Update app.py

app.py

@@ -10,7 +10,9 @@ def init():
 
     # Step 1: Load the YOLOv5 model from Hugging Face
     try:
-        object_detection_model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5/weights/best14.pt')  # Assuming model is locally available
+        # Load the YOLOv5 model with trust_repo=True to skip the warning
+        object_detection_model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5/weights/best14.pt', trust_repo=True)
+        # Assuming model is locally available
         print("YOLOv5 model loaded successfully.")
     except Exception as e:
         print(f"Error loading YOLOv5 model: {e}")
@@ -76,15 +78,16 @@ def process_image(image):
 # Initialize models
 init()
 
-# Gradio Interface
+# Updated Gradio interface with new syntax
 interface = gr.Interface(
     fn=process_image,  # Function to run
-    inputs=gr.inputs.Image(type="pil"),  # Input: Image upload
-    outputs=[gr.outputs.Image(type="pil", label="Detected Objects"),  # Output 1: Image with bounding boxes
-             gr.outputs.JSON(label="Object Captions & Bounding Boxes"),  # Output 2: JSON results for each object
-             gr.outputs.Textbox(label="Whole Image Caption")],  # Output 3: Caption for the whole image
+    inputs=gr.Image(type="pil"),  # Input: Image upload
+    outputs=[gr.Image(type="pil", label="Detected Objects"),  # Output 1: Image with bounding boxes
+             gr.JSON(label="Object Captions & Bounding Boxes"),  # Output 2: JSON results for each object
+             gr.Textbox(label="Whole Image Caption")],  # Output 3: Caption for the whole image
     live=True
 )
 
+
 # Launch the Gradio app
 interface.launch()