Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+import cv2
+from ultralytics import YOLO
+
+# Load the YOLOv8 model
+model = YOLO('best.pt')
+
+def detect_objects(image):
+    # Convert the input image to a format YOLO can work with
+    image = np.array(image)
+    
+    # Perform detection
+    results = model(image)[0]
+    
+    # Draw bounding boxes on the image
+    for box in results.boxes.data.cpu().numpy():
+        x1, y1, x2, y2, score, class_id = box
+        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+        
+        # Draw the bounding box
+        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        
+        # Put the class name above the bounding box
+        class_name = model.model.names[int(class_id)]
+        cv2.putText(image, f'{class_name} {score:.2f}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+    
+    # Convert back to PIL image
+    return Image.fromarray(image)
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=gr.outputs.Image(type="pil"),
+    title="YOLOv8 Object Detection",
+    description="Upload an image and YOLOv8 will detect objects in the image."
+)
+
+# Launch the interface
+interface.launch()