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--- |
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title: DocTalk - Chat With PDF |
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emoji: 📗💬 |
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colorFrom: indigo |
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colorTo: pink |
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sdk: streamlit |
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sdk_version: "1.35.0" |
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app_file: app.py |
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pinned: false |
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--- |
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# 📗💬 DocTalk - Chat With PDF |
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An intelligent, completely free-to-run PDF chat application powered by Google's Gemma-2-2b-it model. Optimized for CPU usage on Hugging Face Spaces. |
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## ✨ Features |
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### 🤖 **Core Engine** |
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* **Model:** Google Gemma-2-2B-IT (Instruction Tuned) |
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* **Architecture:** Runs entirely locally on CPU (no GPU required) |
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* **Performance:** Optimized with FAISS for instant vector retrieval |
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### 🎯 **Key Capabilities** |
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* ⚡ **CPU Optimized** - Runs smoothly on Hugging Face Free Tier |
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* 📤 **Easy Upload** - Simple sidebar PDF upload |
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* 🧠 **Smart Context** - Uses `all-MiniLM-L6-v2` for precise semantic search |
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* 💬 **Memory** - Maintains chat history within the session |
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* 🔒 **Secure** - Handles Hugging Face tokens via environment secrets |
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## 🚀 How to Use |
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### 1. Set Up Authentication |
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* This app requires a **Hugging Face Access Token** (Read permissions) to download the Gemma model. |
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* **For Users:** Enter your token in the app sidebar if prompted (or set it in Space secrets). |
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### 2. Upload Your PDF |
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* Navigate to the sidebar |
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* Click "Browse files" to upload your PDF document |
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* Click **"🚀 Process Document"** |
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### 3. Start Chatting! |
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* Wait for the "✅ Ready to chat!" notification |
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* Type your question in the chat input at the bottom |
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* Receive concise, context-aware answers from Gemma-2 |
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## 🛠️ Technical Stack |
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* **Frontend**: Streamlit |
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* **LLM**: google/gemma-2-2b-it |
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* **Embeddings**: sentence-transformers/all-MiniLM-L6-v2 |
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* **Vector Store**: FAISS (Facebook AI Similarity Search) |
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* **PDF Processing**: PyPDFLoader |
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* **Orchestration**: LangChain |
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## 📦 Installation (Local) |
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To run this app on your own machine: |
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https://huggingface.co/spaces/ChiragKaushikCK/Chat_with_PDF |
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**🌟 Features Breakdown** |
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FAISS Vector Search |
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Replaces heavy database lookups with lightweight, in-memory similarity search. |
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Ensures responses are strictly grounded in your uploaded document. |
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Pre-loaded Models |
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The embedding models are cached (@st.cache_resource) to ensure the app feels snappy after the initial cold start. |
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Gemma-2-2B-IT |
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Google's latest lightweight open model. |
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Instruction-tuned for better Q&A performance compared to base models. |
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Small enough (~2.6B params) to fit in standard RAM. |
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**⚠️ Limitations** |
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Speed: Since this runs on CPU, generating long answers may take a few seconds. |
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Memory: Designed for standard PDFs. Extremely large files (500+ pages) might hit RAM limits on free tiers. |
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Session: Chat history is cleared if the page is refreshed. |
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🤝 Contributing |
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Contributions are welcome! Please feel free to submit issues or pull requests to improve the UI or add new features. |
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📄 License |
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MIT License |
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🔗 Links |
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Google Gemma Models |
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LangChain Documentation |
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Streamlit |
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<div align="center"> Made with ❤️ with Streamlit and Gemma model, by Tannu Yadav </div> |
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