README.md

metadata

title: XRayVision AI Backend
emoji: 🩻
colorFrom: blue
colorTo: blue
sdk: docker
app_port: 7860
pinned: false

XRayVision AI — Backend

Advanced medical diagnostic API using a Hybrid Multi-Model Ensemble.

Quick Start

# 1. Create virtual environment
python -m venv venv
venv\Scripts\activate  # Windows
# source venv/bin/activate  # Mac/Linux

# 2. Install dependencies
pip install -r requirements.txt

# 3. Copy environment variables
copy .env.example .env
# Edit .env with your Supabase, OpenRouter, and Hugging Face keys

# 4. Run the server
uvicorn app.main:app --reload --port 8000

API Documentation

Once running, visit:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Models

Model	Task	Input	Output
TorchXRayVision DenseNet121	Chest Pathology	224×224 grayscale	18 pathology probabilities
YOLOv8	Fracture Localization	Any size RGB	Positive fracture boxes only
HF fracture classifier	Fracture Screening	224x224 RGB	Image-level fracture/normal probability
Wound-specific HF classifier	Wound Classification	224×224 RGB	Classification labels
OpenRouter GLM 4.5 Air	Agentic Synthesis	Model outputs + notes	Structured diagnostic report

Fracture scans use a safer two-stage workflow:

• YOLO localizes visible fracture boxes when it can.
• The pretrained classifier provides image-level fracture suspicion.
• Detector Not_Fracture boxes are ignored because they do not prove the whole scan is normal.

Database Setup

1. Create a Supabase project at https://supabase.com
2. Run supabase_schema.sql in the SQL Editor
3. Create a storage bucket named xray-images (private)
4. Copy the project URL and keys into .env

Docker Deployment (HF Spaces)

docker build -t xrayvision-backend .
docker run -p 7860:7860 --env-file .env xrayvision-backend