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import numpy as np |
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import tensorflow as tf |
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import cv2 |
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import gradio as gr |
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from tensorflow import keras |
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model = keras.models.load_model('model_InceptionV3.h5') |
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class_mapping = {1: 'Собака', 2: 'Кінь', 3: 'Слон', 4:'Метелик', |
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5: 'Курка', 6: 'Кіт', 7:'Корова', 8: 'Вівця', |
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9: 'Павук', 10: 'Білка' |
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} |
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def predict_image(image): |
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image = cv2.resize(image, (224, 224)) |
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image = np.asarray(image) |
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image = image.astype('float32') / 255.0 |
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predictions = model.predict(np.expand_dims(image, axis=0))[0] |
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prediction = {} |
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for index, probability in enumerate(predictions) : |
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prediction[class_mapping[index+1]] = float(round(probability, 3)) |
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print(prediction) |
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return prediction |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown("What animal is in the picture") |
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with gr.Tab("Predict image"): |
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image_input = gr.Image(label="Upload image") |
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output = gr.Label(label="Animal predicted by neural network") |
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image_button = gr.Button("Predict") |
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image_button.click(predict_image, inputs=image_input, outputs=output) |
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demo.launch() |
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