Add YOLOv8 object detection app with Gradio interface and dependencies

app.py

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+from ultralytics import YOLO
+import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image
+
+# Load your fine-tuned YOLOv8 model
+model = YOLO("best.pt")
+
+# Function to perform object detection
+def detect_objects(image):
+    image = np.array(image)  # Convert PIL image to numpy
+    results = model(image)  # Run YOLOv8 inference
+
+    # Draw bounding boxes
+    for result in results:
+        for box in result.boxes.xyxy.cpu().numpy():  # Extract bounding boxes
+            x1, y1, x2, y2 = map(int, box[:4])
+            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 3)  # Green box
+
+    return Image.fromarray(image)  # Convert back to PIL image
+
+# Gradio interface
+interface = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="YOLOv8 Object Detection",
+    description="Upload an image and detect objects using a fine-tuned YOLOv8 model."
+)
+
+# Launch the app
+interface.launch()

requirements.txt

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+ultralytics
+gradio
+torch
+torchvision
+opencv-python