Create app.py

app.py

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+import gradio as gr
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+
+# Load the YOLOv5 model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
+
+# Function to perform object detection
+def detect_objects(image):
+    # Convert the image to a numpy array
+    img_array = np.array(image)
+
+    # Perform object detection
+    results = model(img_array)
+
+    # Draw bounding boxes and labels
+    for index, row in results.pandas().xyxy[0].iterrows():
+        x1, y1, x2, y2 = int(row['xmin']), int(row['ymin']), int(row['xmax']), int(row['ymax'])
+        label = row['name']
+        cv2.rectangle(img_array, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        cv2.putText(img_array, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
+
+    # Convert the numpy array back to an image
+    output_image = Image.fromarray(img_array)
+    return output_image
+
+# Set up the Gradio interface
+interface = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(source='webcam', tool='editor', type="pil"),
+    outputs=gr.Image(type="pil"),
+    live=True,
+    title="Real-time Object Detection with YOLOv5"
+)
+
+# Launch the interface
+interface.launch()