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| title: MedChatBot | |
| emoji: 💊 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: docker | |
| pinned: false | |
| # MedChatBot | |
| A medical chatbot application that uses RAG (Retrieval-Augmented Generation) architecture to answer medical questions based on medical literature. The system combines **Google Gemini 2.5 Pro** as the language model with **Pinecone** vector database for efficient document retrieval. | |
| ## Technology Stack | |
| - **Backend**: Flask | |
| - **Language Model**: Google Gemini 2.5 Pro | |
| - **Vector Database**: Pinecone | |
| - **Embeddings**: HuggingFace sentence-transformers (all-MiniLM-L6-v2) | |
| - **Document Processing**: LangChain, PyPDF | |
| - **Frontend**: HTML/CSS/JavaScript | |
| ## Installation & Setup | |
| ### Step 1: Clone the Repository | |
| ```bash | |
| git clone https://github.com/TMTien31/MedChatBot.git | |
| cd MedChatBot | |
| ``` | |
| ### Step 2: Create Virtual Environment | |
| ```bash | |
| # Create virtual environment | |
| python -m venv venv | |
| # Activate virtual environment | |
| # On Windows: | |
| venv\Scripts\activate | |
| # On macOS/Linux: | |
| source venv/bin/activate | |
| ``` | |
| ### Step 3: Install Dependencies | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### Step 4: Get API Keys | |
| #### Google Gemini API Key: | |
| 1. Go to [Google AI Studio](https://makersuite.google.com/app/apikey) | |
| 2. Create a new API key | |
| 3. Copy the generated key | |
| #### Pinecone API Key: | |
| 1. Sign up at [Pinecone](https://www.pinecone.io/) | |
| 2. Go to your dashboard | |
| 3. Copy your API key from the "API Keys" section | |
| ### Step 5: Create Environment File | |
| Create a `.env` file in the project root directory: | |
| ```bash | |
| # Create .env file | |
| touch .env # On macOS/Linux | |
| # or create manually on Windows | |
| ``` | |
| Add your API keys to the `.env` file: | |
| ```env | |
| PINECONE_API_KEY=your_pinecone_api_key_here | |
| GEMINI_API_KEY=your_gemini_api_key_here | |
| ``` | |
| ### Step 6: Prepare Medical Documents | |
| - Place your PDF medical documents in the `Data/` folder | |
| - The project includes "Gale Encyclopedia of Medicine Vol. 1 (A-B).pdf" by default | |
| - You can add more medical PDFs to expand the knowledge base | |
| ### Step 7: Create Vector Index (Run Once) | |
| **Important**: This step only needs to be run once initially, or whenever you add new documents to the `Data/` folder. | |
| ```bash | |
| python store_index.py | |
| ``` | |
| This script will: | |
| - Read all PDF files from the `Data/` directory | |
| - Split text into 500-character chunks with 20-character overlap | |
| - Generate embeddings using sentence-transformers | |
| - Create and populate a Pinecone index named "medchatbot" | |
| **Note**: This process may take several minutes depending on the size of your documents. | |
| ## Running the Application | |
| ### Start the Flask Server | |
| ```bash | |
| python app.py | |
| ``` | |
| ### Access the Application | |
| 1. Open your web browser | |
| 2. Navigate to: `http://0.0.0.0:8080` or `http://localhost:8080` | |
| 3. You should see the medical chatbot interface | |