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* changes * changes * revert changes * changes * add changeset * notebooks script * changes * changes --------- Co-authored-by: Ali Abid <aliabid94@gmail.com> Co-authored-by: gradio-pr-bot <gradio-pr-bot@users.noreply.github.com> Co-authored-by: Ali Abdalla <ali.si3luwa@gmail.com>
35 lines
882 B
Python
35 lines
882 B
Python
import os
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import requests
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import tensorflow as tf
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import gradio as gr
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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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os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"),
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os.path.join(os.path.dirname(__file__), "images/lion.jpg")
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]
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)
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if __name__ == "__main__":
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demo.launch()
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