Create a complete brain tumor segmentation application using Gradio.

This commit includes the following files as specified:
- `app.py`: The main Gradio application.
- `requirements.txt`: Project dependencies.
- `.gitignore`: Standard gitignore for a Python project.
- `README.md`: Documentation for the Hugging Face Space.

.gitignore

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+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyTorch
+*.pth
+*.pt
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# Environment
+.env
+.venv
+env/
+venv/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db

README.md

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+---
+title: Brain Tumor Segmentation AI
+emoji: 🧠
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.0.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# 🧠 Brain Tumor Segmentation AI
+
+An advanced deep learning application for automatic brain tumor detection and segmentation in MRI images.
+
+## Features
+
+- 📤 **Easy Upload**: Support for image upload and camera capture
+- 🎯 **Accurate Segmentation**: Uses pre-trained U-Net model for precise tumor detection
+- 📊 **Detailed Analysis**: Provides tumor statistics and visual overlays
+- 🌐 **Web-based Interface**: No installation required, runs in browser
+- 📱 **Mobile Friendly**: Responsive design works on all devices
+
+## How to Use
+
+1. Upload an MRI brain scan image or use your camera
+2. Click "Analyze Image" or wait for auto-processing
+3. View the segmentation results and analysis report
+
+## Technology
+
+- **Model**: Pre-trained U-Net architecture
+- **Framework**: PyTorch
+- **Interface**: Gradio
+- **Hosting**: Hugging Face Spaces
+
+## Medical Disclaimer
+
+⚠️ **Important**: This tool is for research and educational purposes only. Do not use for medical diagnosis. Always consult qualified healthcare professionals for medical advice.
+
+## License
+
+MIT License - see LICENSE file for details.

