Update README.md

README.md

@@ -1,3 +1,106 @@
----
-license: cc-by-nc-sa-4.0
----
+---
+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 dermatological reasoning vision Language model (VLM).
+
+## ⚠️ 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:
+
+```bash
+conda create -n skingpt-r1 python=3.10 -y
+conda activate skingpt-r1
+```
+
+### 2. Install Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### (Optional) For faster inference on NVIDIA GPUs:
+
+```bash
+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.
+
+```bash
+python demo.py
+```
+
+### Interactive Chat
+
+To have a multi-turn conversation (e.g., asking follow-up questions about the diagnosis) in your terminal:
+```bash
+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
+
+```bash
+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
+```python
+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"])
+```