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from tensorflow.keras.models import load_model |
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import gradio as gr |
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from PIL import Image |
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import numpy as np |
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model = load_model('Pikachu_and_Raichu.h5') |
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labels = ['Pikachu', 'Raichu'] |
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def predict(img): |
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img=img.reshape(160,160,3) |
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"""images_list = [] |
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images_list.append(np.array(img)) |
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x = np.asarray(images_list)""" |
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prediction = model.predict(img).flatten() |
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confidences = {labels[i]: float(prediction[i]) for i in range(2)} |
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return confidences |
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image = gr.inputs.Image(shape=(160, 160)) |
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label = gr.outputs.Label(num_top_classes=2) |
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gr.Interface(fn=predict, inputs=image, title="Garbage Classifier", |
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description="Tradio.",outputs=label,interpretation='default').launch(debug=True,enable_queue=True) |
