Create app.py

app.py

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+"""
+Color Quantification Tool - Hugging Face Space Application
+Web interface for color analysis with download links to desktop version
+"""
+
+import gradio as gr
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+import pandas as pd
+import io
+from sklearn.cluster import KMeans
+
+class WebColorAnalyzer:
+    def __init__(self):
+        self.supported_formats = ['.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.tif']
+        
+    def analyze_single_image(self, image, num_clusters=5):
+        """Analyze a single image for web interface"""
+        try:
+            # Convert PIL image to numpy array
+            if isinstance(image, Image.Image):
+                img_array = np.array(image)
+            else:
+                img_array = image
+            
+            # Convert to RGB if needed
+            if len(img_array.shape) == 3 and img_array.shape[2] == 3:
+                img_rgb = img_array
+            elif len(img_array.shape) == 3 and img_array.shape[2] == 4:
+                img_rgb = img_array[:, :, :3]  # Remove alpha channel
+            else:
+                return None, "Unsupported image format"
+            
+            # Reshape for clustering
+            pixels = img_rgb.reshape(-1, 3)
+            
+            # Remove pure black pixels (background)
+            mask = np.sum(pixels, axis=1) > 30
+            if np.sum(mask) < 100:  # If too few pixels, use all
+                mask = np.ones(len(pixels), dtype=bool)
+            
+            filtered_pixels = pixels[mask]
+            
+            # Perform K-means clustering
+            kmeans = KMeans(n_clusters=min(num_clusters, len(filtered_pixels)), 
+                          random_state=42, n_init=10)
+            kmeans.fit(filtered_pixels)
+            
+            # Get dominant colors
+            colors = kmeans.cluster_centers_.astype(int)
+            labels = kmeans.labels_
+            
+            # Calculate color percentages
+            unique_labels, counts = np.unique(labels, return_counts=True)
+            percentages = (counts / len(labels)) * 100
+            
+            # Create results
+            results = []
+            for i, (color, percentage) in enumerate(zip(colors, percentages)):
+                results.append({
+                    'Color': f'Color {i+1}',
+                    'RGB': f'({color[0]}, {color[1]}, {color[2]})',
+                    'Hex': f'#{color[0]:02x}{color[1]:02x}{color[2]:02x}',
+                    'Percentage': f'{percentage:.1f}%'
+                })
+            
+            return results, None
+            
+        except Exception as e:
+            return None, f"Analysis error: {str(e)}"
+    
+    def create_color_palette_image(self, results):
+        """Create a color palette visualization"""
+        if not results:
+            return None
+        
+        try:
+            # Create figure
+            fig, ax = plt.subplots(1, 1, figsize=(10, 2))
+            
+            # Extract colors and percentages
+            colors = []
+            percentages = []
+            for result in results:
+                rgb_str = result['RGB'].strip('()')
+                r, g, b = map(int, rgb_str.split(', '))
+                colors.append([r/255, g/255, b/255])
+                percentages.append(float(result['Percentage'].strip('%')))
+            
+            # Create color bars
+            left = 0
+            for i, (color, percentage) in enumerate(zip(colors, percentages)):
+                width = percentage / 100
+                ax.barh(0, width, left=left, color=color, height=0.5)
+                
+                # Add percentage text
+                if width > 0.1:  # Only show text if bar is wide enough
+                    ax.text(left + width/2, 0, f'{percentage:.1f}%', 
+                           ha='center', va='center', fontweight='bold')
+                
+                left += width
+            
+            ax.set_xlim(0, 1)
+            ax.set_ylim(-0.3, 0.3)
+            ax.set_xlabel('Color Distribution')
+            ax.set_title('Dominant Colors Palette')
+            ax.set_yticks([])
+            
+            # Convert to image
+            buf = io.BytesIO()
+            plt.savefig(buf, format='png', bbox_inches='tight', dpi=150)
+            buf.seek(0)
+            plt.close()
+            
+            return Image.open(buf)
+            
+        except Exception as e:
+            print(f"Error creating palette: {e}")
+            return None
+
+def create_download_interface():
+    """Create the download and information interface"""
+    
+    download_html = """
+    <div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 10px; margin: 10px 0;">
+        <h2 style="color: white; margin-bottom: 20px;">🖥️ Desktop Version Available</h2>
+        <p style="color: white; font-size: 16px; margin-bottom: 20px;">
+            Get the full-featured desktop application with advanced analysis capabilities!
