Update app.py

app.py

@@ -2,30 +2,14 @@ import torch
 import gradio as gr
 from PIL import Image
 
-# Debugging print statements
-print("Loading YOLOv5 model...")
-
-try:
-    # Load YOLOv5 model (adjust based on the model you uploaded)
-    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # 'yolov5s' is the small model, change if needed
-    print("Model loaded successfully!")
-
-except Exception as e:
-    print(f"Error loading model: {e}")
-    raise
+# Load the YOLOv5 model from the uploaded file (e.g., 'yolov5s.pt')
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.pt')  # Adjust the file name if needed
 
 # Define the function for image inference
 def predict_image(image):
-    try:
-        print("Running inference on the uploaded image...")
-        results = model(image)  # Run inference on the input image
-        print("Inference completed!")
-        results.show()  # Optional: Visualize the results (bounding boxes)
-        return results.pandas().xywh  # Return the results (bounding box coordinates)
-
-    except Exception as e:
-        print(f"Error during inference: {e}")
-        return str(e)
+    results = model(image)  # Run inference on the input image
+    results.show()  # Optionally visualize the results (bounding boxes)
+    return results.pandas().xywh  # Return the results (bounding box coordinates)
 
 # Set up Gradio interface to allow image uploads and get predictions
 interface = gr.Interface(fn=predict_image, inputs=gr.Image(), outputs=gr.Dataframe())