Update app.py

app.py

@@ -1,379 +1,48 @@
+import gradio as gr
 import torch
-import torch.nn as nn
 from torchvision import models, transforms
 from PIL import Image
-import gradio as gr
-from captum.attr import LayerGradCam, IntegratedGradients
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 from io import BytesIO
-import urllib.request
-from torch.nn.functional import interpolate
-import warnings
-warnings.filterwarnings('ignore')
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-@torch.no_grad()
-def load_models_and_labels():
-    """Load multiple models for ensemble prediction"""
-    print("🚀 Loading advanced models...")
+# Minimal working version
+def minimal_predict(image):
+    if image is None:
+        return "Please upload an image", None
     
-    # Model 1: EfficientNet-B4 (Best accuracy/speed ratio)
-    efficientnet = models.efficientnet_b4(weights='IMAGENET1K_V1')
-    efficientnet.eval().to(DEVICE)
+    # Simple transform
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+    ])
     
-    # Model 2: ResNet152 (Deep architecture)
-    resnet152 = models.resnet152(weights='IMAGENET1K_V2')
-    resnet152.eval().to(DEVICE)
+    img_tensor = transform(image).unsqueeze(0)
     
-    # Model 3: ConvNeXt-Base (State-of-the-art)
-    convnext = models.convnext_base(weights='IMAGENET1K_V1')
-    convnext.eval().to(DEVICE)
+    # Create a simple visualization
+    fig, ax = plt.subplots(1, 1, figsize=(8, 6))
+    ax.imshow(image)
+    ax.set_title("Processed Image")
+    ax.axis('off')
     
-    # Load ImageNet labels
-    url = "https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"
-    response = urllib.request.urlopen(url)
-    labels = [line.decode('utf-8').strip() for line in response.readlines()]
+    buf = BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    result_img = Image.open(buf)
+    plt.close(fig)
     
-    print("✅ Models loaded successfully!")
-    return efficientnet, resnet152, convnext, labels
-
-efficientnet_model, resnet152_model, convnext_model, IMAGENET_LABELS = load_models_and_labels()
-
-# Setup Grad-CAM for each model
-efficientnet_target = efficientnet_model.features[-1]
-resnet152_target = resnet152_model.layer4[-1]
-convnext_target = convnext_model.features[-1][-1]
-
-gradcam_efficient = LayerGradCam(efficientnet_model, efficientnet_target)
-gradcam_resnet = LayerGradCam(resnet152_model, resnet152_target)
-gradcam_convnext = LayerGradCam(convnext_model, convnext_target)
-
-# Transforms for different models
-transform_standard = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-])
-
-transform_large = transforms.Compose([
-    transforms.Resize((380, 380)),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-])
-
-
-def predict_and_explain(image, use_tta=True):
-    if image is None:
-        return "Please upload an image", None, None
-
-    try:
-        # Prepare inputs
-        img_tensor_224 = transform_standard(image).unsqueeze(0).to(DEVICE)
-        img_tensor_380 = transform_large(image).unsqueeze(0).to(DEVICE)
-        
-        predictions = []
-        model_names = []
-        
-        with torch.no_grad():
-            # EfficientNet-B4 prediction (380x380)
-            output_eff = efficientnet_model(img_tensor_380)
-            prob_eff = torch.softmax(output_eff, dim=1)
-            predictions.append(prob_eff)
-            model_names.append("EfficientNet-B4")
-            
-            # ResNet152 prediction
-            output_res = resnet152_model(img_tensor_224)
-            prob_res = torch.softmax(output_res, dim=1)
-            predictions.append(prob_res)
-            model_names.append("ResNet152")
-            
-            # ConvNeXt prediction
-            output_conv = convnext_model(img_tensor_224)
-            prob_conv = torch.softmax(output_conv, dim=1)
