📘 StudyMate – AI Academic Assistant

StudyMate is a Streamlit-based AI assistant that helps you study and research efficiently.
It allows you to upload PDF documents, extract text, split it into chunks, perform semantic search,
and get concise AI-generated answers using OpenAI or Hugging Face models

🚀 Features

• 📥 Upload PDFs – Supports multiple PDFs at once.
• 📄 Text Extraction – Extracts searchable text from PDFs (supports scanned PDFs with future OCR integration).
• ✂ Chunking – Splits text into overlapping chunks for better semantic search.
• 🔎 Semantic Search – Uses Sentence Transformers for embeddings and optional FAISS for fast similarity search.
• 🤖 AI-Powered Answers – Generates concise, context-based answers from:
  • OpenAI Chat models (e.g., gpt-3.5-turbo)
  • Hugging Face Inference API (e.g., Mistral-7B-Instruct)
• 📚 Source References – Always provides document/chunk IDs in the answer

🛠 Installation

1️⃣ Clone the Repository

git clone https://github.com/YOUR_USERNAME/studymate.git
cd studymate

2️⃣ Create a Virtual Environment (Recommended)

python -m venv venv
# Activate it:
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

If you don’t have a requirements.txt yet, create one with:

streamlit
python-dotenv
PyMuPDF
numpy
sentence-transformers
faiss-cpu
openai
requests

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🔑 Environment Variables

Create a .env file in the root of your project:

OPENAI_API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
HUGGINGFACE_API_KEY=hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
OPENAI_MODEL=gpt-3.5-turbo
HUGGINGFACE_MODEL=mistralai/Mistral-7B-Instruct-v0.2

⚠ Important: Never commit your .env file to GitHub. Add it to .gitignore:

.env

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▶ Running the App

Once your .env is set up and dependencies are installed, run:

streamlit run app.py

Open your browser at the URL shown in the terminal (usually http://localhost:8501).

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📂 Project Structure

StudyMate/
│
├── app.py              # Main Streamlit app
├── requirements.txt    # Python dependencies
├── .env                # API keys and config (not tracked in Git)
└── README.md           # Project documentation

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💡 Usage

1. Upload PDFs – Select one or more PDF files.
2. Extract Text – Click "Read text from PDFs".
3. Chunk Text – Adjust chunk size/overlap, then "Create Chunks".
4. Build Index – Click "Build Semantic Index" for embeddings.
5. Ask Questions – Type your question and choose your AI provider.
6. View Answer – See AI-generated answers with source references.

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📦 Optional: Installing FAISS for Faster Search

If FAISS isn’t available, NumPy fallback is used.
To install FAISS CPU version:

pip install faiss-cpu

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📜 License

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

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🙌 Acknowledgements

• Streamlit
• Sentence Transformers
• OpenAI
• Hugging Face
• PyMuPDF