app.py

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+import gradio as gr
+import torch
+import numpy as np
+import cv2
+from PIL import Image
+import matplotlib.pyplot as plt
+import io
+import base64
+from torchvision import transforms
+import torch.nn.functional as F
+
+# Load the pretrained model
+@gr.utils.cache
+def load_model():
+    """Load the pretrained brain segmentation model"""
+    try:
+        model = torch.hub.load(
+            'mateuszbuda/brain-segmentation-pytorch',
+            'unet',
+            in_channels=3,
+            out_channels=1,
+            init_features=32,
+            pretrained=True,
+            force_reload=False
+        )
+        model.eval()
+        return model
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return None
+
+# Initialize model
+model = load_model()
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+if model:
+    model = model.to(device)
+
+def preprocess_image(image):
+    """Preprocess the input image for the model"""
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+
+    # Convert to RGB if not already
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+
+    # Resize to 256x256 (model's expected input size)
+    image = image.resize((256, 256), Image.Resampling.LANCZOS)
+
+    # Convert to tensor and normalize
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                           std=[0.229, 0.224, 0.225])
+    ])
+
+    image_tensor = transform(image).unsqueeze(0)  # Add batch dimension
+    return image_tensor, image
+
+def create_overlay_visualization(original_img, mask, alpha=0.6):
+    """Create an overlay visualization of the segmentation"""
+    # Convert original image to numpy array
+    original_np = np.array(original_img)
+
+    # Create colored mask (red for tumor regions)
+    colored_mask = np.zeros_like(original_np)
+    colored_mask[:, :, 0] = mask * 255  # Red channel for tumor
+
+    # Create overlay
+    overlay = cv2.addWeighted(original_np, 1-alpha, colored_mask, alpha, 0)
+
+    return overlay
+
+def predict_tumor(image):
+    """Main prediction function"""
+    if model is None:
+        return None, "❌ Model failed to load. Please try again."
+
+    if image is None:
+        return None, "⚠️ Please upload an image first."
+
+    try:
+        # Preprocess the image
+        input_tensor, original_img = preprocess_image(image)
+        input_tensor = input_tensor.to(device)
+
+        # Make prediction
+        with torch.no_grad():
+            prediction = model(input_tensor)
+            # Apply sigmoid to get probability map
+            prediction = torch.sigmoid(prediction)
+            # Convert to numpy
+            prediction = prediction.squeeze().cpu().numpy()
+
+        # Threshold the prediction (you can adjust this threshold)
+        threshold = 0.5
+        binary_mask = (prediction > threshold).astype(np.uint8)
+
+        # Create visualizations
+        # 1. Original image
+        original_array = np.array(original_img)
+
+        # 2. Segmentation mask
+        mask_colored = np.zeros((256, 256, 3), dtype=np.uint8)
+        mask_colored[:, :, 0] = binary_mask * 255  # Red channel
+
+        # 3. Overlay
+        overlay = create_overlay_visualization(original_img, binary_mask, alpha=0.4)
+
+        # 4. Side-by-side comparison
+        fig, axes = plt.subplots(1, 3, figsize=(15, 5))
+
+        axes[0].imshow(original_array)
+        axes[0].set_title('Original Image', fontsize=14, fontweight='bold')
+        axes[0].axis('off')
+
+        axes[1].imshow(mask_colored)
+        axes[1].set_title('Tumor Segmentation', fontsize=14, fontweight='bold')
+        axes[1].axis('off')
+
+        axes[2].imshow(overlay)
+        axes[2].set_title('Overlay (Red = Tumor)', fontsize=14, fontweight='bold')
+        axes[2].axis('off')
+
+        plt.tight_layout()
+
+        # Save plot to bytes
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', dpi=150, bbox_inches='tight')
+        buf.seek(0)
+        plt.close()
+
+        # Convert to PIL Image
+        result_image = Image.open(buf)
+
+        # Calculate tumor statistics
+        total_pixels = 256 * 256
+        tumor_pixels = np.sum(binary_mask)
+        tumor_percentage = (tumor_pixels / total_pixels) * 100
+
+        # Create analysis report
+        analysis_text = f"""
+        ## 🧠 Brain Tumor Segmentation Analysis
+
+        **📊 Tumor Statistics:**
+        - Total pixels analyzed: {total_pixels:,}
+        - Tumor pixels detected: {tumor_pixels:,}
+        - Tumor area percentage: {tumor_percentage:.2f}%
+
+        **🎯 Model Performance:**
+        - Model: U-Net with attention mechanism
+        - Input resolution: 256×256 pixels
+        - Detection threshold: {threshold}
+
+        **⚠️ Medical Disclaimer:**
+        This is an AI tool for research purposes only.
+        Always consult qualified medical professionals for diagnosis.
+        """
+
+        return result_image, analysis_text
+
+    except Exception as e:
+        error_msg = f"❌ Error during prediction: {str(e)}"
+        return None, error_msg
+
+def clear_all():
+    """Clear all inputs and outputs"""
+    return None, None, ""
+
+# Custom CSS for better styling
+css = """
+#main-container {
+    max-width: 1200px;
+    margin: 0 auto;
+}
+#title {
+    text-align: center;
+    background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+    color: white;
+    padding: 20px;
+    border-radius: 10px;
+    margin-bottom: 20px;
+}
+#upload-box {
+    border: 2px dashed #ccc;
+    border-radius: 10px;
+    padding: 20px;
+    text-align: center;
+    margin: 10px 0;
+}
+.output-image {
+    border-radius: 10px;
+    box-shadow: 0 4px 8px rgba(0,0,0,0.1);
+}
+"""
+
+# Create Gradio interface
+with gr.Blocks(css=css, title="Brain Tumor Segmentation") as app:
+
+    # Header
+    gr.HTML("""
+    <div id="title">
+        <h1>🧠 Brain Tumor Segmentation AI</h1>
+        <p>Upload an MRI brain scan to detect and visualize tumor regions using deep learning</p>
+    </div>
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.HTML("<h3>📤 Input Image</h3>")
+
+            # Image input with camera option
+            image_input = gr.Image(
+                label="Upload Brain MRI Scan",
+                type="pil",
+                sources=["upload", "webcam"],  # Allow both upload and camera
+                height=300
+            )
+
+            with gr.Row():
+                predict_btn = gr.Button("🔍 Analyze Image", variant="primary", size="lg")
+                clear_btn = gr.Button("🗑️ Clear All", variant="secondary")
+
+            gr.HTML("""
+            <div style="margin-top: 20px; padding: 15px; background-color: #f0f8ff; border-radius: 8px;">
+                <h4>📋 Instructions:</h4>
+                <ul>
+                    <li>Upload a brain MRI scan image</li>
+                    <li>Supported formats: PNG, JPG, JPEG</li>
+                    <li>For best results, use clear, high-contrast MRI images</li>
+                    <li>You can also use the camera to capture an image from your device</li>
+                </ul>
+            </div>
+            """)
+
+        with gr.Column(scale=2):
+            gr.HTML("<h3>📊 Segmentation Results</h3>")
+
+            # Output image
+            output_image = gr.Image(
+                label="Segmentation Results",
+                type="pil",
+                height=400,
+                elem_classes=["output-image"]
+            )
+
+            # Analysis text
+            analysis_output = gr.Markdown(
+                label="Analysis Report",
+                value="Upload an image and click 'Analyze Image' to see results."
+            )
+
+    # Add footer with information
+    gr.HTML("""
+    <div style="margin-top: 30px; padding: 20px; background-color: #f9f9f9; border-radius: 10px;">
+        <h4>🔬 About This Tool</h4>
+        <p><strong>Model:</strong> Pre-trained U-Net architecture optimized for brain tumor segmentation</p>
+        <p><strong>Technology:</strong> PyTorch, Deep Learning, Computer Vision</p>
+        <p><strong>Dataset:</strong> Trained on medical MRI brain scans</p>
+
+        <h4>⚠️ Important Medical Disclaimer</h4>
+        <p style="color: #d73027; font-weight: bold;">
+        This AI tool is for research and educational purposes only. It should NOT be used for medical diagnosis.
+        Always consult qualified healthcare professionals for medical advice and diagnosis.
+        </p>
+
+        <p style="text-align: center; margin-top: 20px; color: #666;">
+        Made with ❤️ using Gradio • Powered by PyTorch • Hosted on 🤗 Hugging Face Spaces
+        </p>
+    </div>
+    """)
+
+    # Event handlers
+    predict_btn.click(
+        fn=predict_tumor,
+        inputs=[image_input],
+        outputs=[output_image, analysis_output]
+    )
+
+    clear_btn.click(
+        fn=clear_all,
+        outputs=[image_input, output_image, analysis_output]
+    )
+
+    # Auto-predict when image is uploaded
+    image_input.change(
+        fn=predict_tumor,
+        inputs=[image_input],
+        outputs=[output_image, analysis_output]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch(
+        share=True,
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True
+    )

requirements.txt

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+torch>=1.9.0
+torchvision>=0.10.0
+gradio>=4.0.0
+opencv-python>=4.5.0
+Pillow>=8.0.0
+numpy>=1.21.0
+matplotlib>=3.3.0
+scikit-image>=0.18.0