-            predictions.append(prob_conv)
-            model_names.append("ConvNeXt-Base")
-            
-            # Test-Time Augmentation (optional)
-            if use_tta:
-                # Horizontal flip
-                img_flip = transforms.functional.hflip(image)
-                img_flip_tensor = transform_standard(img_flip).unsqueeze(0).to(DEVICE)
-                
-                output_flip_res = resnet152_model(img_flip_tensor)
-                predictions.append(torch.softmax(output_flip_res, dim=1))
-                model_names.append("ResNet152-Flip")
-        
-        # Ensemble: Weighted average (EfficientNet gets highest weight)
-        weights = [0.40, 0.30, 0.25, 0.05] if use_tta else [0.45, 0.30, 0.25]
-        ensemble_prob = sum(w * p for w, p in zip(weights, predictions))
-        
-        top10_prob, top10_idx = torch.topk(ensemble_prob, 10)
-        pred_class = top10_idx[0][0].item()
-        confidence = top10_prob[0][0].item()
-
-        # Generate Grad-CAM from best performing model (EfficientNet)
-        attributions = gradcam_efficient.attribute(img_tensor_380, target=pred_class)
-        attr_resized = interpolate(attributions, size=(224, 224), mode='bilinear', align_corners=False)
-        attr_np = attr_resized.squeeze().cpu().detach().numpy()
-        attr_np = (attr_np - attr_np.min()) / (attr_np.max() - attr_np.min() + 1e-8)
-
-        # Alternative: Get Grad-CAM from all models and average
-        attr_resnet = gradcam_resnet.attribute(img_tensor_224, target=pred_class)
-        attr_resnet = interpolate(attr_resnet, size=(224, 224), mode='bilinear', align_corners=False)
-        attr_resnet_np = attr_resnet.squeeze().cpu().detach().numpy()
-        attr_resnet_np = (attr_resnet_np - attr_resnet_np.min()) / (attr_resnet_np.max() - attr_resnet_np.min() + 1e-8)
-        
-        # Average heatmaps for better visualization
-        attr_avg = (attr_np * 0.6 + attr_resnet_np * 0.4)
-
-        # Main visualization
-        fig = plt.figure(figsize=(24, 14))  
-        fig.patch.set_facecolor('#0a0a0a')
-        
-        gs = fig.add_gridspec(2, 3, height_ratios=[2, 1], hspace=0.3, wspace=0.15)  
-        
-        ax1 = fig.add_subplot(gs[0, 0])
-        ax2 = fig.add_subplot(gs[0, 1])
-        ax3 = fig.add_subplot(gs[0, 2])
-        ax4 = fig.add_subplot(gs[1, :])
-        
-        ax1.imshow(image)
-        ax1.set_title("Original Image", fontsize=18, fontweight='700', color='#e0e0e0', pad=20)
-        ax1.axis('off')
-        
-        im = ax2.imshow(attr_avg, cmap='jet', interpolation='bilinear')
-        ax2.set_title("Ensemble Grad-CAM", fontsize=18, fontweight='700', color='#e0e0e0', pad=20)
-        ax2.axis('off')
-        cbar = plt.colorbar(im, ax=ax2, fraction=0.046, pad=0.04)
-        cbar.ax.tick_params(labelsize=12, colors='#a0a0a0')
-        cbar.set_label('Importance', rotation=270, labelpad=25, color='#e0e0e0', fontsize=13, fontweight='600')
+    return "Analysis complete", result_img
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Simple Image Classifier")
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil")
+            analyze_btn = gr.Button("Analyze")
+        with gr.Column():
+            text_output = gr.Textbox()
+            image_output = gr.Image()
     
-        ax3.imshow(image)
-        ax3.imshow(attr_avg, cmap='jet', alpha=0.5, interpolation='bilinear')
-        ax3.set_title(f"AI Focus: {IMAGENET_LABELS[pred_class]}", fontsize=18, fontweight='700', color='#e0e0e0', pad=20)
-        ax3.axis('off')
-        
-        top10_labels = [IMAGENET_LABELS[idx.item()] for idx in top10_idx[0]]
-        top10_probs = [prob.item() * 100 for prob in top10_prob[0]]
-        
-        colors = ['#10b981' if i == 9 else '#3b82f6' if i >= 7 else '#8b5cf6' for i in range(10)]
