Add Hugging Face Transformers integration and update documentation

* **services/ai_agent.py**
- Add `integrate_hugging_face_transformers` function to integrate Hugging Face Transformers for NLP tasks.

* **tests/test_ai_agent_service.py**
- Add unit test for `integrate_hugging_face_transformers` function.

* **requirements.txt**
- Add `transformers` library.

* **.env.example**
- Add example environment variables including Hugging Face Transformers API key.

* **README.md**
- Add installation and setup instructions.
- Add API endpoints documentation.
- Add environment variables documentation.

.env.example

@@ -0,0 +1,23 @@
+# Environment variables for FoodAnalyzer-API
+
+# Database configuration
+DATABASE_URL=sqlite:///./test.db
+
+# JWT Secret Key
+SECRET_KEY=09d8f7a6b5c4e3d2f1a0b9c8d7e6f5a4
+ALGORITHM=HS256
+ACCESS_TOKEN_EXPIRE_MINUTES=30
+
+# Redis configuration
+REDIS_HOST=localhost
+REDIS_PORT=6379
+REDIS_DB=0
+
+# OCR configuration
+TESSERACT_CMD=/usr/bin/tesseract
+
+# API configuration
+API_BASE_URL=https://api.example.com
+
+# Hugging Face Transformers API key
+HUGGING_FACE_API_KEY=your_api_key_here  # Get your API key from https://huggingface.co

README.md

@@ -1,3 +1,70 @@
-# Module 2
+# FoodAnalyzer-API
 
-## API END POINT TO PROCESS PRODUCT DATA AND RETURN RESPONSE
+## Installation and Setup
+
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/prathameshks/FoodAnalyzer-API.git
+   ```
+
+2. **Navigate to the project directory**:
+   ```bash
+   cd FoodAnalyzer-API
+   ```
+
+3. **Create a virtual environment**:
+   ```bash
+   python -m venv venv
+   ```
+
+4. **Activate the virtual environment**:
+   - On Windows:
+     ```bash
+     venv\Scripts\activate
+     ```
+   - On macOS/Linux:
+     ```bash
+     source venv/bin/activate
+     ```
+
+5. **Install dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+6. **Set up environment variables**:
+   Copy the `.env.example` file to `.env` and fill in the required values, including API keys for Hugging Face Transformers.
+
+7. **Run the application**:
+   ```bash
+   uvicorn main:app --reload
+   ```
+
+## API Endpoints
+
+### Authentication
+- `POST /api/register`: Register a new user.
+- `POST /api/login`: Login and obtain an access token.
+- `GET /api/users/me`: Get the current user's information.
+
+### Ingredient Analysis
+- `POST /api/analyze_ingredients`: Analyze a list of ingredients.
+- `GET /api/personalized_recommendations`: Get personalized ingredient recommendations.
+
+### Scan History
+- `POST /api/scan`: Record a new scan.
+- `GET /api/history/{user_id}`: Retrieve the scan history for a user.
+
+### Product Data
+- `GET /api/extract_product_info`: Extract product information from a barcode.
+- `POST /api/fetch_product_data`: Fetch product data for a list of barcodes.
+
+## Environment Variables
+
+### Hugging Face Transformers API key
+Obtain a free API key from Hugging Face by signing up on their website. Add the API key to the `.env` file with the variable name `HUGGING_FACE_API_KEY`.
+
+Example:
+```env
+HUGGING_FACE_API_KEY=your_api_key_here  # Get your API key from https://huggingface.co
+```

requirements.txt

@@ -7,3 +7,4 @@ opencv-python
 sqlalchemy
 alembic
 redis
+transformers

services/ai_agent.py

@@ -5,6 +5,7 @@ from models.ingredient import Ingredient
 from services.ingredients import get_ingredient_by_name, save_ingredient_data
 from typing import Dict, Any
 import json
+from transformers import pipeline
 
 def preprocess_data(barcode: str) -> Dict[str, Any]:
     data = fetch_product_data_from_api(barcode)
@@ -86,3 +87,8 @@ def process_data(db: Session, barcode: str) -> Dict[str, Any]:
     data = enrich_data(db, data)
     save_json_file(barcode, data)
     return data
+
+def integrate_hugging_face_transformers(model_name: str, text: str) -> str:
+    nlp = pipeline("fill-mask", model=model_name)
+    result = nlp(text)
+    return result[0]['sequence']

tests/test_ai_agent_service.py

@@ -1,7 +1,7 @@
 import unittest
 from unittest.mock import patch, MagicMock
 from sqlalchemy.orm import Session
-from services.ai_agent import preprocess_data, validate_data, clean_data, standardize_data, enrich_data, process_data
+from services.ai_agent import preprocess_data, validate_data, clean_data, standardize_data, enrich_data, process_data, integrate_hugging_face_transformers
 
 class TestAIAgentService(unittest.TestCase):
 
@@ -137,5 +137,11 @@ class TestAIAgentService(unittest.TestCase):
         self.assertEqual(result['product_name'], 'Test Product')
         mock_save_json_file.assert_called_once_with('test_barcode', {'product_name': 'Test Product'})
 
+    @patch('services.ai_agent.pipeline')
+    def test_integrate_hugging_face_transformers(self, mock_pipeline):
+        mock_pipeline.return_value = lambda text: [{'sequence': 'Test sequence'}]
+        result = integrate_hugging_face_transformers('test_model', 'Test text')
+        self.assertEqual(result, 'Test sequence')
+
 if __name__ == '__main__':
     unittest.main()