Update app.py

app.py

@@ -388,11 +388,14 @@ def analyze_performance(image, category, user_opinion, frcnn_threshold=0.5, detr
 
     # Convert to HTML with styling
     html_analysis = f"""
-    <div class="{'celebrate' if user_opinion in max_models else ''}">
-        <h3 style='color: {"#4CAF50" if user_opinion in max_models else "#f44336"};'>
-            {"🏆 Victory Achieved!" if user_opinion in max_models else "💪 Keep Trying!"}
+    <div class="{'celebrate' if user_opinion in max_models else ''}" style="margin: 15px 0;">
+        <h3 style='color: {"#4CAF50" if user_opinion in max_models else "#f44336"}; margin-bottom: 15px;'>
+            {"🏆 " + max_models[0] + " Dominates!" if len(max_models) == 1 else "⚔️ Tie Battle!"}
         </h3>
-        <pre style='background: #fff; padding: 20px; border-radius: 10px;'>{analysis}</pre>
+        <div style="background: var(--background-fill-primary); padding: 20px; border-radius: 10px; 
+                    white-space: pre-wrap; overflow-wrap: break-word; color: var(--text-color);">
+            {analysis}
+        </div>
     </div>
     """
     return "Analysis complete!", frcnn_result, detr_result, maskrcnn_result, mask2former_result, html_analysis
@@ -404,7 +407,7 @@ with gr.Blocks(title="AI Vision Showdown", theme=gr.themes.Default(primary_hue="
     ### 🤖 Battle of the algorithms! Upload an image and predict which AI will dominate!
     """)
     
-    # Custom CSS for animations
+    # Enhanced CSS
     gr.HTML("""
     <style>
         @keyframes celebrate {
@@ -415,15 +418,36 @@ with gr.Blocks(title="AI Vision Showdown", theme=gr.themes.Default(primary_hue="
             100% { transform: rotate(0deg); }
         }
         .celebrate { animation: celebrate 0.5s ease-in-out; }
-        .win-border { border: 3px solid #4CAF50 !important; border-radius: 10px; }
-        .battle-card { border-radius: 15px; padding: 20px; margin: 10px 0; background: #f5f5f5; }
-        .tab-header { font-weight: bold !important; }
+        .battle-card {
+            border-radius: 15px;
+            padding: 20px;
+            margin: 10px 0;
+            background: var(--background-fill-primary);
+            border: 1px solid var(--border-color-primary);
+        }
+        .analysis-box {
+            background: var(--background-fill-secondary) !important;
+            color: var(--text-color) !important;
+            padding: 20px;
+            border-radius: 10px;
+            white-space: pre-wrap;
+            overflow-wrap: break-word;
+        }
+        .loading-status {
+            padding: 15px;
+            background: var(--background-fill-secondary);
+            border-radius: 8px;
+            margin: 10px 0;
+            text-align: center;
+            font-weight: bold;
+        }
     </style>
     """)
 
     # State variables
     image_state = gr.State(None)
     category_state = gr.State(None)
+    loading_status = gr.HTML(visible=False)
 
     # Top Section: Inputs
     with gr.Row(variant="battle-card"):
@@ -434,16 +458,16 @@ with gr.Blocks(title="AI Vision Showdown", theme=gr.themes.Default(primary_hue="
 
         with gr.Column(scale=1, min_width=300):
             with gr.Group(visible=False) as prediction_selection:
-                gr.Markdown("## 🤔 Prediction Panel")
+                gr.Markdown("## 🔮 Prediction Arena")
                 category_choice = gr.Radio(
                     choices=["Object Detection", "Object Segmentation"],
-                    label="⚔️ Battle Type Selection",
+                    label="⚔️ Select Battle Ground",
                     value=None,
                     elem_classes="battle-card"
                 )
                 user_opinion = gr.Radio(
                     choices=[],
-                    label="🏆 Which Warrior Will Win?",
+                    label="🏹 Predict the Victor",
                     value=None,
                     visible=False,
                     elem_classes="battle-card"
@@ -523,25 +547,36 @@ with gr.Blocks(title="AI Vision Showdown", theme=gr.themes.Default(primary_hue="
     def run_detection(image, category, user_opinion, frcnn_threshold, detr_threshold, maskrcnn_threshold, mask2former_threshold):
         if not category or not user_opinion:
             return "Please select a category and prediction.", None, None, None, None, "No analysis available.", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-        result = analyze_performance(image, category, user_opinion, frcnn_threshold, detr_threshold, maskrcnn_threshold, mask2former_threshold)
-        message, frcnn_result_img, detr_result_img, maskrcnn_result_img, mask2former_result_img, html_analysis = result
-        return [
-            message,
-            gr.update(value=frcnn_result_img, visible=category == "Object Detection"),
-            gr.update(value=detr_result_img, visible=category == "Object Detection"),
-            gr.update(value=maskrcnn_result_img, visible=category == "Object Segmentation"),
-            gr.update(value=mask2former_result_img, visible=category == "Object Segmentation"),
-            html_analysis,
-            gr.update(visible=True),
-            gr.update(visible=True),
-            gr.update(visible=category == "Object Detection"),
-            gr.update(visible=category == "Object Segmentation")
-        ]
+        
+        def analyze_with_progress(progress=gr.Progress()):
+            progress(0.1, desc="⚙️ Models are gearing up...")
+            result = analyze_performance(image, category, user_opinion, frcnn_threshold, detr_threshold, maskrcnn_threshold, mask2former_threshold)
+            progress(1.0, desc="✅ Battle complete!")
+            return result
+        
+        try:
+            message, frcnn_result_img, detr_result_img, maskrcnn_result_img, mask2former_result_img, html_analysis = analyze_with_progress()
+            return [
+                message,
+                gr.update(value=frcnn_result_img, visible=category == "Object Detection"),
+                gr.update(value=detr_result_img, visible=category == "Object Detection"),
+                gr.update(value=maskrcnn_result_img, visible=category == "Object Segmentation"),
+                gr.update(value=mask2former_result_img, visible=category == "Object Segmentation"),
+                html_analysis,
+                gr.update(visible=True),
+                gr.update(visible=True),
+                gr.update(visible=category == "Object Detection"),
+                gr.update(visible=category == "Object Segmentation"),
+                gr.update(visible=False)
+            ]
+        except Exception as e:
+            return [f"Error: {str(e)}"] + [gr.update()]*9 + [gr.update(visible=False)]
 
     detect_button.click(
         fn=run_detection,
         inputs=[image_state, category_state, user_opinion, frcnn_threshold, detr_threshold, maskrcnn_threshold, mask2former_threshold],
-        outputs=[gr.Textbox(visible=False), frcnn_result, detr_result, maskrcnn_result, mask2former_result, analysis_output, outputs_panel, results_panel, detection_tab, segmentation_tab]
+        outputs=[gr.Textbox(visible=False), frcnn_result, detr_result, maskrcnn_result, mask2former_result, 
+                analysis_output, outputs_panel, results_panel, detection_tab, segmentation_tab, loading_status]
     )
 
     # Restart button click event