Update: layout refinement

app.py

@@ -6,21 +6,7 @@ import torchvision.transforms as transforms
 import gradio as gr
 from yolov5 import xai_yolov5
 from yolov8 import xai_yolov8s
-"""
-def process_image(image, yolo_versions=["yolov5"]):
-    image = np.array(image)
-    image = cv2.resize(image, (640, 640))
-    result_images = []
-    for yolo_version in yolo_versions:
-        if yolo_version == "yolov5":
-            result_images.append(xai_yolov5(image)) 
-        elif yolo_version == "yolov8s":
-            result_images.append(xai_yolov8s(image))
-        else:
-            result_images.append((Image.fromarray(image), f"{yolo_version} not yet implemented."))
-    return result_images
 
-"""
 sample_images = {
     "Sample 1": os.path.join(os.getcwd(), "data/xai/sample1.jpeg"),
     "Sample 2":  os.path.join(os.getcwd(), "data/xai/sample2.jpg"),
@@ -32,33 +18,7 @@ def load_sample_image(sample_name):
     return None
 
 default_sample_image = load_sample_image("Sample 1")
-"""
-with gr.Blocks() as interface:
-    gr.Markdown("# XAI: Upload an image to visualize object detection of your models..")
-    gr.Markdown("Upload an image or select a sample image to visualize object detection.")
-
-    with gr.Row():
-        uploaded_image = gr.Image(type="pil", label="Upload an Image")
-        sample_selection = gr.Dropdown(
-            choices=list(sample_images.keys()),
-            label="Select a Sample Image",
-            type="value",
-        )
-        sample_display = gr.Image(label="Sample Image Preview", value=default_sample_image)
-        sample_selection.change(fn=load_sample_image, inputs=sample_selection, outputs=sample_display)
 
-    selected_models = gr.CheckboxGroup(
-        choices=["yolov5", "yolov8s"],
-        value=["yolov5"], 
-        label="Select Model(s)",
-    )
-    result_gallery = gr.Gallery(label="Results", elem_id="gallery", rows=2, height=500)
-    gr.Button("Run").click(
-        fn=process_image,
-        inputs=[uploaded_image, selected_models],
-        outputs=result_gallery,
-    )
-"""
 def load_sample_image(choice):
     if choice in sample_images:
         image_path = sample_images[choice]
@@ -86,24 +46,31 @@ def process_image(sample_choice, uploaded_image, yolo_versions=["yolov5"]):
             result_images.append((Image.fromarray(image), f"{yolo_version} not yet implemented."))
     return result_images
 
-
-"""
-import gradio as gr
 with gr.Blocks() as interface:
     gr.Markdown("# XAI: Visualize Object Detection of Your Models")
     gr.Markdown("Select a sample image to visualize object detection.")
     default_sample = "Sample 1"
     with gr.Row():
-        sample_selection = gr.Radio(
-            choices=list(sample_images.keys()),
-            label="Select a Sample Image",
-            type="value",
-            value=default_sample,  # Set default selection
-        )
+        # Left side: Sample selection and upload image
+        with gr.Column():
+            sample_selection = gr.Radio(
+                choices=list(sample_images.keys()),
+                label="Select a Sample Image",
+                type="value",
+                value=default_sample,  # Set default selection
+            )
+            # Upload image below sample selection
+            gr.Markdown("**Or upload your own image:**")
+            upload_image = gr.Image(
+                label="Upload an Image",
+                type="filepath",  # Correct type for file path compatibility
+            )
+        # Right side: Selected sample image display
         sample_display = gr.Image(
             value=load_sample_image(default_sample),  
             label="Selected Sample Image",
         )
+    
     sample_selection.change(
         fn=load_sample_image,
         inputs=sample_selection,
@@ -117,49 +84,6 @@ with gr.Blocks() as interface:
     )
     result_gallery = gr.Gallery(label="Results", elem_id="gallery", rows=2, height=500)
 
-    gr.Button("Run").click(
-        fn=process_image,
-        inputs=[sample_selection, selected_models],
-        outputs=result_gallery,
-    )
-
-interface.launch()
-"""
-with gr.Blocks() as interface:
-    gr.Markdown("# XAI: Visualize Object Detection of Your Models")
-    gr.Markdown("Select a sample image to visualize object detection.")
-    default_sample = "Sample 1"
-    with gr.Row():
-        sample_selection = gr.Radio(
-            choices=list(sample_images.keys()),
-            label="Select a Sample Image",
-            type="value",
-            value=default_sample,  # Set default selection
-        )
-        sample_display = gr.Image(
-            value=load_sample_image(default_sample),  
-            label="Selected Sample Image",
-        )
-    sample_selection.change(
-        fn=load_sample_image,
-        inputs=sample_selection,
-        outputs=sample_display,
-    )
-
-    # Adding an upload placeholder
-    gr.Markdown("**Or upload your own image:**")
-    upload_image = gr.Image(
-        label="Upload an Image",
-        type="pil",  # Corrected type for file path compatibility
-    )
-
-    selected_models = gr.CheckboxGroup(
-        choices=["yolov5", "yolov8s"],
-        value=["yolov5"],
-        label="Select Model(s)",
-    )
-    result_gallery = gr.Gallery(label="Results", elem_id="gallery", rows=2, height=500)
-
     gr.Button("Run").click(
         fn=process_image,
         inputs=[sample_selection, upload_image, selected_models],  # Include both options