Create app.py

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+# Images
+torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/06/15/01/11/soccer-1457988_1280.jpg', 'soccer.jpg')
+torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/11/21/14/31/vw-bus-1845719_1280.jpg', 'bus.jpg')
+# Model
+model = torch.hub.load('ultralytics/yolov3', 'yolov3')  # or yolov3-spp, yolov3-tiny, custom
+def yolo(im, size=640):
+    g = (size / max(im.size))  # gain
+    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize
+    results = model(im)  # inference
+    results.render()  # updates results.imgs with boxes and labels
+    return Image.fromarray(results.imgs[0])
+inputs = gr.inputs.Image(type='pil', label="Original Image")
+outputs = gr.outputs.Image(type="pil", label="Output Image")
+title = "YOLOv3"
+description = "YOLOv3 Gradio demo for object detection. Upload an image or click an example image to use."
+article = "<p style='text-align: center'>YOLOv3 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov3' target='_blank'>Source code</a> |<a href='https://apps.apple.com/app/id1452689527' target='_blank'>iOS App</a></p>"
+examples = [['soccer.jpg'], ['bus.jpg']]
+gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(
+    debug=True)