Update app.py

app.py

@@ -128,7 +128,7 @@ iface = gr.Interface(
 )
 
 # Launch the Gradio app
-iface.launch()"""
+iface.launch()
 
 import gradio as gr
 import torch
@@ -156,5 +156,41 @@ iface = gr.Interface(
     description="Upload an image, and the YOLO model will detect objects in it."
 )
 
+# Launch the Gradio app
+iface.launch()"""
+
+from PIL import Image
+import numpy as np
+from ultralytics import YOLO
+import gradio as gr
+
+# Load the YOLO model
+MODEL_URL = 'https://huggingface.co/ayoubsa/yolo_model/resolve/main/best.pt'
+model = YOLO(MODEL_URL)
+
+# Define the prediction function
+def predict(image):
+    try:
+        print("Received image:", type(image))  # Check the type of the received image
+        results = model(image)  # Perform object detection using YOLO
+        results.render()  # Render bounding boxes on the image
+        
+        output_image = Image.fromarray(results.imgs[0])  # Convert to PIL image
+        
+        print("Predicted image:", type(output_image))  # Ensure output is PIL Image
+        return output_image
+    except Exception as e:
+        print("Error during prediction:", e)
+        return None
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload an Image"),  # Upload input as PIL Image
+    outputs=gr.Image(type="pil", label="Predicted Image with Bounding Boxes"),  # Output image
+    title="Object Detection App",
+    description="Upload an image, and the YOLO model will detect objects in it."
+)
+
 # Launch the Gradio app
 iface.launch()