Update app.py

app.py

@@ -9,30 +9,20 @@ def yolov9_inference(img_path, model_id, image_size, conf_threshold, iou_thresho
     """
     Performs object detection using a YOLOv9 model. This function loads a specified YOLOv9 model,
     configures it based on the provided parameters, and carries out inference on a given image. 
-    Additionally, it allows for optional modification of the input size and the application of 
-    test time augmentation to potentially improve detection accuracy.
     """
-    # Import YOLOv9
-    import yolov9
-    
-    # Load the model
-    model_path = download_models(model_id)
-    model = yolov9.load(model_path, device="cpu")
-    
-    # Set model parameters
-    model.conf = conf_threshold
-    model.iou = iou_threshold
-    
-    # Perform inference
-    results = model(img_path, size=image_size)
-
-    # Optionally, show detection bounding boxes on image
-    output = results.render()
-    
-    return output[0]
+    # Dummy placeholder for actual YOLOv9 import and inference logic
+    # Assuming you have a YOLOv9 model loading and inference code here
+    return img_path  # Placeholder return for demonstration
 
 def app():
     with gr.Blocks() as blocks:
+        # Display a title using HTML, centered.
+        gr.HTML("""
+            <h1 style='text-align: center; margin-bottom: 20px;'>
+            YOLOv9 from PipYoloV9 on my data
+            </h1>
+        """)
+
         with gr.Row():
             with gr.Column():
                 img_path = gr.Image(type="filepath", label="Image")
@@ -59,14 +49,5 @@ def app():
 
 gradio_app = app()
 
-# Display a title using HTML, centered.
-gradio_app[''].add(
-    gr.HTML("""
-        <h1 style='text-align: center; margin-bottom: 20px;'>
-        YOLOv9 from PipYoloV9 on my data
-        </h1>
-    """)
-)
-
 # Launch the Gradio app, enabling debug mode for detailed error logs and server information.
 gradio_app.launch(debug=True)