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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/Infection.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,235 @@
+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+import torch
+import os
+import spaces
+import time
+import os
+from huggingface_hub import login
+
+
+# Access the secret token
+hf_token = os.environ.get("HF_TOKEN")
+
+# Login to Hugging Face Hub
+login(token=hf_token)
+
+
+# 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",
+    # device_map="auto",
+)
+print("Model loaded successfully!")
+
+
+@spaces.GPU(duration=300)
+def analyze_img(image, custom_prompt=None):
+    """
+    Analyze image using MedGemma model
+    """
+    if image is None:
+        return "Please upload an image first."
+
+    try:
+        # System prompt for the model
+        system_prompt_text = """You are a expert medical AI assistant with years of experience in interpreting medical images. Your purpose is to assist qualified clinicians by providing an detailed analysis of the provided medical image."""
+        # 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 image in detail, including any abnormalities or notable findings."
+
+        messages = [
+            {
+                "role": "system",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": system_prompt_text,
+                    }
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompt_text},
+                    {"type": "image", "image": image},
+                ],
+            },
+        ]
+
+        # Generate analysis
+        output = pipe(text=messages, max_new_tokens=1024)
+        full_response = output[0]["generated_text"][-1]["content"]
+
+        partial_message = ""
+        for char in full_response:
+            partial_message += char
+            time.sleep(0.01)  # Add a small delay to make the typing visible
+            yield partial_message
+
+    except Exception as e:
+        return f"Error analyzing image: {str(e)}"
+
+
+def load_sample_image():
+    """Load the sample image if it exists"""
+    sample_path = "./images/Infection.jpg"
+    if os.path.exists(sample_path):
+        return Image.open(sample_path)
+    return None
+
+
+# Create Gradio interface
+with gr.Blocks(
+    theme=gr.themes.Citrus(),
+    title="MedGemma",
+    css="""
+    .header {
+        text-align: center;
+        background: linear-gradient(135deg, #f5af19 0%, #f12711 100%);
+        color: white;
+        padding: 2rem;
+        border-radius: 10px;
+        margin-bottom: 2rem;
+    }
+    .warning {
+        background-color: #fff0e6;
+        border: 3px solid #ffab73;
+        border-radius: 8px;
+        padding: 1rem;
+        margin: 1rem 0;
+        color: #8c2b00;
+    }
+    .gradio-container {
+        max-width: 1200px;
+        margin: auto;
+    }
+    .warning strong{
+        color: inherit;
+    }
+    """,
+) as demo:
+
+    # Header
+    gr.HTML(
+        """
+        <div class="header">
+            <h1> MedGemma Medical Image Analysis and QnA</h1>
+            <p>Advanced medical image analysis powered by Google's MedGemma</p>
+        </div>
+    """
+    )
+
+    # Warning disclaimer
+    gr.HTML(
+        """
+        <div class="warning">
+            <strong> Medical Disclaimer:</strong> This model 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 Medical Image (Radiology, Pathology, Dermatology, CT, X-Ray)")
+
+            # Image input
+            image_input = gr.Image(label="Input 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 Image (e.g., 'Focus on the heart area' or 'Look for signs of pneumonia')",
+                value="Describe this Image and Generate a compact Clinical report",
+                lines=3,
+                max_lines=5,
+            )
+
+            # Analyze button
+            analyze_btn = gr.Button("🔍 Analyze Image", variant="primary", size="lg")
+
+        with gr.Column(scale=1):
+            gr.Markdown("### 📊 Analysis Report")
+
+            # Output text
+            output_text = gr.Textbox(
+                label="Generated Report",
+                lines=28,
+                max_lines=1024,
+                show_label=False,
+                show_copy_button=False,
+                placeholder="Upload an X-ray image or CT scan or any othe medical image and click 'Analyze Image' 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.",
+            "Describe the morphology of this skin lesion, focusing on color, border, and texture.",
+            "What are the key histological features visible in this tissue sample?",
+            "Look for any signs of fractures or bone abnormalities.",
+            "Analyze this fundus image and describe the condition of the optic disc and vasculature.",
+        ]
+
+        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_img, inputs=[image_input, custom_prompt], outputs=output_text)
+
+    clear_btn.click(fn=clear_all, outputs=[image_input, custom_prompt, output_text])
+
+    copy_btn.click(
+        fn=None,  # No Python function needed for this client-side action
+        inputs=[output_text],
+        js="""
+    (text_to_copy) => {
+        if (text_to_copy) {
+            navigator.clipboard.writeText(text_to_copy);
+            alert("Results copied to clipboard!");
+        } else {
+            alert("Nothing to copy!");
+        }
+    }
+    """,
+    )
+
+    # Auto-analyze when image is uploaded (optional)
+    image_input.change(
+        fn=lambda img: analyze_img(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 +1,6 @@
+transformers
+Pillow
+spaces
+torch
+accelerate
+bitsandbytes