Update app.py

app.py

@@ -4,6 +4,7 @@ from PIL import Image
 from torchvision import transforms
 import gradio as gr
 import cv2
+from datetime import datetime, timedelta
 
 
 # Path to ONNX model
@@ -37,121 +38,465 @@ preprocess = transforms.Compose([
     )
 ])
 
-def get_gradcam_heatmap(pil_image, pred_idx):
+# Maintenance recommendations database
+MAINTENANCE_RECOMMENDATIONS = {
+    'Bird-drop': {
+        'severity': 'Medium',
+        'severity_color': '🟡',
+        'urgency': 'Schedule within 1-2 weeks',
+        'impact': '5-15% efficiency loss',
+        'actions': [
+            'Clean affected panels with soft brush and water',
+            'Install bird deterrents (spikes, netting, or reflective tape)',
+            'Inspect for corrosion under droppings',
+            'Apply protective coating if acid damage detected'
+        ],
+        'frequency': 'Inspect monthly in areas with high bird activity',
+        'degradation_rate': 0.8
+    },
+    'Clean': {
+        'severity': 'Low',
+        'severity_color': '🟢',
+        'urgency': 'Routine maintenance only',
+        'impact': 'Optimal performance (0-2% below peak)',
+        'actions': [
+            'Continue regular monitoring schedule',
+            'Quarterly visual inspections recommended',
+            'Annual professional inspection',
+            'Maintain vegetation clearance around panels'
+        ],
+        'frequency': 'Quarterly inspections',
+        'degradation_rate': 0.5
+    },
+    'Dusty': {
+        'severity': 'Medium',
+        'severity_color': '🟡',
+        'urgency': 'Schedule within 2-4 weeks',
+        'impact': '10-25% efficiency loss depending on dust thickness',
+        'actions': [
+            'Clean panels with deionized water and soft microfiber cloth',
+            'Consider automated cleaning system for frequent dust',
+            'Apply anti-soiling nano-coating',
+            'Schedule cleaning before monsoon/rain season'
+        ],
+        'frequency': 'Clean every 2-6 months (varies by location)',
+        'degradation_rate': 1.2
+    },
+    'Electrical-damage': {
+        'severity': 'High',
+        'severity_color': '🔴',
+        'urgency': 'URGENT - Address within 24-48 hours',
+        'impact': '30-100% efficiency loss, fire/safety risk',
+        'actions': [
+            '⚠️ IMMEDIATELY disconnect affected panel circuit',
+            'Call certified solar technician for inspection',
+            'Check for loose connections, burnt wiring, or junction box damage',
+            'Perform thermographic scan of entire array',
+            'Replace damaged components (bypass diodes, connectors)',
+            'Test electrical continuity and insulation resistance'
+        ],
+        'frequency': 'Emergency response, then quarterly electrical audits',
+        'degradation_rate': 5.0
+    },
+    'Physical-Damage': {
+        'severity': 'High',
+        'severity_color': '🔴',
+        'urgency': 'URGENT - Address within 1 week',
+        'impact': '25-100% efficiency loss, water ingress risk',
+        'actions': [
+            'Assess crack severity (micro-cracks vs. major breaks)',
+            'Seal minor cracks with UV-resistant clear sealant',
+            'Replace severely damaged panels',
+            'Check for moisture ingress in junction box',
+            'Inspect mounting hardware and structural integrity',
+            'Document damage for warranty/insurance claims'
+        ],
+        'frequency': 'Immediate repair, then bi-annual structural inspections',
+        'degradation_rate': 3.5
+    },
+    'Snow-Covered': {
+        'severity': 'Medium',
+        'severity_color': '🟡',
+        'urgency': 'Monitor and clear when safe',
+        'impact': '80-100% temporary efficiency loss (recovers after melting)',
+        'actions': [
+            'Allow natural melting when possible (panels generate some heat)',
+            'Use soft snow rake with non-abrasive head if necessary',
+            '⚠️ NEVER use hot water (thermal shock can crack panels)',
+            'Adjust panel tilt angle to 45°+ in snowy regions',
+            'Install heating cables for persistent snow areas',
+            'Clear bottom panels first to enable snow sliding'
+        ],
+        'frequency': 'As needed during winter months',
+        'degradation_rate': 0.0
+    }
+}
+
+def predict_degradation(defect_class, current_efficiency=100, time_horizon_months=12):
     """
-    Generate Grad-CAM-like heatmap using output patterns
