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import gradio as gr
from huggingface_hub import hf_hub_download
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)
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