integrating public url

app.py

@@ -1,4 +1,5 @@
-"""
+import netron
+import threading
 import gradio as gr
 import os
 from PIL import Image
@@ -6,6 +7,10 @@ import cv2
 import numpy as np
 from yolov5 import xai_yolov5
 from yolov8 import xai_yolov8s
+import time
+import tempfile
+
+
 
 # Sample images directory
 sample_images = {
@@ -13,56 +18,73 @@ sample_images = {
     "Sample 2": os.path.join(os.getcwd(), "data/xai/sample2.jpg"),
 }
 
-# Function to load sample image
 def load_sample_image(sample_name):
+    """Load a sample image based on user selection."""
     image_path = sample_images.get(sample_name)
     if image_path and os.path.exists(image_path):
         return Image.open(image_path)
     return None
 
-# Function to process the image
 def process_image(sample_choice, uploaded_image, yolo_versions):
-    # Use uploaded or sample image
+    """Process the image using selected YOLO models."""
     if uploaded_image is not None:
-        image = uploaded_image
+        image = uploaded_image  # Use the uploaded image
     else:
-        image = load_sample_image(sample_choice)
+        image = load_sample_image(sample_choice)  # Use selected sample image
 
-    # Resize and process the image
     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))
+            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 implemented."))
+            result_images.append((Image.fromarray(image), f"{yolo_version} not yet implemented."))
 
     return result_images
 
+
+def view_model(file):
+    if file:
+        file_path = file.name
+        #netron_url = start_netron_server(file_path)
+        if True:
+            # Embed the Netron viewer using an iframe with the generated URL
+            iframe_html = f"""
+            <iframe 
+                src="https://netron.app/?url=https://huggingface.co/FFusion/FFusionXL-BASE/blob/main/vae_encoder/model.onnx" 
+                width="100%" 
+                height="800" 
+                frameborder="0">
+            </iframe>
+            """
+            return iframe_html
+        return "<p>Error: Unable to generate Netron visualization.</p>"
+    return "<p>Please upload a valid model file.</p>"
+
+
+
 # Custom CSS for styling (optional)
 custom_css = """
- #run_button {
-  #  background-color: purple;
-   # color: white;
-   # width: 120px;
-    #border-radius: 5px;
-    #font-size: 14px;
-#}
+#run_button {
+    background-color: purple;
+    color: white;
+    width: 120px;
+    border-radius: 5px;
+    font-size: 14px;
+}
 """
 
-# Gradio UI
 with gr.Blocks(css=custom_css) as interface:
     gr.Markdown("# XAI: Visualize Object Detection of Your Models")
-
-    # Default sample
+    
     default_sample = "Sample 1"
 
     with gr.Row():
-        # Left: Select sample or upload image
+        # Left side: Sample selection and upload image
         with gr.Column():
             sample_selection = gr.Radio(
                 choices=list(sample_images.keys()),
@@ -71,7 +93,10 @@ with gr.Blocks(css=custom_css) as interface:
                 value=default_sample,
             )
 
-            upload_image = gr.Image(label="Upload an Image", type="pil")
+            upload_image = gr.Image(
+                label="Upload an Image",
+                type="pil",  
+            )
 
             selected_models = gr.CheckboxGroup(
                 choices=["yolov5", "yolov8s"],
@@ -81,14 +106,13 @@ with gr.Blocks(css=custom_css) as interface:
 
             run_button = gr.Button("Run", elem_id="run_button")
 
-        # Right: Display sample image
         with gr.Column():
             sample_display = gr.Image(
-                value=load_sample_image(default_sample),
+                value=load_sample_image(default_sample),  
                 label="Selected Sample Image",
             )
 
-    # Results
+    # Below the sample image, display results and architecture side by side
     with gr.Row():
         result_gallery = gr.Gallery(
             label="Results",
@@ -97,21 +121,28 @@ with gr.Blocks(css=custom_css) as interface:
             height=500,
         )
 
-    # Sample selection update
+        model_file_input = gr.File(label="Upload Model File", type="filepath")  # Fixed type here
+        netron_display = gr.HTML(label="Netron Visualization")
+
     sample_selection.change(
         fn=load_sample_image,
         inputs=sample_selection,
         outputs=sample_display,
     )
 
-    # Process image
     run_button.click(
         fn=process_image,
         inputs=[sample_selection, upload_image, selected_models],
         outputs=[result_gallery],
     )
 
-# Launch Gradio app
+    model_file_input.change(
+        fn=view_model,
+        inputs=model_file_input,
+        outputs=netron_display,
+    )
+
+# Launching Gradio app and handling Netron visualization separately.
 if __name__ == "__main__":
     interface.launch(share=True)
 """
@@ -157,12 +188,12 @@ def visualize_model(file):
         if True:
             # Embed the Netron viewer using an iframe with the generated URL
             iframe_html = f"""
-            <iframe 
-                src="https://netron.app/?url=https://huggingface.co/FFusion/FFusionXL-BASE/blob/main/vae_encoder/model.onnx" 
-                width="100%" 
-                height="800" 
-                frameborder="0">
-            </iframe>
+           # <iframe 
+             #   src="https://netron.app/?url=https://huggingface.co/FFusion/FFusionXL-BASE/blob/main/vae_encoder/model.onnx" 
+             #   width="100%" 
+             #   height="800" 
+           #   #  frameborder="0">
+           # </iframe>
             """
             return iframe_html
         return "<p>Error: Unable to generate Netron visualization.</p>"
@@ -178,3 +209,4 @@ with gr.Blocks() as demo:
 
 # Launch the Gradio app
 demo.launch()
+"""