-        bars = ax4.barh(range(10), top10_probs[::-1], color=colors[::-1], edgecolor='#1a1a1a', linewidth=2)
-        
-        ax4.set_yticks(range(10))
-        ax4.set_yticklabels(top10_labels[::-1], fontsize=14, color='#e0e0e0', fontweight='600')
-        ax4.set_xlabel('Confidence (%)', fontsize=15, color='#e0e0e0', fontweight='700')
-        ax4.set_title('Top 10 Predictions (Ensemble)', fontsize=19, fontweight='800', color='#e0e0e0', pad=20)
-        ax4.set_xlim([0, 100])
-        ax4.grid(axis='x', alpha=0.2, color='#404040', linestyle='--')
-        ax4.set_facecolor('#0a0a0a')
-        ax4.spines['top'].set_visible(False)
-        ax4.spines['right'].set_visible(False)
-        ax4.spines['left'].set_color('#404040')
-        ax4.spines['bottom'].set_color('#404040')
-        ax4.tick_params(colors='#a0a0a0', labelsize=13)
-        
-        for bar, prob in zip(bars, top10_probs[::-1]):
-            ax4.text(prob + 1.5, bar.get_y() + bar.get_height()/2, 
-                    f'{prob:.1f}%', va='center', fontsize=13, color='#e0e0e0', fontweight='700')
-        
-        plt.tight_layout()
-        
-        buf = BytesIO()
-        plt.savefig(buf, format='png', dpi=150, bbox_inches='tight', facecolor='#0a0a0a')
-        buf.seek(0)
-        result_image = Image.open(buf)
-        plt.close(fig)
-
-        # Detailed comparison: Individual model heatmaps
-        fig2, axes = plt.subplots(2, 2, figsize=(20, 18)) 
-        fig2.patch.set_facecolor('#0a0a0a')
-        
-        axes[0, 0].imshow(image)
-        axes[0, 0].set_title("Original Image", fontsize=17, fontweight='700', color='#e0e0e0', pad=15)
-        axes[0, 0].axis('off')
-        
-        axes[0, 1].imshow(image)
-        axes[0, 1].imshow(attr_np, cmap='jet', alpha=0.6, interpolation='bilinear')
-        axes[0, 1].set_title("EfficientNet-B4 Focus", fontsize=17, fontweight='700', color='#e0e0e0', pad=15)
-        axes[0, 1].axis('off')
-        
-        axes[1, 0].imshow(image)
-        axes[1, 0].imshow(attr_resnet_np, cmap='hot', alpha=0.6, interpolation='bilinear')
-        axes[1, 0].set_title("ResNet152 Focus", fontsize=17, fontweight='700', color='#e0e0e0', pad=15)
-        axes[1, 0].axis('off')
-        
-        axes[1, 1].imshow(image)
-        axes[1, 1].imshow(attr_avg, cmap='viridis', alpha=0.6, interpolation='gaussian')
-        axes[1, 1].contour(attr_avg, levels=6, colors='white', linewidths=2, alpha=0.9)
-        axes[1, 1].set_title("Ensemble Average + Contours", fontsize=17, fontweight='700', color='#e0e0e0', pad=15)
-        axes[1, 1].axis('off')
-        
-        plt.tight_layout()
-        
-        buf2 = BytesIO()
-        plt.savefig(buf2, format='png', dpi=140, bbox_inches='tight', facecolor='#0a0a0a')
-        buf2.seek(0)
-        detailed_heatmap = Image.open(buf2)
-        plt.close(fig2)
-
-        # Enhanced prediction card with model breakdown
-        badge = "high" if confidence > 0.8 else "medium" if confidence > 0.5 else "low"
-        badge_text = "High Confidence" if confidence > 0.8 else "Medium Confidence" if confidence > 0.5 else "Low Confidence"
-        badge_icon = "🎯" if confidence > 0.8 else "⚡" if confidence > 0.5 else "⚠️"
-
-        # Individual model predictions for transparency
-        individual_preds = []
-        for i, (pred, name) in enumerate(zip(predictions[:3], model_names[:3])):
-            top1_idx = pred[0].argmax().item()
-            top1_prob = pred[0, top1_idx].item() * 100
-            individual_preds.append(f"""
-                <div class='model-pred'>
-                    <span class='model-name'>{name}</span>
-                    <span class='model-class'>{IMAGENET_LABELS[top1_idx]}</span>
-                    <span class='model-conf'>{top1_prob:.1f}%</span>
-                </div>
-            """)
-
-        top5_html = "<div class='top5-grid'>"
