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metadata

title: MediVision — Dermatology & Wound Care AI Assistant
emoji: 🩺
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 5.29.0
app_file: app.py
pinned: true
license: mit

MediVision — Dermatology & Wound Care AI Assistant

AMD Developer Hackathon 2026 · Track 3: Vision & Multimodal AI

MediVision is a multilingual AI assistant specialized in dermatology and wound care — it analyzes skin condition images combined with patient symptom descriptions. It supports English, Tiếng Việt, 中文, Español, Français, and 日本語 — every output (diagnosis, severity, actions, disclaimer) is delivered natively in the selected language. Inference is served by a vLLM server running on AMD Developer Cloud (AMD Instinct™ MI300X + ROCm), and the lightweight Gradio frontend on Hugging Face Spaces simply calls that API — no model weights are loaded in the Space itself.

Live Demo

https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/medivision-ai-agent

Architecture

User Browser
     │
     ▼
Hugging Face Space  (Gradio UI — CPU, no GPU needed)
     │  HTTP  (OpenAI-compatible chat/completions)
     ▼
AMD Developer Cloud VM
└── vLLM server  ←  Qwen/Qwen2.5-VL-7B-Instruct
        running on AMD Instinct™ MI300X + ROCm

The HF Space encodes any uploaded image as base64 and passes it to the vLLM endpoint via the standard OpenAI vision message format. This keeps the Space footprint tiny (no PyTorch, no model download) while all heavy lifting happens on the AMD GPU server.

Features

Multimodal Analysis — Combines skin/wound image + freeform symptom text for richer dermatology diagnosis suggestions.
Multilingual — Full support for 6 languages: English, Tiếng Việt, 中文 (Chinese), Español, Français, and 日本語 (Japanese). All output — diagnosis, severity, recommendations, and disclaimer — is rendered natively in the selected language.
Structured Output — Every analysis returns:
- Diagnosis suggestion
- Severity badge: Low · Medium · High · Urgent
- Actionable recommended steps (clinical-grade language)
- Confidence score with visual progress bar
AMD MI300X Powered — Inference via vLLM on AMD Instinct™ MI300X + ROCm.
HF Space Ready — Minimal dependencies; no GPU required in the Space.

Conditions Analyzed

Condition	Typical Severity
Superficial Abrasions & Cuts	Low
Contact Dermatitis / Allergic Rash	Low – Medium
Atopic Eczema Flare	Medium
Tinea Corporis (Ringworm)	Low
Psoriasis Plaque	Medium
Partial-Thickness Burn (2nd degree)	High
Suspected Cellulitis / Skin Infection	Urgent

Tech Stack

Layer	Technology
Vision Model	Qwen/Qwen2.5-VL-7B-Instruct
Inference Runtime	vLLM (OpenAI-compatible API)
Inference Hardware	AMD Instinct™ MI300X (192 GB HBM3) + ROCm
Inference Host	AMD Developer Cloud
Frontend	Gradio 5.29 (Hugging Face Space — CPU)
Language Support	English · Tiếng Việt · 中文 · Español · Français · 日本語

Project Structure

medivision-ai-agent/
├── app.py                   # HF Space entry point (Gradio UI)
├── requirements.txt         # Minimal dependencies (no PyTorch)
├── .env.example             # Environment variable reference
├── README.md                # This file
├── LICENSE                  # MIT
├── src/
│   ├── __init__.py
│   ├── config.py            # VLLM_API_URL, MODEL_NAME, MOCK_MODE, etc.
│   ├── model_loader.py      # OpenAI client → vLLM (image base64 + text)
│   ├── agent.py             # analyze_image_and_text(), mock data pools
│   └── inference.py         # MediVisionPipeline orchestrator
└── sample_test_images/
    └── ABOUT.md

Running Locally

Prerequisites

Python 3.10+
A running vLLM server with Qwen/Qwen2.5-VL-7B-Instruct
(AMD Developer Cloud or any machine with an AMD/NVIDIA GPU)

1. Clone

git clone https://github.com/your-org/medivision-ai-agent
cd medivision-ai-agent

2. Install dependencies

pip install -r requirements.txt

3. Configure the vLLM endpoint

cp .env.example .env
# Edit .env and set VLLM_API_URL to your AMD VM address

Or export directly:

export VLLM_API_URL=http://<AMD_VM_IP>:8000
export MODEL_NAME=Qwen/Qwen2.5-VL-7B-Instruct

4. Launch

python app.py

The app is available at http://localhost:7860.

Starting the vLLM Server (AMD Developer Cloud)

On your AMD MI300X VM with ROCm installed:

pip install vllm

vllm serve Qwen/Qwen2.5-VL-7B-Instruct \
    --host 0.0.0.0 \
    --port 8000 \
    --dtype float16 \
    --max-model-len 4096

The server exposes an OpenAI-compatible API at http://<VM_IP>:8000/v1.

Environment Variables

Variable	Default	Description
`VLLM_API_URL`	`http://localhost:8000`	Base URL of the vLLM server
`MODEL_NAME`	`Qwen/Qwen2.5-VL-7B-Instruct`	Model ID served by vLLM
`VLLM_API_KEY`	`not-required`	API key (if vLLM auth is enabled)

HF Space secret: set VLLM_API_URL in the Space settings → Repository secrets so the Gradio app can reach your AMD VM.

Disclaimer

MediVision is a demonstration prototype built for the AMD Developer Hackathon 2026. It is intended for educational and informational purposes only. It does not replace professional medical advice, diagnosis, or treatment. Always consult a licensed healthcare professional for any medical concerns.

License

Built with ❤️ on AMD ROCm · AMD Developer Cloud · AMD Developer Hackathon 2026