Initial MedGemma Space setup

README.md

@@ -12,3 +12,12 @@ short_description: used for Medical image analysis
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# MedGemma Space
+
+A Hugging Face Space running the Google MedGemma 4B model for medical image analysis.
+
+## Usage
+
+Upload a medical image in the UI to receive AI-generated findings.  
+For API access, use the Gradio Python client or call the `/predict` endpoint.

app.py

@@ -0,0 +1,47 @@
+import os
+from PIL import Image
+import gradio as gr
+import torch
+from transformers import pipeline
+from huggingface_hub import login
+
+MODEL_ID = "google/medgemma-4b-pt"
+
+# If model is gated, add token as secret in HF Space settings
+hf_token = os.environ.get("HUGGING_FACE_HUB_TOKEN")
+if hf_token:
+    login(token=hf_token)
+
+def load_model():
+    device = 0 if torch.cuda.is_available() else -1
+    try:
+        return pipeline(
+            "image-text-to-text",
+            model=MODEL_ID,
+            device=device,
+            torch_dtype=torch.bfloat16
+        )
+    except:
+        return pipeline("image-text-to-text", model=MODEL_ID, device=device)
+
+pipe = load_model()
+
+def analyze_image(image: Image.Image):
+    if pipe is None:
+        return "Model failed to load."
+    try:
+        result = pipe(images=image, text="<start_of_image> findings:", max_new_tokens=200)
+        return result[0].get("generated_text", str(result))
+    except Exception as e:
+        return f"Error: {e}"
+
+demo = gr.Interface(
+    fn=analyze_image,
+    inputs=gr.Image(type="pil", label="Upload image"),
+    outputs=gr.Textbox(label="AI Findings"),
+    title="MedGemma Image Analyzer",
+    description="Upload a medical image and get AI-generated findings."
+)
+
+if __name__ == "__main__":
+    demo.launch()