Image URL Classifier

Overview

This is a simple URL image classifier that uses a ResNet-50 model to classify images. The project is built using Flask for the web framework and provides an easy-to-use interface for uploading and classifying images.

Running the Project

To run the project, follow these steps:

1. Install the required dependencies:

   poetry install

2. Start the Flask application:

   poetry run python app/main.py

3. Open your web browser and navigate to:

   http://127.0.0.1:5000/
   

Why ResNet-50 and Flask?

ResNet-50

• High Accuracy: ResNet-50 achieves high accuracy in image classification tasks, making it a reliable choice.
• Pre-trained Models: Availability of pre-trained models on large datasets like ImageNet makes it easy to use for transfer learning. Note, the pre-trained classes were specified via this link and note these classes may be updated in the future so redownload the file if necessary.

Flask

• Lightweight: Flask is a lightweight web framework that is easy to set up and use.
• Flexibility: It provides the flexibility to structure the project in a way that suits the application's needs.
• Community Support: Flask has a large community and extensive documentation, making it easier to find solutions and support.

Testing

To run the tests for this project, follow these steps:

1. Ensure all dependencies are installed:

   poetry install

2. Run the tests using pytest:

   poetry run pytest

Extensions

Here are some potential extensions for the project:

• Improve UI: Make the pages prettier and add a limit on the number of images before it begins removing them.
• Testing: Add more comprehensive tests to ensure the reliability of the application.
• Image cleanup: Add a feature to remove images after a certain period to free up space.

Design Choices

• File Structure: The project is split into multiple files to enhance maintainability. For example, the main application logic is in app/main.py, while other functionalities are modularised.
• Maintainability: The code is organised in a way that makes it easy to extend and maintain in the long term. This includes using clear naming conventions and separating concerns within the codebase. Additionally, the project uses:
  • Poetry: For dependency management and packaging, which simplifies the process of managing project dependencies and virtual environments.
  • Black: For code formatting to ensure a consistent code style -> poetry run black .
  • isort: For sorting imports to maintain a clean and organised import structure -> poetry run isort .
  • mypy: For static type checking to catch type-related errors early in the development process -> poetry run mypy .

Future steps

• Deployment: Deploy the application to a cloud platform like AWS. Be aware of the static folder being publicly accessible.
• Monitoring: Add monitoring for the application to track performance and usage.
• Security: Enhance security by adding measures like rate limiting.