📚 Paper Analyzer (Mistral-Powered)

This project is a pure Python backend that uses the Mistral AI API to analyze exam questions and extract the most frequently occurring high-level topics. It's perfect for competitive exams, semester question paper analysis, and education-focused AI applications.

Just Load YOur Papers(pdf form) in /papers and run main.py

🚀 Features

• 🔍 Analyze multiple questions in one go
• 🎯 Extract 5–6 most common technical topics
• ⚡ Powered by Mistral API
• 📂 Simple script-based usage (main.py)
• 🛡️ Environment-safe with .env file

🛠️ Installation & Setup

1. Clone the repository

bash git clone https://github.com/mahivram/Paper_Analyzer.git cd paper-analyzer

2. Create a virtual environment

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

3. Install dependencies

bash pip install -r requirements.txt

4. Set up your .env file

Create a .env file in the root directory:

MISTRAL_API_KEY=your_mistral_api_key_here

🔐 Never share or commit your .env file.

▶️ Running the Script

This project is not an API server (yet). You simply run the script to analyze questions from a text file.

Example usage:

bash python main.py

Make sure your important_questions.txt file exists and contains one question per line.

🧾 Dependencies

Install these using pip install -r requirements.txt:

txt requests python-dotenv

Generate requirements.txt if needed:

bash pip freeze > requirements.txt

🛡️ .gitignore (Auto-Ignored Files)

gitignore

Python

pycache/ *.pyc

Virtual Environment

venv/

Environment variables

.env

OS/system

.DS_Store Thumbs.db

🌐 Deployment

This project is currently script-based and not meant for server deployment. However, you can easily turn it into a REST API later using FastAPI or Flask.

Let me know if you want to upgrade it to an API.

🧽 Contributions

Feel free to fork this repo and contribute via pull requests. If you have suggestions for more advanced analysis (e.g., using embeddings or ranking), open an issue!

📄 License

MIT License. Use freely with attribution.

Built with ❤️ using Python + Mistral API