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import torch |
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import gradio as gr |
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from huggingface_hub import hf_hub_download |
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from PIL import Image |
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REPO_ID = "thoucentric/Shelf_Objects_Detection_Yolov7_Pytorch" |
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FILENAME = "best.pt" |
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yolov7_custom_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) |
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model = torch.hub.load('',model='custom', path_or_model=yolov7_custom_weights, force_reload=True) |
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def object_detection(im, size=640): |
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results = model(im) |
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results.render() |
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return Image.fromarray(results.imgs[0]) |
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title = "Yolov7 Custom" |
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image = gr.inputs.Image(shape=(640, 640), image_mode="RGB", source="upload", label="Upload Image", optional=False) |
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outputs = gr.outputs.Image(type="pil", label="Output Image") |
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Custom_description="<center>Custom Training Performed on Kaggle <a href='https://www.kaggle.com/code/owaiskhan9654/shelf-object-detection-yolov7-pytorch/notebook' style='text-decoration: underline' target='_blank'>Link</a> </center><br> <center>Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors </center> <br> on around 140 general items in Stores" |
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Footer = ( |
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"<center>Model Trained by: Owais Ahmad Data Scientist at <b> Thoucentric </b> <a href=\"https://www.linkedin.com/in/owaiskhan9654/\">Visit Profile</a> <br></center>" |
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"<center> Model Trained Kaggle Kernel <a href=\"https://www.kaggle.com/code/owaiskhan9654/shelf-object-detection-yolov7-pytorch/notebook\">Link</a> <br></center>" |
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"<center> HuggingFace🤗 Model Deployed Repository <a href=\"https://huggingface.co/thoucentric/Shelf_Objects_Detection_Yolov7_Pytorch\">Link</a> <br></center>" |
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) |
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examples1=[["Image1.jpg"],["Image2.jpg"],["Image3.jpg"],["Image4.jpg"],["Image5.jpg"],["Image6.jpg"]] |
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Top_Title="<center>Yolov7 🚀 Custom Trained by <a href='https://www.linkedin.com/in/owaiskhan9654/' style='text-decoration: underline' target='_blank'>Owais Ahmad </center></a> on around 140 general items in Stores" |
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css = ".output-image, .input-image {height: 50rem !important; width: 100% !important;}" |
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css = ".image-preview {height: auto !important;}" |
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gr.Interface( |
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fn=object_detection, |
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inputs=image, |
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outputs=outputs, |
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title=Top_Title, |
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description=Custom_description, |
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article=Footer, |
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examples=examples1).launch() |
