Update README.md

README.md

@@ -1,20 +1,97 @@
 ---
-title: DocTalk-Chat With PDF
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
+title: DocTalk - Chat With PDF
+emoji:  📗💬
+colorFrom: indigo
+colorTo: pink
+sdk: streamlit
+sdk_version: "1.35.0"
+app_file: app.py
 pinned: false
-short_description: ' DocTalk-Chat_With_PDF '
-license: mit
 ---
 
-# Welcome to Streamlit!
+# 📗💬 DocTalk - Chat With PDF
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+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.
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+## ✨ Features
+
+### 🤖 **Core Engine**
+* **Model:** Google Gemma-2-2B-IT (Instruction Tuned)
+* **Architecture:** Runs entirely locally on CPU (no GPU required)
+* **Performance:** Optimized with FAISS for instant vector retrieval
+
+### 🎯 **Key Capabilities**
+* ⚡ **CPU Optimized** - Runs smoothly on Hugging Face Free Tier
+* 📤 **Easy Upload** - Simple sidebar PDF upload
+* 🧠 **Smart Context** - Uses `all-MiniLM-L6-v2` for precise semantic search
+* 💬 **Memory** - Maintains chat history within the session
+* 🔒 **Secure** - Handles Hugging Face tokens via environment secrets
+
+## 🚀 How to Use
+
+### 1. Set Up Authentication
+* This app requires a **Hugging Face Access Token** (Read permissions) to download the Gemma model.
+* **For Users:** Enter your token in the app sidebar if prompted (or set it in Space secrets).
+
+### 2. Upload Your PDF
+* Navigate to the sidebar
+* Click "Browse files" to upload your PDF document
+* Click **"🚀 Process Document"**
+
+### 3. Start Chatting!
+* Wait for the "✅ Ready to chat!" notification
+* Type your question in the chat input at the bottom
+* Receive concise, context-aware answers from Gemma-2
+
+## 🛠️ Technical Stack
+
+* **Frontend**: Streamlit
+* **LLM**: google/gemma-2-2b-it
+* **Embeddings**: sentence-transformers/all-MiniLM-L6-v2
+* **Vector Store**: FAISS (Facebook AI Similarity Search)
+* **PDF Processing**: PyPDFLoader
+* **Orchestration**: LangChain
+
+## 📦 Installation (Local)
+
+To run this app on your own machine:
+
+https://huggingface.co/spaces/ChiragKaushikCK/Chat_with_PDF
+
+**🌟 Features Breakdown**
+FAISS Vector Search
+Replaces heavy database lookups with lightweight, in-memory similarity search.
+
+Ensures responses are strictly grounded in your uploaded document.
+
+Pre-loaded Models
+The embedding models are cached (@st.cache_resource) to ensure the app feels snappy after the initial cold start.
+
+Gemma-2-2B-IT
+Google's latest lightweight open model.
+
+Instruction-tuned for better Q&A performance compared to base models.
+
+Small enough (~2.6B params) to fit in standard RAM.
+
+**⚠️ Limitations**
+Speed: Since this runs on CPU, generating long answers may take a few seconds.
+
+Memory: Designed for standard PDFs. Extremely large files (500+ pages) might hit RAM limits on free tiers.
+
+Session: Chat history is cleared if the page is refreshed.
+
+🤝 Contributing
+Contributions are welcome! Please feel free to submit issues or pull requests to improve the UI or add new features.
+
+📄 License
+MIT License
+
+🔗 Links
+Google Gemma Models
+
+LangChain Documentation
+
+Streamlit
+
+<div align="center"> Made with ❤️ with Streamlit and Gemma model, by Tannu Yadav </div>