Medibot: A Streamlit-Powered Medical Assistant Chatbot

Overview

Medibot is a medical assistant chatbot built using Streamlit, LangChain, and Hugging Face models. The chatbot leverages a pre-trained language model to provide accurate answers to medical queries by retrieving relevant information from a local FAISS vector store. The project supports persistent memory, allowing conversations to continue across sessions.

Features

• Medical Assistance: Provides answers to medical queries using a pre-trained language model.
• Persistent Memory: Saves chat history locally to continue conversations across sessions.
• Streamlit Interface: Interactive user interface built with Streamlit.
• Custom Prompt Templates: Uses custom prompt templates to enhance response quality.
• Local Vector Store: Uses FAISS vector store for efficient information retrieval.

Setup Instructions

Prerequisites

• Python 3.9 or higher
• Anaconda (recommended for managing environments)
• Hugging Face API token

Installation

1. Clone the Repository:

   git clone <repository_url>
   cd <repository_directory>

2. Create a Conda Environment: conda create --name medibot python=3.11 conda activate medibot
3. Install Required Packages: pip install -r requirements.txt
4. Configuration Create a .env File:

Add your Hugging Face API token to the .env file: HF_TOKEN=your_hugging_face_api_token

1. Usage Load Data and Build Vector Store:

Run the memory_.py script to load PDF documents, create text chunks, and store embeddings in the FAISS vector store: python memory_.py

2.Run the Chatbot:

Launch the Streamlit app: streamlit run medibot.py

Contributing Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License This project is licensed under the MIT License.

Acknowledgements Hugging Face for providing the pre-trained models and API.

LangChain for the retrieval and chaining framework.

Streamlit for the interactive user interface framework.

Explanation

• Overview: Briefly describes what the project does.
• Features: Lists the main features of the project.
• Setup Instructions: Provides step-by-step instructions to set up the project.
• Configuration: Details how to configure the environment variables.
• Usage: Explains how to run the project.
• Contributing: Invites contributions and provides guidance on how to contribute.
• License: Mentions the project's license.
• Acknowledgements: Credits the tools and libraries used in the project.

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