README.md

👀 AllCR App

AllCR App is a Streamlit application that allows users to capture real-life objects like recipes, documents, animals, vehicles, and more, and turn them into searchable documents. The app integrates with OpenAI's GPT-4 for OCR (Optical Character Recognition) to JSON conversion and MongoDB Atlas for storing the extracted information.

Features

• Authentication: Secure access to the application using an API code.
• Image Capture: Capture images using your device's camera.
• OCR to JSON: Convert captured images to JSON format using OpenAI's GPT-4.
• MongoDB Integration: Store and retrieve the extracted information from MongoDB.
• Search and Display: Search and display stored documents along with their images.
• Chat with AI: Open the sidebar to chat with GPT on the context captured by the app.

Requirements

• Python 3.8+
• Streamlit
• OpenAI Python Client Library
• MongoDB Atlas cluster

Once the cluster is deployed perform the following tasks:

1. Create a database named 'ocr_db' with collection 'api_keys' :

use ocr_db
db.api_keys.insertOne({'api_key' : "<YOUR_IMAGINARY_KEY>"});

2. Create a 2 search indexes: 2.1 Vector search index on 'ocr_db.ocr_documents':

{
  "fields": [
    {
      "numDimensions": 1536,
      "path": "embedding",
      "similarity": "cosine",
      "type": "vector"
    },
    {
      "path": "api_key",
      "type": "filter"
    }
  ]
}

2.2 Atlas text Search index on 'ocr_documents':

{
  "mappings": {
    "dynamic": true,
    "fields": {
      "api_key": {
        "type": "string"
      },
      "ocr": {
        "dynamic": true,
        "type": "document"
      }
    }
  }
}

• PIL (Python Imaging Library)
• Haystack (for advanced search functionality)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/allcr-app.git
   cd allcr-app

   Installation

2. Install the required packages:

   pip install -r requirements.txt

3. Set up environment variables:

   Create a .env file in the root directory of your project and add your OpenAI API key, MongoDB URI, and API code for authentication.

   OPENAI_API_KEY=your_openai_api_key
   MONGODB_ATLAS_URI=your_mongodb_atlas_uri

Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Access the app:

   Open your web browser and go to `http://localhost:8501`.

Once prompted input the api_key saved in Atlas under the 'ocr_db.api_keys' collection.

3. Authenticate:

   Enter the API code provided in your .env file to access the application.

4. Capture and Process Images:

   • Select the type of object you want to capture.
   • Use the camera to take a picture of the object.
   • The image will be processed, and the extracted text will be displayed for confirmation.
   • Save the processed document to MongoDB.

5. Search and Display Documents:

   • Use the search functionality to find stored documents.
   • Expand the results to view the extracted text and display the associated image.

Code Overview

• app.py: Main application script that contains the Streamlit app logic.
• requirements.txt: List of required Python packages.

Key Functions

• auth_form(): Handles user authentication using an API code.
• transform_image_to_text(image): Transforms a captured image to text using OpenAI's GPT-4.
• save_image_to_mongodb(image, description): Saves the captured image and extracted text to MongoDB.
• searc_aggregation(query), vector_search_aggregation(query, limit): Searches and displays images from MongoDB based on the query/term.
• get_ai_task(doc): Performs an AI generation subtask with the inputed document as context. chat_ai() : Use GPT to form a streaming chat with the vector query context on the application "sidebar".

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or suggestions, please contact Pavel.