Update app.py

app.py

@@ -1,3 +1,4 @@
+import netron
 import threading
 import gradio as gr
 import os
@@ -6,6 +7,8 @@ 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 = {
@@ -67,31 +70,6 @@ custom_css = """
 }
 """
 
-def run_both(sample_choice, uploaded_image, yolo_versions):
-    """Run both image processing and model visualization simultaneously."""
-    results = []
-
-    def process_thread():
-        result_images = process_image(sample_choice, uploaded_image, yolo_versions)
-        results.append(result_images)
-    
-    def model_thread():
-        model_html = view_model(yolo_versions)
-        results.append(model_html)
-
-    # Create threads to run both functions simultaneously
-    process_thread_obj = threading.Thread(target=process_thread)
-    model_thread_obj = threading.Thread(target=model_thread)
-
-    process_thread_obj.start()
-    model_thread_obj.start()
-
-    # Wait for both threads to finish
-    process_thread_obj.join()
-    model_thread_obj.join()
-
-    return results[0], results[1]  # Return processed image results and model visualization
-
 with gr.Blocks(css=custom_css) as interface:
     gr.Markdown("# NeuralVista: Visualize Object Detection of Your Models")
     
@@ -144,9 +122,12 @@ with gr.Blocks(css=custom_css) as interface:
         outputs=sample_display,
     )
 
-    # Run both functions concurrently on button click
+    # Process image and display results, also trigger Netron visualization when run button is clicked
     run_button.click(
-        fn=run_both,
+        fn=lambda sample_choice, uploaded_image, yolo_versions: [
+            process_image(sample_choice, uploaded_image, yolo_versions),  # Process image
+            view_model(yolo_versions)  # Display model visualization
+        ],
         inputs=[sample_selection, upload_image, selected_models],
         outputs=[result_gallery, netron_display],
     )