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app.py
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import requests
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import tensorflow as tf
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inception_net = tf.keras.applications.MobileNetV2()
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import requests
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# Download human-readable labels for ImageNet.
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response = requests.get("https://git.io/JJkYN")
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labels = response.text.split("\n")
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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.mobilenet_v2.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(1000)}
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return confidences
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import gradio as gr
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gr.Interface(fn=classify_image,
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inputs=gr.inputs.Image(shape=(224, 224)),
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outputs=gr.outputs.Label(num_top_classes=3),
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examples=["banana.jpg", "car.jpg"],
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theme="default",
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css=".footer{display:none !important}").launch()
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