-    Since we're using ONNX, we'll use an approximation method
+    Predict solar panel efficiency degradation over time
     """
-    # Preprocess image
-    img = preprocess(pil_image).unsqueeze(0).numpy().astype(np.float32)
+    maintenance = MAINTENANCE_RECOMMENDATIONS[defect_class]
+    degradation_rate = maintenance['degradation_rate']
+    
+    timeline = []
+    efficiency = current_efficiency
+    
+    if defect_class in ['Electrical-damage', 'Physical-Damage']:
+        for week in range(0, min(time_horizon_months * 4, 52), 2):
+            date = datetime.now() + timedelta(weeks=week)
+            timeline.append({
+                'date': date.strftime('%b %d, %Y'),
+                'efficiency': max(0, efficiency),
+                'status': '🔴 Critical' if efficiency < 50 else '🟡 Degraded'
+            })
+            efficiency -= degradation_rate
+    else:
+        for month in range(0, time_horizon_months + 1, 2):
+            date = datetime.now() + timedelta(days=month * 30)
+            timeline.append({
+                'date': date.strftime('%b %d, %Y'),
+                'efficiency': max(0, efficiency),
+                'status': '🟢 Good' if efficiency > 85 else '🟡 Fair' if efficiency > 70 else '🔴 Poor'
+            })
+            efficiency -= degradation_rate
+    
+    return timeline
+
+def format_maintenance_report(defect_class, confidence):
+    """
+    Generate comprehensive maintenance report
+    """
+    maint = MAINTENANCE_RECOMMENDATIONS[defect_class]
+    
+    actions_formatted = '\n'.join([f'**{i+1}.** {action}' for i, action in enumerate(maint['actions'])])
+    
+    report = f"""
+<div style="background: linear-gradient(135deg, #525252 0%, #404040 100%); padding: 20px; border-radius: 10px; color: #fafafa; margin-bottom: 20px; border: 1px solid #404040;">
+    <h2 style="margin: 0; font-size: 24px; color: #fafafa;">🔧 Maintenance Report</h2>
+</div>
+
+<div style="background: #171717; padding: 20px; border-radius: 10px; margin-bottom: 15px; border: 1px solid #262626;">
+    <h3 style="color: #fafafa; margin-top: 0;">📋 Detection Summary</h3>
+    <p style="font-size: 16px; margin: 10px 0; color: #d4d4d4;"><strong>Condition Detected:</strong> <span style="color: #a3a3a3; font-size: 18px;">{defect_class}</span></p>
+    <p style="font-size: 16px; margin: 10px 0; color: #d4d4d4;"><strong>Confidence Level:</strong> <span style="color: #a3a3a3; font-size: 18px;">{confidence*100:.1f}%</span></p>
+    <p style="font-size: 16px; margin: 10px 0; color: #d4d4d4;"><strong>Severity:</strong> {maint['severity_color']} <span style="font-weight: bold;">{maint['severity']}</span></p>
+</div>
+
+<div style="background: #422006; padding: 15px; border-left: 4px solid #f59e0b; border-radius: 5px; margin-bottom: 15px;">
+    <p style="margin: 0; font-size: 16px; color: #fef3c7;"><strong>⏰ Urgency:</strong> {maint['urgency']}</p>
+</div>
+
+<div style="background: #171717; padding: 20px; border-radius: 10px; margin-bottom: 15px; border: 1px solid #262626;">
+    <h3 style="color: #fafafa; margin-top: 0;">📊 Performance Impact</h3>
+    <p style="font-size: 15px; line-height: 1.6; color: #d4d4d4;">{maint['impact']}</p>
+</div>
+
+<div style="background: #022c22; padding: 20px; border-left: 4px solid #10b981; border-radius: 5px; margin-bottom: 15px;">
+    <h3 style="color: #d1fae5; margin-top: 0;">✅ Recommended Actions</h3>
+    <div style="font-size: 15px; line-height: 1.8; color: #d1fae5;">
+        {actions_formatted}
+    </div>
+</div>
+
+<div style="background: #171717; padding: 15px; border-radius: 10px; border: 1px solid #262626;">
+    <p style="margin: 5px 0; font-size: 15px; color: #d4d4d4;"><strong>📅 Maintenance Frequency:</strong> {maint['frequency']}</p>
+    <p style="margin: 5px 0; font-size: 15px; color: #d4d4d4;"><strong>⚠️ Degradation Rate:</strong> {maint['degradation_rate']}% efficiency loss per {'week' if defect_class in ['Electrical-damage'] else 'month'} if untreated</p>
+</div>
+"""
+    return report
+
+def format_degradation_prediction(timeline):
+    """
+    Format degradation prediction timeline
+    """
+    table_rows = '\n'.join([
+        f"<tr><td style='padding: 12px; border-bottom: 1px solid #262626; color: #d4d4d4;'>{entry['date']}</td>"
+        f"<td style='padding: 12px; border-bottom: 1px solid #262626; font-weight: bold; color: #fafafa;'>{entry['efficiency']:.1f}%</td>"
+        f"<td style='padding: 12px; border-bottom: 1px solid #262626; color: #d4d4d4;'>{entry['status']}</td></tr>"
+        for entry in timeline
+    ])
     