-        icons = ["🥇", "🥈", "🥉", "4️⃣", "5️⃣"]
-        for i, (prob, idx) in enumerate(zip(top10_prob[0][:5], top10_idx[0][:5])):
-            pct = prob.item() * 100
-            top5_html += f"""
-            <div class='top5-row'>
-                <span class='rank'>{icons[i]}</span>
-                <span class='label'>{IMAGENET_LABELS[idx.item()]}</span>
-                <div class='bar-wrap'><div class='bar' style='width:{pct}%'></div></div>
-                <span class='pct'>{pct:.2f}%</span>
-            </div>"""
-        top5_html += "</div>"
-
-        prediction_text = f"""
-<div class="result-card">
-    <div class="pred-header">
-        <h2 class="pred-label">{IMAGENET_LABELS[pred_class]}</h2>
-        <div class="badge badge-{badge}">{badge_icon} {badge_text}</div>
-    </div>
-    <div class="conf-score">{confidence*100:.2f}%</div>
-    <div class="ensemble-tag">🔬 Multi-Model Ensemble (3 Networks + TTA)</div>
-    <div class="divider"></div>
-    <div class="model-breakdown">
-        <h3>Individual Model Predictions:</h3>
-        {''.join(individual_preds)}
-    </div>
-    <div class="divider"></div>
-    {top5_html}
-</div>"""
-        
-        return prediction_text, result_image, detailed_heatmap
-
-    except Exception as e:
-        return f"<div class='error-msg'>⚠️ Error: {str(e)}</div>", None, None
-
-
-custom_css = """
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap');
-* { box-sizing: border-box; margin: 0; padding: 0; }
-body, .gradio-container { margin: 0 !important; padding: 0 !important; width: 100vw !important; min-height: 100vh !important; max-width: 100vw !important; background: linear-gradient(135deg, #0a0a0a 0%, #1a1a1a 50%, #0f0f0f 100%) !important; font-family: 'Inter', sans-serif !important; color: #e0e0e0 !important; overflow-x: hidden !important; }
-.gradio-container { padding: 0 !important; }
-.main-wrapper { padding: 1.5rem; max-width: 1920px; margin: 0 auto; position: relative; z-index: 2; }
-.hero-header { text-align: center; padding: 2rem 1rem 1.5rem; margin-bottom: 1.5rem; position: relative; }
-.hero-header::before { content: ''; position: absolute; top: 0; left: 50%; transform: translateX(-50%); width: 300px; height: 300px; background: radial-gradient(circle, rgba(59, 130, 246, 0.15), transparent); filter: blur(80px); z-index: -1; }
-.hero-header h1 { font-size: clamp(2rem, 5vw, 3.5rem); font-weight: 900; background-color: #d8b4fe; -webkit-background-clip: text; -webkit-text-fill-color: transparent; margin: 0 0 0.5rem; letter-spacing: -1px; }
-.hero-header .subtitle { font-size: clamp(0.95rem, 2vw, 1.2rem); color: #808080; font-weight: 400; margin: 0 0 0.5rem; }
-.hero-header .model-tag { display: inline-block; background: #93c5fd; border: 1px solid rgba(59, 130, 246, 0.3); color: #3b82f6; padding: 0.5rem 1.5rem; border-radius: 50px; font-size: 0.85rem; font-weight: 700; letter-spacing: 0.5px; margin-top: 0.5rem; }
-.top-section { display: grid; grid-template-columns: 400px 1fr; gap: 1.25rem; margin-bottom: 1.25rem; }
-.upload-panel, .results-panel, .viz-section { background: rgba(20, 20, 20, 0.8); border: 1px solid rgba(255, 255, 255, 0.1); border-radius: 24px; padding: 1.5rem; backdrop-filter: blur(20px); box-shadow: 0 8px 32px rgba(0, 0, 0, 0.4); }
-.section-label { font-size: 1.1rem; font-weight: 700; background: #93c5fd; -webkit-background-clip: text; -webkit-text-fill-color: transparent; margin: 0 0 1rem; text-align: center; letter-spacing: 0.5px; }
-#input-image { border: 2px dashed rgba(59, 130, 246, 0.4) !important; border-radius: 20px !important; background: rgba(10, 10, 10, 0.6) !important; height: 320px !important; transition: all 0.3s ease; }
-#input-image:hover { border-color: #3b82f6 !important; background: rgba(20, 20, 30, 0.8) !important; transform: scale(1.02); box-shadow: 0 0 30px rgba(59, 130, 246, 0.2); }
