| import gradio as gr |
| import tensorflow as tf |
| from tensorflow.keras.preprocessing import image |
| import numpy as np |
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| |
| model_url = "https://teachablemachine.withgoogle.com/models/ZPfAhDYCh/" |
| model = tf.keras.models.load_model(model_url + "model.json") |
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| |
| def preprocess_image(img): |
| img = image.img_to_array(img) |
| img = np.expand_dims(img, axis=0) |
| img /= 255.0 |
| return img |
|
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| |
| def predict_image(img): |
| img = preprocess_image(img) |
| prediction = model.predict(img) |
| return {class_name: float(prediction[0][i]) for i, class_name in enumerate(classes)} |
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| |
| metadata_url = model_url + "metadata.json" |
| metadata = tf.keras.utils.get_file("metadata.json", metadata_url) |
| classes = tf.keras.utils.get_file("classes.txt", metadata_url.replace("metadata.json", "classes.txt")).read().splitlines() |
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| |
| iface = gr.Interface(fn=predict_image, inputs="image", outputs="label", live=True, capture_session=True) |
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| |
| iface.launch() |
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