2021-12-14 14:02:19 +08:00
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import requests
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2022-01-21 21:44:12 +08:00
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import tensorflow as tf
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2021-12-14 14:02:19 +08:00
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2022-01-21 21:44:12 +08:00
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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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2021-12-14 14:02:19 +08:00
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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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2022-01-21 21:44:12 +08:00
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2021-12-14 14:02:19 +08:00
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def classify_image(inp):
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2022-01-21 21:44:12 +08:00
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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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2021-12-14 14:02:19 +08:00
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image = gr.inputs.Image(shape=(224, 224))
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label = gr.outputs.Label(num_top_classes=3)
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2022-01-21 21:44:12 +08:00
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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=[["images/cheetah1.jpg"], ["images/lion.jpg"]],
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).launch()
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