This repository contains the backend implementation for a Resume Analysis application. It provides functionalities for user authentication, PDF extraction, LLM-based data processing, and data retrieval.

https://resume-analysis-app-ba3q.onrender.com

https://github.com/addy0328p/resume-analysis-app/tree/main

• User Authentication: Secure user authentication through the /auth/login route.
• PDF Extraction and Processing: Extracts text from PDF URLs, processes it using Google Gemini, and stores structured information in MongoDB via the /auth/login/extract-pdf route.
• Data Retrieval: Retrieves matching records from the database in JSON format through the /resumes/search route.
• MongoDB Integration: Utilizes MongoDB for efficient data storage.
• Google Gemini Integration: Leverages Google Gemini for advanced data processing and analysis.
• JWT Validation: Implements JWT validation for secure access to protected routes.
• Deployed on Render: The backend is deployed and accessible via Render.

• POST /auth/login: Handles user authentication.
• POST /auth/login/extract-pdf: Validates JWT, extracts text from a PDF URL, processes data using LLM, and stores structured information in MongoDB.
• GET /resumes/search: Returns a list of matching records in JSON format with status code 200.

• Node.js
• Express.js (Specify which one you used)
• MongoDB
• Google Gemini
• JSON Web Tokens (JWT)
• Render

• Developed a mini backend using Node.js with Express.js/Nest.js.
• Integrated MongoDB as the database for storing user and file-related data.
• Implemented authentication in the /auth/login route.
• Built a PDF extraction feature in /auth/login/extract-pdf that validates JWT, extracts text, and processes it with LLM.
• Integrated Google Gemini for advanced data processing.
• Deployed the backend using Render for accessibility.
• Ensured proper API structuring and optimized response handling.
• Followed best practices for security and efficiency in backend development.

1. Clone the repository:

   git clone [Add your GitHub repository link here]

2. Install dependencies:

   npm install
   # or
   yarn install

3. Configure environment variables:
   • Create a .env file in the root directory.
   • Add necessary environment variables, such as MongoDB connection string, JWT secret, and Google Gemini API key.
   • Example:

     MONGODB_URI=your_mongodb_connection_string
     JWT_SECRET=your_jwt_secret
     GEMINI_API_KEY=your_gemini_api_key
     

4. Run the application:

   npm start
   # or
   yarn start

• Login:

  POST /auth/login
  Content-Type: application/json
  {
    "username": "your_username",
    "password": "your_password"
  }

• Extract PDF:

  POST /auth/login/extract-pdf
  Authorization: Bearer your_jwt_token
  Content-Type: application/json
  {
    "pdfUrl": "your_pdf_url"
  }

• Search Resumes:

  GET /resumes/search
  Authorization: Bearer your_jwt_token

Contributions are welcome! Please feel free to submit a pull request.