Update app.py

app.py

@@ -2,16 +2,32 @@ import torch
 import gradio as gr
 from PIL import Image
 
-# Load the YOLOv5 model (adjust based on the model you uploaded)
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # You can choose yolov5s, yolov5m, yolov5l
+# Debugging print statements
+print("Loading YOLOv5 model...")
 
-# Function to handle image inference
+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
+
+# Define the function for image inference
 def predict_image(image):
-    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)
+    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)
 
-# Set up Gradio interface to upload an image and get predictions
+# Set up Gradio interface to allow image uploads and get predictions
 interface = gr.Interface(fn=predict_image, inputs=gr.Image(), outputs=gr.Dataframe())
 
 # Launch the interface