Remove zoom feature - keep interface clean and simple

- Removed all zoom sliders and zoomed image components
- Removed apply_zoom functions and global image storage
- Back to core functionality: upload image, adjust threshold, detect
- Auto-detection on image upload and threshold change
- Manual detect button and clear button still available

app.py

@@ -6,16 +6,10 @@ from model import TrafficSignDetector
 # Load the detector
 detector = TrafficSignDetector('config.yaml')
 
-# Store original images in memory
-original_output_image = None
-original_preprocessed_image = None
-
 def detect_traffic_signs(image, confidence_threshold):
     """
     Process the uploaded image and return the image with detected signs.
     """
-    global original_output_image, original_preprocessed_image
-    
     print(f"Received image type: {type(image)}")
     if hasattr(image, 'convert'):
         image = np.array(image)
@@ -32,31 +26,12 @@ def detect_traffic_signs(image, confidence_threshold):
     result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
     preprocessed_image = cv2.cvtColor(preprocessed_image, cv2.COLOR_BGR2RGB)
 
-    # Store originals
-    original_output_image = result_image.copy()
-    original_preprocessed_image = preprocessed_image.copy()
-
     return result_image, preprocessed_image
 
-def apply_zoom(image, zoom_level):
-    """Resize image based on zoom level"""
-    if image is None:
-        return None
-    
-    zoom_factor = zoom_level / 100.0
-    h, w = image.shape[:2]
-    new_w = int(w * zoom_factor)
-    new_h = int(h * zoom_factor)
-    
-    if new_w > 0 and new_h > 0:
-        zoomed = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
-        return zoomed
-    return image
-
 # Create Gradio interface
 with gr.Blocks(title="Traffic Sign Detector") as demo:
     gr.Markdown("# Traffic Sign Detector")
-    gr.Markdown("Upload an image to detect traffic signs using YOLOv8.")
+    gr.Markdown("Upload an image to detect traffic signs using YOLOv8. Detection runs automatically when you upload or adjust the threshold.")
     
     with gr.Row():
         input_image = gr.Image(label="Upload Image", type="pil")
@@ -71,28 +46,15 @@ with gr.Blocks(title="Traffic Sign Detector") as demo:
             maximum=0.9,
             value=0.30,
             step=0.01,
-            label="Confidence Threshold"
+            label="Confidence Threshold",
+            info="Lower values show more detections (less confident). Adjust to find optimal balance."
         )
     
     with gr.Row():
         detect_btn = gr.Button("Detect Traffic Signs", variant="primary")
         reset_btn = gr.Button("Clear")
     
-    gr.Markdown("### Zoom Inspector")
-    gr.Markdown("**Note:** Use these sliders to zoom in and inspect details. Scroll within the image view.")
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("**Detected Image**")
-            zoom_output = gr.Slider(50, 300, value=100, step=10, label="Zoom %")
-            output_zoomed = gr.Image(label="", interactive=False, show_download_button=False)
-        
-        with gr.Column(scale=1):
-            gr.Markdown("**Preprocessed Image**")
-            zoom_preprocessed = gr.Slider(50, 300, value=100, step=10, label="Zoom %")
-            preprocessed_zoomed = gr.Image(label="", interactive=False, show_download_button=False)
-    
-    # Auto-detect on upload
+    # Auto-detect on image upload
     input_image.change(
         fn=detect_traffic_signs,
         inputs=[input_image, confidence_threshold],
@@ -116,26 +78,10 @@ with gr.Blocks(title="Traffic Sign Detector") as demo:
         queue=True
     )
     
-    # Zoom output
-    zoom_output.change(
-        fn=lambda z: apply_zoom(original_output_image, z),
-        inputs=[zoom_output],
-        outputs=[output_zoomed],
-        queue=False
-    )
-    
-    # Zoom preprocessed
-    zoom_preprocessed.change(
-        fn=lambda z: apply_zoom(original_preprocessed_image, z),
-        inputs=[zoom_preprocessed],
-        outputs=[preprocessed_zoomed],
-        queue=False
-    )
-    
-    # Clear
+    # Clear button
     reset_btn.click(
-        fn=lambda: (None, None, None, 100, 100, None, None),
-        outputs=[input_image, output_image, preprocessed_image, zoom_output, zoom_preprocessed, output_zoomed, preprocessed_zoomed]
+        fn=lambda: (None, None, None, 0.30),
+        outputs=[input_image, output_image, preprocessed_image, confidence_threshold]
     )
 
 if __name__ == "__main__":