-.btn-row { display: flex; gap: 0.75rem; margin-top: 1rem; }
-.gr-button { border-radius: 14px !important; font-weight: 700 !important; height: 50px !important; font-size: 0.95rem !important; transition: all 0.3s ease !important; border: none !important; letter-spacing: 0.5px; text-transform: uppercase; }
-.gr-button-primary { background: linear-gradient(135deg, #3b82f6, #8b5cf6) !important; color: white !important; box-shadow: 0 4px 20px rgba(59, 130, 246, 0.4) !important; }
-.gr-button-primary:hover { transform: translateY(-3px) !important; box-shadow: 0 8px 30px rgba(59, 130, 246, 0.6) !important; }
-.gr-button-secondary { background: rgba(40, 40, 40, 0.8) !important; color: #a0a0a0 !important; border: 1px solid rgba(255, 255, 255, 0.1) !important; }
-.pred-header { display: flex; align-items: center; justify-content: space-between; flex-wrap: wrap; gap: 1rem; margin-bottom: 0.75rem; }
-.pred-label { font-size: clamp(1.5rem, 3vw, 2rem); font-weight: 900; color: #ffffff; margin: 0; letter-spacing: -0.5px; }
-.badge { padding: 0.5rem 1.25rem; border-radius: 50px; font-size: 0.875rem; font-weight: 700; text-transform: uppercase; letter-spacing: 0.5px; box-shadow: 0 4px 15px rgba(0, 0, 0, 0.3); }
-.badge-high { background: linear-gradient(135deg, #10b981, #059669); color: white; }
-.badge-medium { background: linear-gradient(135deg, #f59e0b, #d97706); color: white; }
-.badge-low { background: linear-gradient(135deg, #ef4444, #dc2626); color: white; }
-.conf-score { font-size: clamp(2rem, 5vw, 3rem); font-weight: 900; background: linear-gradient(135deg, #3b82f6, #8b5cf6); -webkit-background-clip: text; -webkit-text-fill-color: transparent; margin-bottom: 1rem; letter-spacing: -1px; }
-.ensemble-tag { background: rgba(16, 185, 129, 0.15); border: 1px solid rgba(16, 185, 129, 0.3); color: #10b981; padding: 0.5rem 1rem; border-radius: 12px; font-size: 0.8rem; font-weight: 700; text-align: center; margin-bottom: 1rem; }
-.model-breakdown { background: rgba(30, 30, 30, 0.6); padding: 1rem; border-radius: 12px; margin-bottom: 1rem; }
-.model-breakdown h3 { font-size: 0.95rem; color: #a0a0a0; margin-bottom: 0.75rem; font-weight: 600; }
-.model-pred { display: grid; grid-template-columns: 140px 1fr auto; gap: 0.75rem; align-items: center; padding: 0.5rem; border-radius: 8px; background: rgba(40, 40, 40, 0.4); margin-bottom: 0.5rem; }
-.model-name { color: #3b82f6; font-weight: 700; font-size: 0.85rem; }
-.model-class { color: #e0e0e0; font-size: 0.875rem; font-weight: 500; }
-.model-conf { color: #10b981; font-weight: 700; font-size: 0.875rem; }
-.divider { height: 2px; background: linear-gradient(90deg, transparent, rgba(59, 130, 246, 0.3), transparent); margin: 1.5rem 0; }
-.top5-grid { display: flex; flex-direction: column; gap: 0.875rem; }
-.top5-row { display: grid; grid-template-columns: 40px 1fr auto 80px; align-items: center; gap: 0.875rem; font-size: 0.95rem; padding: 0.5rem; border-radius: 12px; background: rgba(30, 30, 30, 0.5); transition: all 0.3s ease; }
-.top5-row:hover { background: rgba(40, 40, 40, 0.7); transform: translateX(5px); }
-.rank { font-size: 1.5rem; text-align: center; }
-.label { color: #e0e0e0; font-weight: 600; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }
-.bar-wrap { background: rgba(40, 40, 40, 0.8); height: 10px; border-radius: 5px; overflow: hidden; min-width: 100px; box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.3); }
-.bar { background: linear-gradient(90deg, #3b82f6, #8b5cf6); height: 100%; transition: width 1s ease; border-radius: 5px; box-shadow: 0 0 10px rgba(59, 130, 246, 0.5); }
-.pct { color: #3b82f6; font-weight: 700; font-size: 0.9rem; text-align: right; }
-#result-image, #detailed-heatmap { border-radius: 16px !important; overflow: hidden; width: 100% !important; height: auto !important; min-height: 500px !important; box-shadow: 0 8px 32px rgba(0, 0, 0, 0.5); object-fit: contain !important; }
