Upload 2 files

medgemma_space.py

@@ -0,0 +1,182 @@
+# app.py - Main Gradio application
+import gradio as gr
+import torch
+from transformers import AutoProcessor, AutoModelForImageTextToText
+from PIL import Image
+import os
+
+# Model configuration
+MODEL_ID = "google/medgemma-4b-it"
+
+# Load model and processor
+@gr.utils.async_wrapper
+def load_model():
+    model = AutoModelForImageTextToText.from_pretrained(
+        MODEL_ID,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    processor = AutoProcessor.from_pretrained(MODEL_ID)
+    return model, processor
+
+# Initialize model (this will be cached)
+model, processor = load_model()
+
+def analyze_medical_image(image, clinical_question, patient_history=""):
+    """
+    Analyze medical image with clinical context
+    """
+    if image is None:
+        return "Please upload an image first."
+    
+    # Prepare the conversation
+    messages = [
+        {
+            "role": "system",
+            "content": [{"type": "text", "text": "You are an expert medical AI assistant. Provide detailed analysis while emphasizing that this is for educational purposes only and should not replace professional medical diagnosis."}]
+        }
+    ]
+    
+    # Add patient history if provided
+    if patient_history.strip():
+        messages.append({
+            "role": "user", 
+            "content": [{"type": "text", "text": f"Patient History: {patient_history}"}]
+        })
+    
+    # Add the main question with image
+    user_content = [{"type": "text", "text": clinical_question}]
+    if image:
+        user_content.append({"type": "image", "image": image})
+    
+    messages.append({
+        "role": "user",
+        "content": user_content
+    })
+    
+    try:
+        # Process inputs
+        inputs = processor.apply_chat_template(
+            messages, 
+            add_generation_prompt=True, 
+            tokenize=True,
+            return_dict=True, 
+            return_tensors="pt"
+        ).to(model.device, dtype=torch.bfloat16)
+        
+        input_len = inputs["input_ids"].shape[-1]
+        
+        # Generate response
+        with torch.inference_mode():
+            generation = model.generate(
+                **inputs, 
+                max_new_tokens=1000,
+                do_sample=True,
+                temperature=0.7,
+                top_p=0.9
+            )
+            generation = generation[0][input_len:]
+        
+        # Decode response
+        response = processor.decode(generation, skip_special_tokens=True)
+        
+        # Add disclaimer
+        disclaimer = "\n\n⚠️ IMPORTANT DISCLAIMER: This analysis is for educational and research purposes only. Always consult qualified healthcare professionals for medical diagnosis and treatment decisions."
+        
+        return response + disclaimer
+        
+    except Exception as e:
+        return f"Error processing request: {str(e)}"
+
+# Create Gradio interface
+def create_interface():
+    with gr.Blocks(title="MedGemma Medical Image Analysis", theme=gr.themes.Soft()) as demo:
+        gr.Markdown("""
+        # 🏥 MedGemma Medical Image Analysis
+        
+        **Educational Medical AI Assistant powered by Google's MedGemma-4B**
+        
+        ⚠️ **Important**: This tool is for educational and research purposes only. 
+        Do not use real patient data. Always consult healthcare professionals for medical decisions.
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                image_input = gr.Image(
+                    label="Medical Image", 
+                    type="pil",
+                    height=400
+                )
+                
+                clinical_question = gr.Textbox(
+                    label="Clinical Question",
+                    placeholder="e.g., 'Describe the findings in this chest X-ray' or 'What pathological changes do you observe?'",
+                    lines=3
+                )
+                
+                patient_history = gr.Textbox(
+                    label="Patient History (Optional)",
+                    placeholder="e.g., '45-year-old male with chronic cough and shortness of breath'",
+                    lines=3
+                )
+                
+                analyze_btn = gr.Button("🔍 Analyze Image", variant="primary", size="lg")
+                
+            with gr.Column(scale=1):
+                output = gr.Textbox(
+                    label="Medical Analysis",
+                    lines=20,
+                    max_lines=30
+                )
+        
+        # Example cases
+        gr.Markdown("## 📋 Example Cases")
+        
+        examples = gr.Examples(
+            examples=[
+                [
+                    "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png",
+                    "Describe the findings in this chest X-ray and identify any abnormalities.",
+                    "Adult patient with respiratory symptoms"
+                ],
+                [
+                    None,  # User will upload their own
+                    "What pathological changes are visible in this medical image?",
+                    ""
+                ],
+                [
+                    None,
+                    "Provide a differential diagnosis based on the imaging findings.",
+                    "Patient presenting with acute symptoms"
+                ]
+            ],
+            inputs=[image_input, clinical_question, patient_history]
+        )
+        
+        # Event handlers
+        analyze_btn.click(
+            fn=analyze_medical_image,
+            inputs=[image_input, clinical_question, patient_history],
+            outputs=output
+        )
+        
+        # Footer
+        gr.Markdown("""
+        ---
+        **Data Privacy Notice**: Do not upload real patient data or personally identifiable information. 
+        Use only synthetic, anonymized, or publicly available medical images for demonstration purposes.
+        
+        **Model**: Google MedGemma-4B | **Purpose**: Educational and Research Use Only
+        """)
+    
+    return demo
+
+# Launch the app
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True
+    )

requirements_txt.txt

@@ -0,0 +1,7 @@
+torch>=2.0.0
+transformers>=4.40.0
+gradio>=4.0.0
+Pillow>=9.0.0
+accelerate>=0.20.0
+requests>=2.28.0
+numpy>=1.21.0