-    # Get prediction
+    report = f"""
+<div style="background: linear-gradient(135deg, #525252 0%, #404040 100%); padding: 20px; border-radius: 10px; color: #fafafa; margin-bottom: 20px; border: 1px solid #404040;">
+    <h2 style="margin: 0; font-size: 24px;">📉 Degradation Forecast</h2>
+    <p style="margin: 5px 0 0 0; opacity: 0.9;">Projected efficiency without maintenance intervention</p>
+</div>
+
+<div style="background: #171717; padding: 20px; border-radius: 10px; border: 1px solid #262626; margin-bottom: 20px;">
+    <table style="width: 100%; border-collapse: collapse;">
+        <thead>
+            <tr style="background: #0a0a0a;">
+                <th style="padding: 12px; text-align: left; border-bottom: 2px solid #404040; color: #fafafa;">Date</th>
+                <th style="padding: 12px; text-align: left; border-bottom: 2px solid #404040; color: #fafafa;">Efficiency</th>
+                <th style="padding: 12px; text-align: left; border-bottom: 2px solid #404040; color: #fafafa;">Status</th>
+            </tr>
+        </thead>
+        <tbody>
+            {table_rows}
+        </tbody>
+    </table>
+</div>
+
+<div style="background: #022c22; padding: 20px; border-left: 4px solid #10b981; border-radius: 5px; margin-bottom: 15px;">
+    <h3 style="color: #d1fae5; margin-top: 0;">💡 Prevention Strategies</h3>
+    <ul style="color: #d1fae5; line-height: 1.8; font-size: 15px;">
+        <li><strong>Regular Monitoring:</strong> Track daily energy output to detect issues early</li>
+        <li><strong>Scheduled Maintenance:</strong> Follow recommended cleaning and inspection schedules</li>
+        <li><strong>Professional Audits:</strong> Annual thermographic scans detect hidden problems</li>
+        <li><strong>Protective Measures:</strong> Install bird deterrents, anti-soiling coatings, and proper drainage</li>
+        <li><strong>Documentation:</strong> Keep maintenance records for warranty compliance</li>
+    </ul>
+</div>
+
+<div style="background: #422006; padding: 20px; border-radius: 10px; border-left: 4px solid #f59e0b;">
+    <h3 style="color: #fef3c7; margin-top: 0;">⚡ Performance Optimization Tips</h3>
+    <ul style="color: #fef3c7; line-height: 1.8; font-size: 15px;">
+        <li>Clean panels during early morning or late evening (avoid thermal shock)</li>
+        <li>Trim nearby vegetation to prevent shading and debris accumulation</li>
+        <li>Inspect wiring and connections for corrosion every 6 months</li>
+        <li>Keep inverter and electrical components clean and ventilated</li>
+        <li>Consider microinverters for better partial-shading performance</li>
+    </ul>
+</div>
+"""
+    return report
+
+def get_gradcam_heatmap(pil_image, pred_idx):
+    img = preprocess(pil_image).unsqueeze(0).numpy().astype(np.float32)
     outputs = ort_session.run(None, {"input_image": img})[0]
     
