Resume Analyzer

A Django-based web application that analyzes resumes using AI and allows users to chat with their resume data.

Features

• Upload and process PDF resumes
• Extract text from PDF files
• Create embeddings and store in Pinecone vector database
• Chat interface to ask questions about the resume
• Configurable AI model parameters (model, temperature, max tokens)
• Chat history persistence
• Responsive design with Bootstrap

Tech Stack

• Backend: Django 4.2
• Frontend: HTML, CSS, JavaScript, Bootstrap 5
• Database: SQLite (development), PostgreSQL (production)
• AI/ML: LangChain, Groq, Pinecone, HuggingFace
• Deployment: Vercel

Setup Instructions

Prerequisites

• Python 3.9+
• Pinecone API key
• Groq API key

Installation

1. Clone the repository:

   git clone <repository-url>
   cd resume-analyzer

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file in the project root with the following variables:

   SECRET_KEY=your-secret-key
   DEBUG=True
   ALLOWED_HOSTS=localhost,127.0.0.1
   GROQ_API_KEY=your-groq-api-key
   PINECONE_API_KEY=your-pinecone-api-key
   INDEX_NAME=your-pinecone-index-name
   

5. Run migrations:

   python manage.py makemigrations
   python manage.py migrate

6. Create a superuser (optional):

   python manage.py createsuperuser

7. Run the development server:

   python manage.py runserver

8. Access the application at http://127.0.0.1:8000/

Deployment to Vercel

1. Install Vercel CLI:

   npm install -g vercel

2. Login to Vercel:

   vercel login

3. Deploy the application:

   vercel

4. Set environment variables in the Vercel dashboard.

Usage

1. Upload a resume in PDF format
2. Wait for the processing to complete
3. Start asking questions about the resume
4. Adjust model parameters as needed for different types of responses

Development Notes

Version Control

The project includes a comprehensive .gitignore file that excludes:

• Virtual environment files
• Database files (SQLite)
• Media files (uploaded resumes)
• Compiled Python files
• Environment variable files (.env)
• Cache directories
• Log files
• Static files collected for production

This ensures that only the necessary code is committed to the repository, keeping it clean and secure.

License

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