AI-Powered Personalized Learning Assistant

Elevate your learning experience with our cutting-edge AI-powered assistant that transforms the way you interact with educational content. Leveraging the advanced capabilities of Google's Gemini Pro LLM, this application offers a suite of features designed to enhance comprehension, retention, and engagement with your study materials.

🚀 Features

• Intelligent Document Processing: Easily upload and analyze multiple PDF documents.
• Interactive Q&A: Have dynamic conversations about your content.
• Adaptive Quiz Generation: Automatically generate quizzes to test your knowledge.(Still glitchy, working on fixes!!)
• Smart Summarization: Receive concise overviews of complex documents.
• Flashcard Creation: Create study aids for efficient revision.
• Multilingual Support: Translate content into various languages.
• Document Sharing: Share processed documents via email.
• Text Complexity Analysis: Understand the readability of your materials.
• Key Concept Extraction: Quickly identify crucial ideas.
• Audio Learning: Convert text to speech for on-the-go studying. (Coming Soon)
• Progress Tracking: Keep a comprehensive chat history of your learning journey.

🛠️ Technology Stack

• Core: Python 3.11
• Framework: Streamlit
• AI Model: Google Gemini Pro
• NLP & ML: LangChain, Transformers, Scikit-learn
• Data Processing: PyPDF2, FAISS
• Visualization: Matplotlib, Seaborn
• Audio: gTTS (Google Text-to-Speech)

🚀 Getting Started

1. Clone & Setup:

   For Mac/Linux:

   git clone https://github.com/anubhab-m02/PDF-QnA.git
   cd PDF-QnA
   python -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

   For Windows:

   git clone https://github.com/anubhab-m02/PDF-QnA.git
   cd PDF-QnA
   python -m venv venv
   venv\Scripts\activate
   pip install -r requirements.txt

2. API Configuration:

   • Obtain a Google Cloud API key with Gemini Pro access.
   • Create a .env file in the project root:

     GOOGLE_API_KEY=your_api_key_here
     

3. Launch:

   streamlit run app.py

💡 Usage Guide

1. Document Upload: Use the sidebar to upload your PDF documents.
2. Processing: Click "Process Documents" to analyze your materials.
3. Feature Selection: Choose from a variety of learning tools in the main interface.
4. Interaction: Engage with the AI assistant through your chosen feature.

🔐 Security & Performance

• Safe Deserialization: Exercise caution with allow_dangerous_deserialization=True. Only use with trusted FAISS index sources.
• Efficient Indexing: The FAISS index updates incrementally, preserving knowledge from all uploaded documents.
• Automatic Cleanup: Old data and large caches are periodically removed to maintain performance.

🤝 Contributing

Contributions are welcome! Whether it's feature suggestions, bug reports, or code improvements, please feel free to open an issue or submit a pull request.