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import os |
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import numpy as np |
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import tensorflow as tf |
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from tensorflow import keras |
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from tensorflow.keras.preprocessing import image |
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import gradio as gr |
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artists = ['botero', |
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'davinci', |
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'elgreco', |
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'melzi', |
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'michelangelo', |
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'modigliani', |
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'picasso', |
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'rembrandt', |
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'rubens', |
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'vermeer'] |
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model = keras.models.load_model('models/model_2023-08-29T1856_ep40_bz32_img224_nc10.h5') |
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description = 'Welcome!!! This app was built based on Gradio. The aim of this App is to predict the author of a painting. In this first version of the App, we only considered 10 authors [botero, davinci, elgreco, melzi, michelangelo,modigliani, picasso, rembrandt, rubens, vermeer]' |
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def predicting_author(input): |
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try: |
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if input is None: |
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return 'Please upload an image' |
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x = image.img_to_array(input) |
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x = np.expand_dims(x, axis=0) |
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x = x.astype('float32') / 255.0 |
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prediction = model.predict(x) |
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class_probabilities = prediction[0] |
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results = {artists[i]: float(class_probabilities[i]) for i in range(len(artists))} |
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return results |
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except Exception as e: |
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print("An error occurred:", e) |
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import traceback |
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traceback.print_exc() |
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return "An error occurred" |
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demo = gr.Interface( |
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title='Predicting paintings authors', |
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description=description, |
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fn=predicting_author, |
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inputs=gr.Image(shape=(224, 224)), |
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outputs=gr.Label(num_top_classes=3), |
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examples=['./test_images/image1.jpg', './test_images/image2.jpg', './test_images/image3.jpg'] |
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) |
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demo.launch() |
