# Professor Research & Email API A FastAPI-based web service that combines professor research data scraping with personalized email generation capabilities. ## Features - **Professor Data Scraping**: Find professors based on research interests and requirements - **Email Generation**: Create personalized emails to professors using their research data - **Combined Operations**: Scrape professor data and generate emails in one request - **Interactive Documentation**: Automatic API documentation via FastAPI ## Setup ### Prerequisites - Python 3.7+ - pip (Python package manager) ### Installation 1. **Clone or navigate to the project directory** ```bash cd c:\projects\Summer_Siege ``` 2. **Install dependencies** ```bash pip install -r requirements.txt ``` 3. **Set up environment variables** Create a `.env` file in the root directory with: ``` GENAI_API_KEY=your_gemini_api_key_here ``` ### Running the Server #### Option 1: Using the startup scripts **Windows Command Prompt:** ```bash start_server.bat ``` **Windows PowerShell:** ```powershell .\start_server.ps1 ``` #### Option 2: Direct Python execution ```bash python app.py ``` #### Option 3: Using uvicorn directly ```bash uvicorn app:app --host 0.0.0.0 --port 8000 --reload ``` The server will start on `http://localhost:8000` ## API Endpoints ### 1. Root Endpoint - **GET** `/` - Returns API information and available endpoints ### 2. Professor Data Scraping - **POST** `/scraping` - Scrapes professor data based on research interests **Request Body:** ```json { "prompt": "I am interested in application of AI which puts impact in society, My department is AI and I am looking for professors who are working in this area." } ``` **Response:** ```json { "name": "Professor Name", "email": "[email protected]", "data": [ { "title": "Research Paper Title", "data": "Research details..." } ] } ``` ### 3. Email Generation - **POST** `/email` - Generates personalized emails to professors **Request Body:** ```json { "name": "Krishna Prasad Miyapuram", "email": "[email protected]", "user_prompt": "I am interested in application of AI which puts impact in society", "user_data": "Name: Yuvraj Rathod, Department: AI, Institute: IIT Gandhinagar, Year: second, Interest: use of AI in real world applications", "data": null } ``` **Response:** ```json { "subject": "Email Subject", "body": "Email body content...", "to": "[email protected]" } ``` ### 4. Combined Scrape and Email - **POST** `/scrape-and-email` - Combines scraping and email generation in one request **Query Parameters:** - `scraping_prompt`: Prompt for finding professors - `user_data`: Information about the user - `user_prompt`: Optional custom prompt for email generation **Response:** ```json { "professor_data": { "name": "Professor Name", "email": "[email protected]", "data": [...] }, "email": { "subject": "Email Subject", "body": "Email body...", "to": "[email protected]" } } ``` ### 5. Health Check - **GET** `/health` - Returns server health status ## Interactive Documentation Once the server is running, visit: - **Swagger UI**: `http://localhost:8000/docs` - **ReDoc**: `http://localhost:8000/redoc` ## Testing Run the test script to verify all endpoints: ```bash python test_api.py ``` ## Example Usage ### Using curl **Scrape professor data:** ```bash curl -X POST "http://localhost:8000/scraping" \ -H "Content-Type: application/json" \ -d '{ "prompt": "I am interested in AI applications in healthcare" }' ``` **Generate email:** ```bash curl -X POST "http://localhost:8000/email" \ -H "Content-Type: application/json" \ -d '{ "name": "Dr. Smith", "email": "[email protected]", "user_prompt": "Research collaboration", "user_data": "PhD student in AI" }' ``` ### Using Python requests ```python import requests # Scrape professor data response = requests.post("http://localhost:8000/scraping", json={ "prompt": "Machine learning research opportunities" }) professor_data = response.json() # Generate email response = requests.post("http://localhost:8000/email", json={ "name": professor_data["name"], "email": professor_data["email"], "user_prompt": "Research collaboration opportunity", "user_data": "Graduate student in Computer Science" }) email_data = response.json() ``` ## Project Structure ``` Summer_Siege/ ├── app.py # Main FastAPI application ├── requirements.txt # Python dependencies ├── start_server.bat # Windows batch startup script ├── start_server.ps1 # PowerShell startup script ├── test_api.py # API testing script ├── README.md # This file ├── scraping_agent/ # Professor data scraping module │ ├── main.py │ ├── get_name.py │ ├── get_data.py │ └── ... └── email_agent/ # Email generation module ├── main.py ├── email_content.py ├── data.py └── ... ``` ## Error Handling The API includes comprehensive error handling: - **400**: Bad Request - Invalid input data - **500**: Internal Server Error - Processing errors - Detailed error messages in response body ## Environment Variables Required environment variables: - `GENAI_API_KEY`: Your Gemini API key for AI processing ## Logging The application uses FastAPI's built-in logging. For production deployments, consider adding structured logging. ## Deployment For production deployment: 1. Use a production WSGI server like Gunicorn: ```bash pip install gunicorn gunicorn app:app -w 4 -k uvicorn.workers.UvicornWorker ``` 2. Set up reverse proxy (nginx, Apache) 3. Configure SSL certificates 4. Set up environment-specific configurations ## Contributing 1. Fork the repository 2. Create a feature branch 3. Make your changes 4. Add tests 5. Submit a pull request ## License [Add your license information here] # Email-Drafter-Backend