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---
title: Medical Chatbot API
emoji: 🏥
colorFrom: blue
colorTo: green
sdk: docker
sdk_version: "1.0"
app_file: app/main.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
# Medical Chatbot API
A FastAPI-based medical chatbot API powered by LangChain and custom models.
## Configuration
### Environment Variables
Create a `.env` file with the following variables:
```env
QDRANT_URL=your_qdrant_url
QDRANT_API_KEY=your_qdrant_api_key
HF_TOKEN=your_huggingface_token
```
### Model Configuration
The application expects the following model structure:
```
models/
├── embeddings/
└── llm/
```
### Dependencies
Key dependencies include:
- LangChain ecosystem
- Qdrant for vector storage
- Unsloth for optimized model loading
- FastAPI for the web framework
## Development Setup
1. Install dependencies:
```bash
pip install -r requirements.txt
```
2. Run the development server:
```bash
uvicorn app.main:app --reload --host 0.0.0.0 --port 7860
```
## Docker Deployment
Build and run with Docker:
```bash
docker build -t medical-chatbot .
docker run -p 7860:7860 --env-file .env medical-chatbot
```
## API Endpoints
- `GET /health`: Health check endpoint
- `POST /chat`: Chat endpoint
```json
{
"question": "What are the symptoms of diabetes?",
"context": "Optional medical context"
}
```
## Production Deployment
For Hugging Face Spaces:
1. Set repository secrets in Space settings
2. Deploy using the provided Dockerfile
3. Ensure model weights are properly configured |