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DocTalk-Chat_With_PDF
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---
title: DocTalk - Chat With PDF
emoji:  📗💬
colorFrom: indigo
colorTo: pink
sdk: streamlit
sdk_version: "1.35.0"
app_file: app.py
pinned: false
---

# 📗💬 DocTalk - Chat With PDF

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.

## ✨ 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>