "llm_model_type" +LLM_API_URL = "llm_api_url" +LLM_MODEL = "llm_model" +OPENAI = "openai" +CLAUDE = "claude" +OLLAMA = "ollama" +GEMINI = "gemini" +HUGGINGFACE = "huggingface" +PERPLEXITY = "perplexity"