Create app_YOLOv5.py

app_YOLOv5.py

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+import gradio as gr
+import torch
+from PIL import Image
+
+# Images
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', 'bus.jpg')
+
+# Model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # force_reload=True to update
+
+
+def yolo(im, size=1080):
+    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 = "YOLOv5"
+description = "YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use."
+article = "<p style='text-align: center'>YOLOv5 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/yolov5'>Source code</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
+
+examples = [['zidane.jpg'], ['bus.jpg']]
+gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(cache_examples=True,enable_queue=True)