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import gradio as gr |
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from fastai.vision.all import * |
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learn=load_learner('export.pkl') |
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labels = learn.dls.vocab |
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def predict(img): |
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img = PILImage.create(img) |
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pred,pred_idx,probs = learn.predict(img) |
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return {labels[i]: float(probs[i]) for i in range(len(labels))} |
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with open("./1001epochs.png", "rb") as f: |
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image_data = f.read() |
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image_base64 = base64.b64encode(image_data).decode("utf-8") |
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allow_flagging = "never" |
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title = f""" |
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<h2 style="background-image: linear-gradient(to right, #3A5FCD, #87CEFA); -webkit-background-clip: text; |
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-webkit-text-fill-color: transparent; text-align: center;"> |
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Emergency Vehicle Classifier |
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</h2> |
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""" |
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description = f""" |
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<div style="display: flex; align-items: center; justify-content: center; flex-direction: column;"> |
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<p style="font-size: 18px; color: #4AAAFF; text-align: center;"> |
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Simply upload a photo and let our sophisticated AI system determine the specific type of emergency vehicle depicted. |
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</p> |
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<div style="display: flex; align-items: center; margin-bottom: 0px;"> |
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<img src='data:image/jpeg;base64,{image_base64}' width='50' height='30' style="margin-right: 5px;"/> |
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<p style="font-size: 14px; color: #555;"> |
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Disclaimer: This web app is for demonstration purposes only and not intended for commercial use. Contact: contact@1001epochs.co.uk for full solution. |
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</p> |
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</div> |
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</div> |
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""" |
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interpretation='default' |
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gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3), title=title, interpretation=interpretation, description=description).launch() |
