Update app.py

app.py

@@ -211,14 +211,33 @@ class ForgeryDetector:
         # Create visualization
         overlay = self._create_overlay(original_image, results)
         
-        # Create gauge charts
-        gauge_dice = create_gauge_chart(MODEL_METRICS['segmentation']['dice'], 'Segmentation Dice')
-        gauge_accuracy = create_gauge_chart(MODEL_METRICS['classification']['overall_accuracy'], 'Classification Accuracy')
+        # Create HTML for model performance metrics
+        model_performance_html = f"""
+        <div style='padding:12px; border:1px solid #444; border-radius:10px; background:var(--background-fill-primary);'>
+            <p style='margin-top:0; margin-bottom:12px;'><b>Trained Model Performance:</b></p>
+            
+            <b>Segmentation Dice:</b>
+            <div style='width:100%; background:#333; height:12px; border-radius:6px; margin-bottom:12px;'>
+                <div style='width:{MODEL_METRICS['segmentation']['dice']*100:.1f}%; background:#4169E1; height:12px; border-radius:6px;'></div>
+            </div>
+            
+            <b>Classification Accuracy:</b>
+            <div style='width:100%; background:#333; height:12px; border-radius:6px; margin-bottom:6px;'>
+                <div style='width:{MODEL_METRICS['classification']['overall_accuracy']*100:.1f}%; background:#5cb85c; height:12px; border-radius:6px;'></div>
+            </div>
+            
+            <small style='opacity:0.8;'>
+                Dice: {MODEL_METRICS['segmentation']['dice']*100:.1f}% | 
+                IoU: {MODEL_METRICS['segmentation']['iou']*100:.1f}% | 
+                Accuracy: {MODEL_METRICS['classification']['overall_accuracy']*100:.1f}%
+            </small>
+        </div>
+        """
         
         # Create HTML response
         results_html = self._create_html_report(results)
         
-        return overlay, gauge_dice, gauge_accuracy, results_html
+        return overlay, model_performance_html, results_html
     
     def _create_overlay(self, image, results):
         """Create overlay visualization"""
@@ -312,15 +331,15 @@ def detect_forgery(file):
     try:
         if file is None:
             empty_html = "<div style='padding:12px; border:1px solid #d9534f; border-radius:8px;'>❌ <b>No file uploaded.</b></div>"
-            return None, None, None, empty_html
+            return None, empty_html, empty_html
         
         # Get file path
         file_path = file if isinstance(file, str) else file
         
         # Detect forgeries
-        overlay, gauge_dice, gauge_acc, results_html = detector.detect(file_path)
+        overlay, model_perf_html, results_html = detector.detect(file_path)
         
-        return overlay, gauge_dice, gauge_acc, results_html
+        return overlay, model_perf_html, results_html
     
     except Exception as e:
         import traceback
@@ -331,7 +350,7 @@ def detect_forgery(file):
             ❌ <b>Error:</b> {str(e)}
         </div>
         """
-        return None, None, None, error_html
+        return None, error_html, error_html
 
 
 # Custom CSS - subtle styling
@@ -397,8 +416,9 @@ with gr.Blocks(css=custom_css) as demo:
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown("### Model Performance")
-            gauge_dice = gr.Plot(label="Segmentation Dice Score")
-            gauge_accuracy = gr.Plot(label="Classification Accuracy")
+            model_performance = gr.HTML(
+                value="<i>Model performance will be shown after analysis.</i>"
+            )
         
         with gr.Column(scale=1):
             gr.Markdown("### Analysis Report")
@@ -420,13 +440,13 @@ with gr.Blocks(css=custom_css) as demo:
     analyze_btn.click(
         fn=detect_forgery,
         inputs=[input_file],
-        outputs=[output_image, gauge_dice, gauge_accuracy, output_html]
+        outputs=[output_image, model_performance, output_html]
     )
     
     clear_btn.click(
-        fn=lambda: (None, None, None, None, "<i>No analysis yet. Upload a document and click Analyze.</i>"),
+        fn=lambda: (None, None, "<i>Model performance will be shown after analysis.</i>", "<i>No analysis yet. Upload a document and click Analyze.</i>"),
         inputs=None,
-        outputs=[input_file, output_image, gauge_dice, gauge_accuracy, output_html]
+        outputs=[input_file, output_image, model_performance, output_html]
     )