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import gradio as gr |
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import tensorflow as tf |
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import numpy as np |
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from PIL import Image |
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import tensorflow.keras as keras |
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from tensorflow.keras.models import load_model |
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model = load_model('model520.h5') |
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classnames = ['paper', 'cardboard', 'plastic', 'metal', 'food', 'battery', 'shoes', 'clothes', 'glass', 'medical'] |
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def predict_image(img): |
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img_4d=img.reshape(-1,224, 224,3) |
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prediction=model.predict(img_4d)[0] |
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return {classnames[i]: float(prediction[i]) for i in range(len(classnames))} |
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image = gr.inputs.Image(shape=(224, 224)) |
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label = gr.outputs.Label(num_top_classes=3) |
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article="<p style='text-align: center; font-weight:bold;'>Model based on the VGG-16 CNN</p>" |
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gr.Interface(fn=predict_image, inputs=image, title="Garbage Classifier VGG-16", |
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description="This is a Garbage Classification Model Trained using VGG-16 architecture. Deployed to Hugging Face using Gradio.", outputs=label, article=article, enable_queue=True, interpretation='default').launch(share="True") |
