https://github.com/jalandhar04/medichat
An end-to-end medical chatbot powered by Generative AI, providing safe and accurate health information with source verification.
https://github.com/jalandhar04/medichat
ai-chatbot generative-ai generative-model huggingface langchain python-aiml-chatbot
Last synced: about 1 month ago
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An end-to-end medical chatbot powered by Generative AI, providing safe and accurate health information with source verification.
- Host: GitHub
- URL: https://github.com/jalandhar04/medichat
- Owner: jalandhar04
- License: mit
- Created: 2025-04-26T18:31:04.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-04-29T16:17:31.000Z (about 1 month ago)
- Last Synced: 2025-05-01T12:02:18.358Z (about 1 month ago)
- Topics: ai-chatbot, generative-ai, generative-model, huggingface, langchain, python-aiml-chatbot
- Language: Jupyter Notebook
- Homepage:
- Size: 131 MB
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# MediChat :hospital:
[](https://github.com/jalandhar04/MediChat/actions)
An end-to-end medical chatbot powered by Generative AI. Ask health-related questions and get informed, accurate answers in real-time.
---
## Table of Contents
- [Features](#features)
- [Tech Stack](#tech-stack)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Clone Repository](#clone-repository)
- [Create & Activate Conda Environment](#create--activate-conda-environment)
- [Install Dependencies](#install-dependencies)
- [Configure Environment Variables](#configure-environment-variables)
- [Usage](#usage)
- [Indexing Documents](#indexing-documents)
- [Running the App](#running-the-app)
- [AWS CI/CD Deployment](#aws-cicd-deployment)
- [1. AWS Setup](#1-aws-setup)
- [2. ECR & Docker](#2-ecr--docker)
- [3. EC2 Self-Hosted Runner](#3-ec2-self-hosted-runner)
- [4. GitHub Secrets](#4-github-secrets)
- [Contributing](#contributing)
- [License](#license)
---
## Features
- :brain: Medical AI Assistant with **Gemini 2.0 Flash** LLM
- :mag_right: Semantic search using **HuggingFace** embeddings & **Pinecone** vector DB
- :gear: Built with **Python 3.12**, **Flask**, and **LangChain**
- :art: Modern glassmorphism UI with real-time interactions
- :rocket: Automated AWS deployment via GitHub Actions CI/CD
- :warning: Built-in safety checks for emergency responses
## Tech Stack
- **Language & Framework**: Python 3.12, Flask
- **AI & LLM**: LangChain, Google Gemini 2.0 Flash
- **Embeddings**: HuggingFace Transformers (`all-MiniLM-L6-v2`)
- **Vector DB**: Pinecone
- **Frontend**: HTML5, Bootstrap 5, jQuery, CSS Glassmorphism
- **CI/CD & Hosting**: Docker, AWS ECR, EC2, GitHub Actions
| Category | Technologies |
|---------------------|---------------------------------------|
| Backend | Python 3.12, Flask, LangChain |
| AI/LLM | Google Gemini 2.0 Flash, HuggingFace |
| Vector Database | Pinecone |
| Frontend | Bootstrap 5, CSS Glassmorphism |
| Infrastructure | Docker, AWS ECR/EC2, GitHub Actions |
## Prerequisites
- [Conda](https://docs.conda.io/en/latest/) installed
- :snake: Python 3.12+
- :whale: Docker 20.10+
- :key: API Keys:
- Google Gemini API Key
- Pinecone API Key
- AWS IAM User with EC2/ECR access
## Installation
### Clone Repository
```bash
git clone https://github.com/jalandhar04/MediChat.git
cd MediChat
```
### Create & Activate Conda Environment
```bash
conda create -n medichat python=3.12 -y
conda activate medichat
```
### Install Dependencies
```bash
pip install -r requirements.txt
```
### Configure Environment Variables
Create a `.env` file in the project root and add:
```ini
PINECONE_API_KEY="YOUR_PINECONE_API_KEY"
GEMINI_API_KEY="YOUR_GOOGLE_API_KEY"
```
> **Note**: Keep your keys secret. Do not commit `.env` to version control.
## Usage
### Indexing Documents
Generate and upload embeddings for your documents:
```bash
python store_index.py
```
### Running the App
```bash
python app.py
```
Open your browser at `http://localhost:8080` to start chatting.
## AWS CI/CD Deployment
Automate build & deployment via GitHub Actions.
### 1. AWS Setup
1. **IAM User**: Create with policies:
- `AmazonEC2FullAccess`
- `AmazonEC2ContainerRegistryFullAccess`
2. **ECR Repository**:
```text
970547337635.dkr.ecr.ap-south-1.amazonaws.com/medicalchatbot
```
### 2. ECR & Docker
1. Build Docker image:
```bash
docker build -t medicalchatbot .
```
2. Tag & push to ECR:
```bash
aws ecr get-login-password --region ap-south-1 | docker login --username AWS --password-stdin 970547337635.dkr.ecr.ap-south-1.amazonaws.com
docker tag medicalchatbot:latest 970547337635.dkr.ecr.ap-south-1.amazonaws.com/medicalchatbot:latest
docker push 970547337635.dkr.ecr.ap-south-1.amazonaws.com/medicalchatbot:latest
```
### 3. EC2 Self-Hosted Runner
1. Launch Ubuntu EC2 instance
2. Install Docker:
```bash
sudo apt-get update -y curl -fsSL [https://get.docker.com](https://get.docker.com) -o get-docker.sh sudo sh get-docker.sh sudo usermod -aG docker \$USER newgrp docker
```
3. Configure as GitHub Actions runner (Settings → Actions → Runners)
### 4. GitHub Secrets
In your repo settings, add:
- `AWS_ACCESS_KEY_ID`
- `AWS_SECRET_ACCESS_KEY`
- `AWS_DEFAULT_REGION` (e.g. `ap-south-1`)
- `ECR_REPO` (e.g. `970547337635.dkr.ecr.ap-south-1.amazonaws.com/medicalchatbot`)
- `PINECONE_API_KEY`
- `GEMINI_API_KEY`
## Contributing
1. Fork the repo
2. Create a feature branch (`git checkout -b feature/YourFeature`)
3. Commit your changes (`git commit -m 'Add feature'`)
4. Push and open a Pull Request
## License
MIT © [https://jalandharpaswan.com](https://jalandharpaswan.com)