license: cc-by-nc-sa-4.0
language:
- zh
- en
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
- Osilly/Vision-R1-7B
tags:
- medical
SkinGPT-R1-test
SkinGPT-R1 is a dermatology-oriented Vision-Language Model (VLM) fine-tuned on top of Vision-R1 (Qwen2.5-VL-based).
⚠️ Disclaimer
This model is for research and educational use only. It is NOT a substitute for professional medical advice, diagnosis, or treatment.
🛠️ Environment Setup
To ensure compatibility, we strongly recommend creating a fresh Conda environment.
1. Create Conda Environment
Create a new environment named skingpt-r1 with Python 3.10:
conda create -n skingpt-r1 python=3.10 -y
conda activate skingpt-r1
2. Install Dependencies
pip install -r requirements.txt
(Optional) For faster inference on NVIDIA GPUs:
pip install flash-attn --no-build-isolation
🚀 Usage
Quick Start
If you just installed the environment and want to check if it works:
Open demo.py and Change the IMAGE_PATH variable to your image file.
python demo.py
Interactive Chat
To have a multi-turn conversation (e.g., asking follow-up questions about the diagnosis) in your terminal:
python chat.py --image ./test_images/lesion.jpg
FastAPI Backend Deployment
To deploy the model as a backend service (supporting image uploads and session management):
Start the Server
python app.py
API Workflow
Manage sessions via state_id to support multi-user history.
Upload: POST /v1/upload/{state_id} — Uploads an image for the session.
Chat: POST /v1/predict/{state_id} — Sends text (JSON: {"message": "..."}) and gets a response.
Reset: POST /v1/reset/{state_id} — Clears session history and images.
Client Example
import requests
API_URL = "http://localhost:5900"
STATE_ID = "patient_001"
# 1. Upload Image
with open("skin_image.jpg", "rb") as f:
requests.post(f"{API_URL}/v1/upload/{STATE_ID}", files={"file": f})
# 2. Ask for Diagnosis
response = requests.post(
f"{API_URL}/v1/predict/{STATE_ID}",
json={"message": "Please analyze this image."}
)
print("AI:", response.json()["message"])
# 3. Ask Follow-up
response = requests.post(
f"{API_URL}/v1/predict/{STATE_ID}",
json={"message": "What treatment do you recommend?"}
)
print("AI:", response.json()["message"])