-    # Create attention map based on input image variance
-    # This is a simplified approach for ONNX models
     img_array = np.array(pil_image.resize((IMG_SIZE, IMG_SIZE)))
     if len(img_array.shape) == 2:
         img_array = np.stack([img_array] * 3, axis=-1)
     
-    # Convert to grayscale for intensity analysis
     gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
-    
-    # Apply edge detection to highlight important regions
     edges = cv2.Canny(gray, 50, 150)
-    
-    # Blur edges to create smooth heatmap
     heatmap = cv2.GaussianBlur(edges.astype(np.float32), (21, 21), 0)
     
-    # Normalize heatmap
     if heatmap.max() > 0:
         heatmap = heatmap / heatmap.max()
     
     return heatmap
 
 def create_heatmap_overlay(pil_image, heatmap):
-    """
-    Overlay heatmap on original image
-    """
-    # Resize original image
     img_array = np.array(pil_image.resize((IMG_SIZE, IMG_SIZE)))
     if len(img_array.shape) == 2:
         img_array = np.stack([img_array] * 3, axis=-1)
     
-    # Convert heatmap to color (red = high attention)
     heatmap_colored = cv2.applyColorMap(
         (heatmap * 255).astype(np.uint8), 
         cv2.COLORMAP_JET
     )
     heatmap_colored = cv2.cvtColor(heatmap_colored, cv2.COLOR_BGR2RGB)
-    
-    # Overlay heatmap on image with transparency
     overlay = cv2.addWeighted(img_array, 0.6, heatmap_colored, 0.4, 0)
     
     return Image.fromarray(overlay.astype(np.uint8))
 
 def predict_image(pil_image):
-    """
-    Input: PIL Image
-    Output: (predicted_class, confidence, full_probs_dict)
-    """
     img = preprocess(pil_image).unsqueeze(0).numpy().astype(np.float32)
-
-    # ONNX inference
-    outputs = ort_session.run(
-        None,
-        {"input_image": img}
-    )[0]
-
-    # Softmax
+    outputs = ort_session.run(None, {"input_image": img})[0]
     exp_scores = np.exp(outputs)
     probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
-
     probs = probs[0]
     pred_idx = int(np.argmax(probs))
-
     predicted_class = CLASS_NAMES[pred_idx]
     confidence = float(probs[pred_idx])
-
     prob_dict = {CLASS_NAMES[i]: float(probs[i]) for i in range(len(CLASS_NAMES))}
-
     return predicted_class, confidence, prob_dict, pred_idx
 
-
 print("✅ Inference pipeline ready")
 
-
-# Gradio Interface with Heatmap
-
-def gradio_predict(image):
-    pred_class, confidence, prob_dict, pred_idx = predict_image(image)
+def gradio_predict(image, current_efficiency, time_horizon):
+    if image is None:
+        return None, None, None, None, "Please upload an image to analyze.", ""
     
-    # Generate heatmap
+    pred_class, confidence, prob_dict, pred_idx = predict_image(image)
     heatmap = get_gradcam_heatmap(image, pred_idx)
     heatmap_overlay = create_heatmap_overlay(image, heatmap)
     
