Update app.py

app.py

@@ -1,48 +1,22 @@
 import gradio as gr
-from huggingface_hub import hf_hub_download
+import torch
 from PIL import Image
-import yolov5
-
-# Downloading the model from HuggingFace
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='myYOLO/best.pt', force_reload=True)
-
-
-# Prediction function
-def predict(im, threshold=0.50):
-    """
-    Performs prediction using Datacat YOLOv5 model
-    """
-
-    # We could resize the image, but the application handles high definition for now
-    # g = (size / max(im.size))  # gain
-    # im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)
-    
-    # initializing confidence threshold
-    model.conf = threshold
-    # inference
-    results = model(im)
-    numpy_image = results.render()[0]
-    output_image = Image.fromarray(numpy_image)
-
-    return output_image
-
-title = "YOLOv5 - Auction sale catalogues layout analysis"
-
-description = "<p style='text-align: center'>YOLOv5 Gradio demo for auction sales catalogues layout analysis. Detecting titles and catalogues entries.</p>"
-
-article = "<p style='text-align: center'>YOLOv5 source code : <a href='https://github.com/ultralytics/yolov5'>Source code</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
-
-
-demo=gr.Interface(fn=predict,
-            inputs=[gr.Image(type="pil", label="document image"), gr.Slider(maximum=1, step=0.01, value=0.50)],
-            outputs=gr.Image(type="pil", label="annotated document").style(height=700),
-            title=title,
-            description=description,
-            article=article,
-            theme="huggingface")
-
-
-if __name__ == "__main__":
-    demo.launch(debug=True)
-
-
+# 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=1920):
+    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)