Update app.py

app.py

@@ -13,6 +13,7 @@ import json
 from pathlib import Path
 import sys
 from typing import Dict, List, Tuple
+import plotly.graph_objects as go
 
 # Add src to path
 sys.path.insert(0, str(Path(__file__).parent))
@@ -52,6 +53,37 @@ MODEL_METRICS = {
 }
 
 
+def create_gauge_chart(value: float, title: str, max_value: float = 1.0) -> go.Figure:
+    """Create a subtle radial gauge chart"""
+    fig = go.Figure(go.Indicator(
+        mode="gauge+number",
+        value=value * 100,
+        domain={'x': [0, 1], 'y': [0, 1]},
+        title={'text': title, 'font': {'size': 14}},
+        number={'suffix': '%', 'font': {'size': 24}},
+        gauge={
+            'axis': {'range': [0, 100], 'tickwidth': 1},
+            'bar': {'color': '#4169E1', 'thickness': 0.7},
+            'bgcolor': 'rgba(0,0,0,0)',
+            'borderwidth': 0,
+            'steps': [
+                {'range': [0, 50], 'color': 'rgba(217, 83, 79, 0.1)'},
+                {'range': [50, 75], 'color': 'rgba(240, 173, 78, 0.1)'},
+                {'range': [75, 100], 'color': 'rgba(92, 184, 92, 0.1)'}
+            ]
+        }
+    ))
+    
+    fig.update_layout(
+        paper_bgcolor='rgba(0,0,0,0)',
+        plot_bgcolor='rgba(0,0,0,0)',
+        height=200,
+        margin=dict(l=20, r=20, t=40, b=20)
+    )
+    
+    return fig
+
+
 class ForgeryDetector:
     """Main forgery detection pipeline"""
     
@@ -85,12 +117,11 @@ class ForgeryDetector:
         """
         Detect forgeries in document image or PDF
         
-        Args:
-            image: PIL Image, numpy array, or path to PDF file
-            
         Returns:
             original_image: Original uploaded image
             overlay_image: Image with detection overlay
+            gauge_dice: Dice score gauge
+            gauge_accuracy: Accuracy gauge
             results_html: Detection results as HTML
         """
         # Handle PDF files
@@ -174,10 +205,14 @@ 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 response
         results_html = self._create_html_report(results)
         
-        return original_image, overlay, results_html
+        return original_image, overlay, gauge_dice, gauge_accuracy, results_html
     
     def _create_overlay(self, image, results):
         """Create overlay visualization"""
@@ -236,10 +271,6 @@ class ForgeryDetector:
             • Regions detected: {num_detections}<br>
             • Average confidence: {avg_confidence*100:.1f}%<br><br>
             
-            <b>Model Performance:</b><br>
-            • Segmentation Dice: {MODEL_METRICS['segmentation']['dice']*100:.1f}%<br>
-            • Classification Accuracy: {MODEL_METRICS['classification']['overall_accuracy']*100:.1f}%<br><br>
-            
             <b>Detections:</b><br>
         """
         
@@ -253,7 +284,7 @@ class ForgeryDetector:
             color_hex = f"#{color_rgb[0]:02x}{color_rgb[1]:02x}{color_rgb[2]:02x}"
             
             html += f"""
-            <div style='margin:8px 0; padding:8px; border-left:3px solid {color_hex}; background:#f9f9f9;'>
+            <div style='margin:8px 0; padding:8px; border-left:3px solid {color_hex}; background:rgba(0,0,0,0.02);'>
                 <b>Region {i}:</b> {forgery_type} ({confidence*100:.1f}%)<br>
                 <small>Location: ({bbox[0]}, {bbox[1]}) | Size: {bbox[2]}×{bbox[3]}px</small>
             </div>
@@ -274,19 +305,20 @@ def detect_forgery(file):
     """Gradio interface function"""
     try:
         if file is None:
-            return None, None, "<div style='padding:12px; border:1px solid #d9534f; border-radius:8px;'>❌ <b>No file uploaded.</b></div>"
+            empty_html = "<div style='padding:12px; border:1px solid #d9534f; border-radius:8px;'>❌ <b>No file uploaded.</b></div>"
+            return None, None, None, None, empty_html
         
         # Get file path
         file_path = file.name if hasattr(file, 'name') else file
         
         # Check if PDF
         if file_path.lower().endswith('.pdf'):
-            original, overlay, results_html = detector.detect(file_path)
+            original, overlay, gauge_dice, gauge_acc, results_html = detector.detect(file_path)
         else:
             image = Image.open(file_path)
-            original, overlay, results_html = detector.detect(image)
+            original, overlay, gauge_dice, gauge_acc, results_html = detector.detect(image)
         
-        return original, overlay, results_html
+        return original, overlay, gauge_dice, gauge_acc, results_html
     
     except Exception as e:
         import traceback
@@ -297,7 +329,7 @@ def detect_forgery(file):
             ❌ <b>Error:</b> {str(e)}
         </div>
         """
-        return None, None, error_html
+        return None, None, None, None, error_html
 
 
 # Custom CSS - subtle styling
@@ -348,14 +380,17 @@ with gr.Blocks(css=custom_css) as demo:
                 """
             )
         
-        with gr.Column(scale=1):
-            gr.Markdown("### Original Image")
-            original_image = gr.Image(label="Uploaded Document", type="numpy")
+        with gr.Column(scale=2):
+            gr.Markdown("### Detection Results")
+            with gr.Row():
+                original_image = gr.Image(label="Original Document", type="numpy")
+                output_image = gr.Image(label="Detected Forgeries", type="numpy")
     
     with gr.Row():
         with gr.Column(scale=1):
-            gr.Markdown("### Detection Result")
-            output_image = gr.Image(label="Annotated Document", type="numpy")
+            gr.Markdown("### Model Performance")
+            gauge_dice = gr.Plot(label="Segmentation Dice Score")
+            gauge_accuracy = gr.Plot(label="Classification Accuracy")
         
         with gr.Column(scale=1):
             gr.Markdown("### Analysis Report")
@@ -377,13 +412,13 @@ with gr.Blocks(css=custom_css) as demo:
     analyze_btn.click(
         fn=detect_forgery,
         inputs=[input_file],
-        outputs=[original_image, output_image, output_html]
+        outputs=[original_image, output_image, gauge_dice, gauge_accuracy, output_html]
     )
     
     clear_btn.click(
-        fn=lambda: (None, None, None, "<i>No analysis yet. Upload a document and click Analyze.</i>"),
+        fn=lambda: (None, None, None, None, None, "<i>No analysis yet. Upload a document and click Analyze.</i>"),
         inputs=None,
-        outputs=[input_file, original_image, output_image, output_html]
+        outputs=[input_file, original_image, output_image, gauge_dice, gauge_accuracy, output_html]
     )