Update app.py

app.py

@@ -328,110 +328,116 @@ if __name__ == "__main__":
             <p style='margin: 10px 0 0 0; font-size: 1.2em; opacity: 0.9;'>Compact interface for advanced document structure detection</p>
         </div>
         """)
+
+        # LEFT COLUMN - Controls and Input
+        with gr.Column(scale=1):
         
-        # Controls Section
-        with gr.Group(elem_classes=["controls-container"]):
-            # 1. Image Upload (First)
-            gr.HTML("<h3 style='margin-top: 0;'>📄 Upload Document</h3>")
-            input_img = gr.Image(
-                label="Document Image",
-                type="pil",
-                height=300,
-                interactive=True
-            )
-            
-            # Divider
-            gr.HTML("<div class='section-divider'></div>")
-            
-            # 2. Model Selection (Second)
-            gr.HTML("<h3>🤖 Model Selection</h3>")
-            model_dropdown = gr.Dropdown(
-                choices=list(MODELS.keys()),
-                value="Egret XLarge",
-                label="AI Model",
-                info="Model will load automatically when analyzing",
-                interactive=True
-            )
-            
-            # Divider
-            gr.HTML("<div class='section-divider'></div>")
-            
-            # 3. Detection Parameters (Third)
-            gr.HTML("<h3>⚙️ Detection Settings</h3>")
-            
-            with gr.Row():
-                conf_threshold = gr.Slider(
-                    minimum=0.0,
-                    maximum=1.0,
-                    value=0.6,
-                    step=0.05,
-                    label="Confidence Threshold",
-                    info="Minimum confidence for detections"
+            # Controls Section
+            with gr.Group(elem_classes=["controls-container"]):
+                # 1. Image Upload (First)
+                gr.HTML("<h3 style='margin-top: 0;'>📄 Upload Document</h3>")
+                input_img = gr.Image(
+                    label="Document Image",
+                    type="pil",
+                    height=300,
+                    interactive=True
                 )
                 
-                iou_threshold = gr.Slider(
-                    minimum=0.0,
-                    maximum=1.0,
-                    value=0.5,
-                    step=0.05,
-                    label="NMS IoU Threshold",
-                    info="Non-maximum suppression threshold"
+                # Divider
+                gr.HTML("<div class='section-divider'></div>")
+                
+                # 2. Model Selection (Second)
+                gr.HTML("<h3>🤖 Model Selection</h3>")
+                model_dropdown = gr.Dropdown(
+                    choices=list(MODELS.keys()),
+                    value="Egret XLarge",
+                    label="AI Model",
+                    info="Model will load automatically when analyzing",
+                    interactive=True
                 )
-            
-            with gr.Row():
-                nms_method = gr.Radio(
-                    choices=["Custom IoMin", "Standard IoU"],
-                    value="Custom IoMin",
-                    label="NMS Algorithm",
-                    info="Choose suppression method"
+                
+                # Divider
+                gr.HTML("<div class='section-divider'></div>")
+                
+                # 3. Detection Parameters (Third)
+                gr.HTML("<h3>⚙️ Detection Settings</h3>")
+                
+                with gr.Row():
+                    conf_threshold = gr.Slider(
+                        minimum=0.0,
+                        maximum=1.0,
+                        value=0.6,
+                        step=0.05,
+                        label="Confidence Threshold",
+                        info="Minimum confidence for detections"
+                    )
+                    
+                    iou_threshold = gr.Slider(
+                        minimum=0.0,
+                        maximum=1.0,
+                        value=0.5,
+                        step=0.05,
+                        label="NMS IoU Threshold",
+                        info="Non-maximum suppression threshold"
+                    )
+                
+                with gr.Row():
+                    nms_method = gr.Radio(
+                        choices=["Custom IoMin", "Standard IoU"],
+                        value="Custom IoMin",
+                        label="NMS Algorithm",
+                        info="Choose suppression method"
+                    )
+                    
+                    alpha_slider = gr.Slider(
+                        minimum=0.0,
+                        maximum=1.0,
+                        value=0.3,
+                        step=0.1,
+                        label="Overlay Transparency",
+                        info="Transparency of detection overlays"
+                    )
+                
+                show_labels_checkbox = gr.Checkbox(
+                    value=True,
+                    label="Show Class Labels and Confidence Scores",
+                    info="Display detection labels on the output image",
+                    interactive=True
                 )
                 
-                alpha_slider = gr.Slider(
-                    minimum=0.0,
-                    maximum=1.0,
-                    value=0.3,
-                    step=0.1,
-                    label="Overlay Transparency",
-                    info="Transparency of detection overlays"
+                # Divider
+                gr.HTML("<div class='section-divider'></div>")
+                
+                # 4. Analyze Button (Last)
+                detect_btn = gr.Button(
+                    "🔍 Analyze Document", 
+                    variant="primary", 
+                    size="lg",
+                    elem_classes=["analyze-btn"]
                 )
-            
-            show_labels_checkbox = gr.Checkbox(
-                value=True,
-                label="Show Class Labels and Confidence Scores",
-                info="Display detection labels on the output image",
-                interactive=True
-            )
-            
-            # Divider
-            gr.HTML("<div class='section-divider'></div>")
-            
-            # 4. Analyze Button (Last)
-            detect_btn = gr.Button(
-                "🔍 Analyze Document", 
-                variant="primary", 
-                size="lg",
-                elem_classes=["analyze-btn"]
-            )
+
+        # RIGHT COLUMN - Results and Output
+        with gr.Column(scale=1):
         
-        # Results Section
-        with gr.Group(elem_classes=["results-container"]):
-            gr.HTML("<h3 style='margin-top: 0;'>🎯 Analysis Results</h3>")
-            
-            output_img = gr.Image(
-                label="Analyzed Document",
-                type="numpy",
-                height=600,
-                interactive=False
-            )
+            # Results Section
+            with gr.Group(elem_classes=["results-container"]):
+                gr.HTML("<h3 style='margin-top: 0;'>🎯 Analysis Results</h3>")
+                
+                output_img = gr.Image(
+                    label="Analyzed Document",
+                    type="numpy",
+                    height=600,
+                    interactive=False
+                )
+                
+                detection_info = gr.Textbox(
+                    label="Detection Summary",
+                    value="Ready for analysis. Upload an image and click 'Analyze Document'.",
+                    interactive=False,
+                    lines=2,
+                    show_copy_button=True
+                )
             
-            detection_info = gr.Textbox(
-                label="Detection Summary",
-                value="Ready for analysis. Upload an image and click 'Analyze Document'.",
-                interactive=False,
-                lines=2,
-                show_copy_button=True
-            )
-        
         # Event Handler
         detect_btn.click(
             fn=process_image,