+        </p>
+        
+        <div style="background: rgba(255,255,255,0.1); padding: 15px; border-radius: 8px; margin: 15px 0;">
+            <h3 style="color: white; margin-bottom: 10px;">📥 Download Links</h3>
+            <div style="display: flex; justify-content: center; gap: 15px; flex-wrap: wrap;">
+                <a href="https://github.com/your-username/ColorQuantificationTool" 
+                   style="background: #4285f4; color: white; padding: 10px 20px; text-decoration: none; border-radius: 5px; font-weight: bold;">
+                   📁 GitHub Repository
+                </a>
+                <a href="https://github.com/your-username/ColorQuantificationTool/releases" 
+                   style="background: #0078d4; color: white; padding: 10px 20px; text-decoration: none; border-radius: 5px; font-weight: bold;">
+                   ⬇️ Download Desktop App
+                </a>
+            </div>
+        </div>
+        
+        <div style="background: rgba(255,255,255,0.1); padding: 15px; border-radius: 8px; margin: 15px 0;">
+            <h3 style="color: white; margin-bottom: 10px;">✨ Desktop Features</h3>
+            <div style="text-align: left; color: white;">
+                <ul style="list-style: none; padding: 0;">
+                    <li>🔬 Advanced multi-color space analysis (RGB, LAB, HSV)</li>
+                    <li>📊 Professional statistical analysis and reporting</li>
+                    <li>🎯 Batch processing of thousands of images</li>
+                    <li>📈 Interactive 3D visualizations and heatmaps</li>
+                    <li>💾 Export to Excel, CSV, JSON formats</li>
+                    <li>🔧 Configurable clustering parameters</li>
+                    <li>⚡ GPU-accelerated processing</li>
+                    <li>🔒 Complete offline operation</li>
+                </ul>
+            </div>
+        </div>
+        
+        <div style="background: rgba(255,255,255,0.1); padding: 15px; border-radius: 8px;">
+            <p style="color: white; margin: 0;">
+                <strong>File Size:</strong> 1.1GB | <strong>Platform:</strong> Windows 64-bit<br>
+                <strong>Requirements:</strong> Windows 10/11, 8GB RAM
+            </p>
+        </div>
+    </div>
+    """
+    
+    return download_html
+
+def analyze_image_web(image, num_clusters):
+    """Web interface analysis function"""
+    if image is None:
+        return None, "Please upload an image first.", None
+    
+    analyzer = WebColorAnalyzer()
+    results, error = analyzer.analyze_single_image(image, num_clusters)
+    
+    if error:
+        return None, error, None
+    
+    # Create DataFrame for display
+    df = pd.DataFrame(results)
+    
+    # Create color palette visualization
+    palette_img = analyzer.create_color_palette_image(results)
+    
+    return df, "✅ Analysis completed successfully!", palette_img
+
+def create_interface():
+    """Create the main Gradio interface"""
+    
+    with gr.Blocks(
+        title="🎨 Color Quantification Tool",
+        theme=gr.themes.Soft(),
+    ) as demo:
+        
+        gr.Markdown("""
+        # 🎨 Color Quantification Tool
+        
+        **Professional Color Analysis Platform** - Web Demo Version
+        
+        This is a simplified web version for demonstration. For full functionality, download the desktop application below.
+        """)
+        
+        # Download section
+        gr.HTML(create_download_interface())
+        
+        gr.Markdown("## 🌐 Try the Web Demo")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("### 📤 Input")
+                
+                image_input = gr.Image(
+                    label="Upload Image",
+                    type="pil",
+                    height=300
+                )
+                
+                num_clusters = gr.Slider(
+                    minimum=2,
+                    maximum=10,
+                    value=5,
+                    step=1,
+                    label="Number of Colors to Extract"
+                )
+                
+                analyze_btn = gr.Button(
+                    "🔍 Analyze Colors",
+                    variant="primary",
+                    size="lg"
+                )
+            
+            with gr.Column(scale=2):
+                gr.Markdown("### 📊 Results")
+                
+                status_output = gr.Textbox(
+                    label="Status",
+                    interactive=False
+                )
+                
+                results_output = gr.Dataframe(
+                    label="Dominant Colors",
+                    headers=["Color", "RGB", "Hex", "Percentage"],
+                    interactive=False
+                )
+                
+                palette_output = gr.Image(
+                    label="Color Palette",
+                    height=150
+                )
+        
+        # Event handlers
+        analyze_btn.click(
+            fn=analyze_image_web,
+            inputs=[image_input, num_clusters],
+            outputs=[results_output, status_output, palette_output]
+        )
+        
+        # Example section
+        gr.Markdown("""
+        ## 📋 Usage Instructions
+        
+        1. **Upload an image** using the file uploader
+        2. **Adjust the number of colors** to extract (2-10)
+        3. **Click "Analyze Colors"** to start the analysis
+        4. **View the results** in the table and color palette
+        
+        ### 🔬 What This Demo Shows
+        - Basic color extraction using K-means clustering
+        - Dominant color identification with percentages
+        - Visual color palette representation
+        
+        ### 🚀 Desktop Version Features
+        - **Multi-color space analysis** (RGB, LAB, HSV)
+        - **Advanced distance metrics** (ΔE2000, Bhattacharyya)
+        - **Batch processing** of multiple images
+        - **Statistical analysis** and professional reporting
+        - **3D visualizations** and interactive charts
+        - **Export capabilities** (Excel, CSV, JSON)
+        """)
+        
+        # Footer
+        gr.Markdown("""
+        ---
+        
+        **🎯 Ready for Advanced Analysis?**
+        
+        Download the full desktop version for professional color quantification with advanced features!
+        
+        *Color Quantification Tool - Making color analysis accessible to everyone*
+        """)
+    
+    return demo
+
+if __name__ == "__main__":
+    # Create and launch the interface
+    demo = create_interface()
+    demo.launch()