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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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import gdown |
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from PIL import Image |
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labels = [ |
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"plane", |
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"car", |
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"bird", |
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"cat", |
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"deer", |
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"dog", |
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"frog", |
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"horse", |
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"ship", |
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"truck", |
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] |
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url = "https://drive.google.com/uc?id=12700bE-pomYKoVQ214VrpBoJ7akXcTpL" |
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output = "modelV2Lmixed.keras" |
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gdown.download(url, output, quiet=False) |
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inception_net = tf.keras.models.load_model("./modelV2Lmixed.keras") |
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def classify_image(inp): |
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inp = inp.reshape((-1, 224, 224, 3)) |
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inp = tf.keras.applications.efficientnet.preprocess_input(inp) |
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prediction = inception_net.predict(inp).flatten() |
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confidences = {labels[i]: float(prediction[i]) for i in range(10)} |
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return confidences |
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import gradio as gr |
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gr.Interface( |
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fn=classify_image, |
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inputs=gr.inputs.Image(shape=(32, 32)), |
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outputs=gr.outputs.Label(num_top_classes=3), |
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examples=["03_cat.jpg", "05_dog.jpg"], |
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theme="default", |
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css=".footer{display:none !important}", |
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).launch() |
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