Update app.py

app.py

@@ -4,59 +4,6 @@ from PIL import Image
 import os 
 import yolov9
 
-import gradio as gr
-import torch
-from PIL import Image
-import os 
-import yolov9
-
-def yolov9_inference(img_path, image_size, conf_threshold, iou_threshold):
-    model = yolov9.load('./best.pt')
-    model.conf = conf_threshold
-    model.iou = iou_threshold
-    results = model(img_path, size=image_size)
-    output = results.render()
-    return output[0]
-
-def app():
-    with gr.Blocks() as demo:
-        gr.HTML(HTML_TEMPLATE)
-        
-        with gr.Row():
-            with gr.Column(scale=1, min_width=300):
-                img_path = gr.Image(type="filepath", label="Upload Image")
-                image_size = gr.Slider(label="Image Size", minimum=320, maximum=1280, step=32, value=640)
-                conf_threshold = gr.Slider(label="Confidence Threshold", minimum=0.1, maximum=1.0, step=0.1, value=0.4)
-                iou_threshold = gr.Slider(label="IoU Threshold", minimum=0.1, maximum=1.0, step=0.1, value=0.5)
-                detect_button = gr.Button("Detect Manholes", variant="primary")
-            
-            with gr.Column(scale=1, min_width=300):
-                output_numpy = gr.Image(type="numpy", label="Detection Result")
-        
-        detect_button.click(
-            fn=yolov9_inference,
-            inputs=[img_path, image_size, conf_threshold, iou_threshold],
-            outputs=[output_numpy]
-        )
-        
-        gr.Examples(
-            examples=[
-                ["./openmanhole.jpg", 640, 0.4, 0.5],
-                ["./images.jpeg", 640, 0.4, 0.5],
-            ],
-            fn=yolov9_inference,
-            inputs=[img_path, image_size, conf_threshold, iou_threshold],
-            outputs=[output_numpy],
-            cache_examples=True,
-        )
-    
-    return demo
-import gradio as gr
-import torch
-from PIL import Image
-import os 
-import yolov9
-
 HTML_TEMPLATE = """
 <style>
     body {
@@ -193,16 +140,46 @@ HTML_TEMPLATE = """
 </div>
 """
 
-# Your existing yolov9_inference function here
 def yolov9_inference(img_path, image_size, conf_threshold, iou_threshold):
-    # ... (your existing code)
+    model = yolov9.load('./best.pt')
+    model.conf = conf_threshold
+    model.iou = iou_threshold
+    results = model(img_path, size=image_size)
+    output = results.render()
+    return output[0]
 
 def app():
     with gr.Blocks() as demo:
         gr.HTML(HTML_TEMPLATE)
         
-        # ... (rest of your existing app code)
-
+        with gr.Row():
+            with gr.Column(scale=1, min_width=300):
+                img_path = gr.Image(type="filepath", label="Upload Image")
+                image_size = gr.Slider(label="Image Size", minimum=320, maximum=1280, step=32, value=640)
+                conf_threshold = gr.Slider(label="Confidence Threshold", minimum=0.1, maximum=1.0, step=0.1, value=0.4)
+                iou_threshold = gr.Slider(label="IoU Threshold", minimum=0.1, maximum=1.0, step=0.1, value=0.5)
+                detect_button = gr.Button("Detect Manholes", variant="primary")
+            
+            with gr.Column(scale=1, min_width=300):
+                output_numpy = gr.Image(type="numpy", label="Detection Result")
+        
+        detect_button.click(
+            fn=yolov9_inference,
+            inputs=[img_path, image_size, conf_threshold, iou_threshold],
+            outputs=[output_numpy]
+        )
+        
+        gr.Examples(
+            examples=[
+                ["./openmanhole.jpg", 640, 0.4, 0.5],
+                ["./images.jpeg", 640, 0.4, 0.5],
+            ],
+            fn=yolov9_inference,
+            inputs=[img_path, image_size, conf_threshold, iou_threshold],
+            outputs=[output_numpy],
+            cache_examples=True,
+        )
+    
     return demo
 
 css = """