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37 lines
947 B
Python
37 lines
947 B
Python
import requests
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import tensorflow as tf # type: ignore
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import gradio as gr
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# get_image() returns the file path to sample images included with Gradio
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from gradio.media import get_image
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inception_net = tf.keras.applications.MobileNetV2() # load the model
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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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return {labels[i]: float(prediction[i]) for i in range(1000)}
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image = gr.Image()
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label = gr.Label(num_top_classes=3)
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demo = gr.Interface(
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fn=classify_image,
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inputs=image,
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outputs=label,
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examples=[
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get_image("cheetah1.jpg"),
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get_image("lion.jpg")
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],
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api_name="predict"
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)
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if __name__ == "__main__":
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demo.launch()
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