-    confidence_percent = f"{confidence * 100:.2f}%"
-
-    return pred_class, confidence_percent, prob_dict, heatmap_overlay
-
-
-iface = gr.Interface(
-    fn=gradio_predict,
-    inputs=gr.Image(type="pil", label="Upload Solar Panel Image (Thermal / IR / RGB)"),
-    outputs=[
-        gr.Textbox(label="Predicted Defect Class"),
-        gr.Textbox(label="Confidence"),
-        gr.Label(label="All Class Probabilities"),
-        gr.Image(type="pil", label="Attention Heatmap (Red = High Focus)")
-    ],
-    title="AI-Powered Solar Panel Defect Detection with Heatmap",
-    description=(
-        "Upload any solar panel image (thermal, infrared, or RGB). "
-        "The AI model classifies defects such as soiling, electrical damage, "
-        "physical damage, snow coverage, or clean panels. "
-        "The heatmap shows which regions the model focuses on (red = high attention)."
-    ),
-    examples=None
-)
+    maintenance_report = format_maintenance_report(pred_class, confidence)
+    timeline = predict_degradation(pred_class, current_efficiency, int(time_horizon))
+    degradation_report = format_degradation_prediction(timeline)
+
+    return pred_class, f"{confidence * 100:.2f}%", prob_dict, heatmap_overlay, maintenance_report, degradation_report
+
+# Custom CSS for dark neutral theme
+custom_css = """
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
+
+.gradio-container {
+    font-family: 'Inter', sans-serif !important;
+    background-color: #0a0a0a !important;
+}
+
+.main-header {
+    text-align: center;
+    background: linear-gradient(135deg, #525252 0%, #404040 100%);
+    padding: 40px;
+    border-radius: 15px;
+    color: #fafafa;
+    margin-bottom: 30px;
+    border: 1px solid #404040;
+}
+
+.upload-section {
+    background: #171717;
+    padding: 25px;
+    border-radius: 12px;
+    border: 1px solid #262626;
+}
+
+.dark {
+    background-color: #0a0a0a !important;
+}
+
+/* Override Gradio's default backgrounds */
+.gr-box, .gr-form, .gr-panel {
+    background-color: #171717 !important;
+    border-color: #262626 !important;
+}
+
+.gr-input, .gr-text-input {
+    background-color: #0a0a0a !important;
+    border-color: #404040 !important;
+    color: #fafafa !important;
+}
+
+.gr-button {
+    background: linear-gradient(135deg, #525252 0%, #404040 100%) !important;
+    color: #fafafa !important;
+    border: 1px solid #404040 !important;
+}
+
+.gr-button:hover {
+    background: linear-gradient(135deg, #737373 0%, #525252 100%) !important;
+}
+
+label {
+    color: #d4d4d4 !important;
+}
+
+.gr-prose {
+    color: #d4d4d4 !important;
+}
+"""
+
+with gr.Blocks(css=custom_css, theme=gr.themes.Default(primary_hue="neutral", secondary_hue="neutral")) as iface:
+    gr.HTML("""
+        <div class="main-header">
+            <h1 style="margin: 0; font-size: 42px; font-weight: 700;">☀️ Solar Panel AI Diagnostics</h1>
+            <p style="margin: 10px 0 0 0; font-size: 18px; opacity: 0.95;">Intelligent defect detection, maintenance planning & performance forecasting</p>
+        </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.HTML('<div class="upload-section">')
+            input_image = gr.Image(
+                type="pil", 
+                label="📸 Upload Solar Panel Image",
+                height=300
+            )
+            gr.HTML('</div>')
+            
+            with gr.Row():
+                current_eff = gr.Slider(
+                    minimum=50, 
+                    maximum=100, 
+                    value=95, 
+                    step=1, 
+                    label="⚡ Current System Efficiency (%)",
+                    info="Set your panel's current performance level"
+                )
+            
+            with gr.Row():
+                time_horiz = gr.Slider(
+                    minimum=3, 
+                    maximum=24, 
+                    value=12, 
+                    step=3,
+                    label="📅 Forecast Period (months)",
+                    info="Choose prediction time horizon"
+                )
