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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+images/Chest_Xray_PA_3-8-2010.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,216 @@
+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+import torch
+import os
+import spaces
+
+# Initialize the model pipeline
+print("Loading MedGemma model...")
+pipe = pipeline(
+    "image-text-to-text",
+    model="google/medgemma-4b-it",
+    torch_dtype=torch.bfloat16,
+    device="cuda" if torch.cuda.is_available() else "cpu",
+)
+print("Model loaded successfully!")
+
+@spaces.GPU()
+def analyze_xray(image, custom_prompt=None):
+    """
+    Analyze X-ray image using MedGemma model
+    """
+    if image is None:
+        return "Please upload an X-ray image first."
+    
+    try:
+        # Use custom prompt if provided, otherwise use default
+        if custom_prompt and custom_prompt.strip():
+            prompt_text = custom_prompt.strip()
+        else:
+            prompt_text = "Describe this X-ray in detail, including any abnormalities or notable findings."
+        
+        messages = [
+            {
+                "role": "system",
+                "content": [{"type": "text", "text": "You are an expert radiologist with years of experience in interpreting medical images."}]
+            },
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompt_text},
+                    {"type": "image", "image": image},
+                ]
+            }
+        ]
+        
+        # Generate analysis
+        output = pipe(text=messages, max_new_tokens=300)
+        result = output[0]["generated_text"][-1]["content"]
+        
+        return result
+        
+    except Exception as e:
+        return f"Error analyzing image: {str(e)}"
+
+def load_sample_image():
+    """Load the sample X-ray image if it exists"""
+    sample_path = "./images/Chest_Xray_PA_3-8-2010.png"
+    if os.path.exists(sample_path):
+        return Image.open(sample_path)
+    return None
+
+# Create Gradio interface
+with gr.Blocks(
+    theme=gr.themes.Soft(),
+    title="AI X-ray Analysis System",
+    css="""
+    .header {
+        text-align: center;
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        color: white;
+        padding: 2rem;
+        border-radius: 10px;
+        margin-bottom: 2rem;
+    }
+    .warning {
+        background-color: #fff3cd;
+        border: 1px solid #ffeaa7;
+        border-radius: 8px;
+        padding: 1rem;
+        margin: 1rem 0;
+        color: #856404;
+    }
+    .gradio-container {
+        max-width: 1200px;
+        margin: auto;
+    }
+    """
+) as demo:
+    
+    # Header
+    gr.HTML("""
+        <div class="header">
+            <h1>🩻 AI X-ray Analysis System</h1>
+            <p>Advanced medical image analysis powered by Google's MedGemma AI</p>
+        </div>
+    """)
+    
+    # Warning disclaimer
+    gr.HTML("""
+        <div class="warning">
+            <strong>⚠️ Medical Disclaimer:</strong> This AI tool is for educational and research purposes only. 
+            It should not be used as a substitute for professional medical diagnosis or treatment. 
+            Always consult qualified healthcare professionals for medical advice.
+        </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 📤 Upload X-ray Image")
+            
+            # Image input
+            image_input = gr.Image(
+                label="X-ray Image",
+                type="pil",
+                height=400,
+                sources=["upload", "clipboard"]
+            )
+            
+            # Sample image button
+            sample_btn = gr.Button(
+                "📋 Load Sample Image", 
+                variant="secondary",
+                size="sm"
+            )
+            
+            # Custom prompt input
+            gr.Markdown("### 💬 Custom Analysis Prompt (Optional)")
+            custom_prompt = gr.Textbox(
+                label="Custom Prompt",
+                placeholder="Enter specific questions about the X-ray (e.g., 'Focus on the heart area' or 'Look for signs of pneumonia')",
+                lines=3,
+                max_lines=5
+            )
+            
+            # Analyze button
+            analyze_btn = gr.Button(
+                "🔍 Analyze X-ray", 
+                variant="primary",
+                size="lg"
+            )
+        
+        with gr.Column(scale=1):
+            gr.Markdown("### 📊 Analysis Results")
+            
+            # Output text
+            output_text = gr.Textbox(
+                label="AI Analysis Report",
+                lines=15,
+                max_lines=20,
+                show_copy_button=True,
+                placeholder="Upload an X-ray image and click 'Analyze X-ray' to see the AI analysis results here..."
+            )
+            
+            # Quick action buttons
+            with gr.Row():
+                clear_btn = gr.Button("🗑️ Clear", variant="secondary", size="sm")
+                copy_btn = gr.Button("📋 Copy Results", variant="secondary", size="sm")
+    
+    # Example prompts section
+    gr.Markdown("### 💡 Example Prompts")
+    with gr.Row():
+        example_prompts = [
+            "Describe this X-ray in detail, including any abnormalities or notable findings.",
+            "Focus on the lung fields and identify any signs of infection or disease.",
+            "Examine the heart size and shape. Is the cardiac silhouette normal?",
+            "Look for any signs of fractures or bone abnormalities.",
+            "Analyze the overall image quality and positioning."
+        ]
+        
+        for i, prompt in enumerate(example_prompts):
+            gr.Button(
+                f"Example {i+1}",
+                size="sm"
+            ).click(
+                lambda p=prompt: p,
+                outputs=custom_prompt
+            )
+    
+    # Event handlers
+    def clear_all():
+        return None, "", ""
+    
+    sample_btn.click(
+        fn=load_sample_image,
+        outputs=image_input
+    )
+    
+    analyze_btn.click(
+        fn=analyze_xray,
+        inputs=[image_input, custom_prompt],
+        outputs=output_text
+    )
+    
+    clear_btn.click(
+        fn=clear_all,
+        outputs=[image_input, custom_prompt, output_text]
+    )
+    
+    # Auto-analyze when image is uploaded (optional)
+    image_input.change(
+        fn=lambda img: analyze_xray(img) if img is not None else "",
+        inputs=image_input,
+        outputs=output_text
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    print("Starting Gradio interface...")
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,  # Set to True if you want to create a public link
+        show_error=True,
+        favicon_path=None
+    )

requirements.txt

@@ -0,0 +1,3 @@
+transformers
+spaces
+pillow