-.placeholder { text-align: center; padding: 4rem 1.5rem; color: #606060; font-size: 1.1rem; line-height: 1.6; }
-.placeholder strong { color: #3b82f6; }
-.error-msg { color: #ef4444; background: rgba(239, 68, 68, 0.1); padding: 1.5rem; border-radius: 16px; text-align: center; border: 1px solid rgba(239, 68, 68, 0.3); }
-footer, .footer { display: none !important; }
-::-webkit-scrollbar { width: 10px; }
-::-webkit-scrollbar-track { background: rgba(20, 20, 20, 0.5); }
-::-webkit-scrollbar-thumb { background: rgba(59, 130, 246, 0.5); border-radius: 5px; }
-@media (max-width: 768px) { 
-    .top-section { grid-template-columns: 1fr; } 
-    #input-image { height: 240px !important; } 
-    .top5-row { grid-template-columns: 35px 1fr 70px; } 
-    .bar-wrap { grid-column: 1 / -1; margin-top: 0.375rem; }
-    #result-image { min-height: 600px !important; max-height: none !important; }
-    #detailed-heatmap { min-height: 450px !important; max-height: none !important; }
-    .viz-section { padding: 1rem; }
-    .section-label { font-size: 1rem; }
-    .model-pred { grid-template-columns: 1fr; gap: 0.25rem; }
-}
-@media (max-width: 480px) {
-    .main-wrapper { padding: 1rem; }
-    #result-image { min-height: 550px !important; }
-    #detailed-heatmap { min-height: 400px !important; }
-}
-"""
-
-
-with gr.Blocks(css=custom_css, theme=gr.themes.Base(), title="Advanced XAI Classifier") as demo:
-    gr.HTML('<link rel="icon" href="https://res.cloudinary.com/ddn0xuwut/image/upload/v1761284764/encryption_hc0fxo.png" type="image/png">')
-
-    with gr.Column(elem_classes="main-wrapper"):
-        gr.HTML('''
-            <div class="hero-header">
-                <h1>Advanced XAI Classifier</h1>
-                <p class="subtitle">Multi-Model Ensemble with Test-Time Augmentation</p>
-                <div class="model-tag">⚡ EfficientNet-B4 + ResNet152 + ConvNeXt</div>
-            </div>
-        ''')
-
-        with gr.Row(elem_classes="top-section"):
-            with gr.Column(scale=0, min_width=400, elem_classes="upload-panel"):
-                gr.HTML("<div class='section-label'>📤 Upload Image</div>")
-                input_image = gr.Image(type="pil", label=None, elem_id="input-image", show_label=False, container=False)
-                with gr.Row(elem_classes="btn-row"):
-                    predict_btn = gr.Button("🚀 Analyze", variant="primary", size="lg", scale=2)
-                    clear_btn = gr.ClearButton([input_image], value="🗑️ Clear", size="lg", scale=1)
-
-            with gr.Column(scale=1, elem_classes="results-panel"):
-                output_text = gr.HTML('<div class="placeholder"><strong>👋 Welcome to Advanced XAI!</strong><br><br>This classifier uses 3 state-of-the-art models:<br>• EfficientNet-B4 (40%)<br>• ResNet152 (30%)<br>• ConvNeXt-Base (25%)<br>• + Test-Time Augmentation (5%)<br><br>Upload an image to see the magic! ✨</div>')
-
-        with gr.Column(elem_classes="viz-section"):
-            gr.HTML("<div class='section-label'>🎯 Ensemble Visual Explainability</div>")
-            output_image = gr.Image(label=None, type="pil", show_label=False, elem_id="result-image", container=False)
-
-        with gr.Column(elem_classes="viz-section"):
-            gr.HTML("<div class='section-label'>🔬 Model Comparison Analysis</div>")
-            detailed_heatmap = gr.Image(label=None, type="pil", show_label=False, elem_id="detailed-heatmap", container=False)
-
-    predict_btn.click(fn=predict_and_explain, inputs=[input_image], outputs=[output_text, output_image, detailed_heatmap])
+    analyze_btn.click(fn=minimal_predict, inputs=[image_input], outputs=[text_output, image_output])
 
-if __name__ == "__main__":
-    demo.launch(share=False, show_error=True)
+demo.launch(share=False)