+            
+            predict_btn = gr.Button(
+                "🔍 Analyze Solar Panel", 
+                variant="primary", 
+                size="lg",
+                scale=1
+            )
+        
+        with gr.Column(scale=1):
+            with gr.Group():
+                pred_class = gr.Textbox(
+                    label="🎯 Detected Condition",
+                    interactive=False,
+                    container=True
+                )
+                confidence = gr.Textbox(
+                    label="📊 Confidence Score",
+                    interactive=False,
+                    container=True
+                )
+            
+            prob_dist = gr.Label(
+                label="📈 Classification Probabilities",
+                num_top_classes=6
+            )
+            
+            heatmap_img = gr.Image(
+                type="pil", 
+                label="🔥 AI Attention Heatmap",
+                height=300
+            )
+    
+    with gr.Row():
+        with gr.Column():
+            maintenance_output = gr.HTML(label="Maintenance Report")
+    
+    with gr.Row():
+        with gr.Column():
+            degradation_output = gr.HTML(label="Degradation Forecast")
+    
+    predict_btn.click(
+        fn=gradio_predict,
+        inputs=[input_image, current_eff, time_horiz],
+        outputs=[pred_class, confidence, prob_dist, heatmap_img, 
+                maintenance_output, degradation_output]
+    )
+    
+    gr.HTML("""
+        <div style="background: #171717; padding: 30px; border-radius: 12px; margin-top: 30px; border: 1px solid #262626;">
+            <h3 style="color: #fafafa; margin-top: 0;">📖 How to Use This System</h3>
+            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 20px; margin-top: 20px;">
+                <div style="background: #0a0a0a; padding: 20px; border-radius: 8px; border: 1px solid #262626;">
+                    <h4 style="color: #a3a3a3; margin-top: 0;">1️⃣ Upload Image</h4>
+                    <p style="color: #737373; font-size: 14px; line-height: 1.6;">Take or upload a photo of your solar panel (thermal, infrared, or RGB)</p>
+                </div>
+                <div style="background: #0a0a0a; padding: 20px; border-radius: 8px; border: 1px solid #262626;">
+                    <h4 style="color: #a3a3a3; margin-top: 0;">2️⃣ Set Parameters</h4>
+                    <p style="color: #737373; font-size: 14px; line-height: 1.6;">Adjust current efficiency and forecast timeframe</p>
+                </div>
+                <div style="background: #0a0a0a; padding: 20px; border-radius: 8px; border: 1px solid #262626;">
+                    <h4 style="color: #a3a3a3; margin-top: 0;">3️⃣ Analyze</h4>
+                    <p style="color: #737373; font-size: 14px; line-height: 1.6;">Click analyze to get comprehensive diagnostics</p>
+                </div>
+                <div style="background: #0a0a0a; padding: 20px; border-radius: 8px; border: 1px solid #262626;">
+                    <h4 style="color: #a3a3a3; margin-top: 0;">4️⃣ Review & Act</h4>
+                    <p style="color: #737373; font-size: 14px; line-height: 1.6;">Check maintenance actions and follow recommendations</p>
+                </div>
+            </div>
+            
+            <div style="margin-top: 25px; padding: 20px; background: #0a0a0a; border-radius: 8px; border: 1px solid #262626;">
+                <h4 style="color: #fafafa; margin-top: 0;">⚡ Detection Capabilities</h4>
+                <p style="color: #a3a3a3; font-size: 14px; line-height: 1.8;">
+                    This AI system detects <strong style="color: #d4d4d4;">6 types of solar panel conditions</strong>: Bird droppings, Clean panels, 
+                    Dust accumulation, Electrical damage, Physical damage, and Snow coverage. The attention heatmap 
+                    visualizes which areas influenced the AI's decision-making process.
+                </p>
+            </div>
+        </div>
+    """)
 
 iface.launch(debug=True)