🤖 Personal Data Assistant Chatbot

Welcome to your own PDF-powered AI Chatbot! This project helps you build a smart, document-aware chatbot using Flask, LangChain, and modern LLMs. Upload a PDF, ask questions, and get instant, context-aware answers—all through a beautiful web interface.

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🌟 Features

• Chat with your PDFs: Upload any PDF and ask questions about its content.
• Modern UI: Clean, responsive frontend with light/dark mode.
• Flexible LLM Backend: Use IBM WatsonX or Hugging Face models.
• Fast & Private: All processing happens locally or in your own cloud.
• Easy Deployment: Run locally or with Docker in minutes.

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📋 Table of Contents

• Getting Started
• How It Works
• Running the Application
• Docker Deployment
• Switching to Hugging Face
• Project Structure
• License

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🚀 Getting Started

Prerequisites

• Python 3.x
• pip and virtualenv
• git

Installation

# Clone the repository
$ git clone https://github.com/Garbii1/personal-data-assistant-chatbot.git
$ cd personal-data-assistant-chatbot/build_chatbot_for_your_data

# Create and activate a virtual environment
$ pip install virtualenv
$ virtualenv my_env
# On Windows:
$ my_env\Scripts\activate
# On macOS/Linux:
$ source my_env/bin/activate

# Install dependencies
$ pip install -r requirements.txt
$ pip install langchain-community

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⚙️ How It Works

🖥️ Frontend

• index.html: Chat interface layout
• static/style.css: Modern, responsive styles (light/dark mode)
• static/script.js: Handles chat, file upload, and UI events

🧠 Backend

• worker.py: Handles PDF processing, embeddings, and LLM queries
• server.py: Flask server, API endpoints, and file handling

Main Flow

1. Upload PDF → Processed & split into chunks
2. Embeddings → Chunks stored in a vector database (Chroma)
3. Ask Questions → LLM retrieves relevant info and answers

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▶️ Running the Application

$ python server.py

Visit http://127.0.0.1:8000 in your browser. Upload a PDF and start chatting!

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🐳 Docker Deployment

# Build the Docker image
$ docker build . -t build_chatbot_for_your_data

# Run the container
$ docker run -p 8000:8000 build_chatbot_for_your_data

Access at http://127.0.0.1:8000.

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✨ Switching to Hugging Face LLM

1. Install extra dependencies:

   pip install langchain==0.1.17 huggingface-hub==0.23.4

2. Get your Hugging Face API key from huggingface.co
3. Edit worker.py:
   • Replace the init_llm() function as shown in the code comments
   • Set your API key and desired model (e.g., mistralai/Mistral-7B-Instruct-v0.3)

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📁 Project Structure

personal-data-assistant-chatbot/
├── build_chatbot_for_your_data/
│   ├── Dockerfile
│   ├── requirements.txt
│   ├── server.py
│   ├── worker.py
│   ├── static/
│   │   ├── script.js
│   │   └── style.css
│   └── templates/
│       └── index.html
├── my_env/ (virtual environment)
└── README.md

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📄 License

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

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Made with ❤️ by Garbii1