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| import tensorflow as tf | |
| import pandas as pd | |
| import gradio as gr | |
| authors_df = pd.read_csv('authors.csv') | |
| labels = sorted(list(authors_df.name)) | |
| model = tf.keras.models.load_model('efficientnetb0.h5') | |
| description = 'This is a DEMO that attempts to recognize the inspirations used by the AI art generator. After uploading a picture of an image, the application displays the predicted artist along with the probability of predicting the top three authors.The DEMO uses EfficientNetB0 convolutional neural network as a base model whose classifier was modified and trained the 8,000+ paintings from [Kaggle](https://www.kaggle.com/datasets/ikarus777/best-artworks-of-all-time) dataset. Model trained by osydorchuk89. Given the dataset limitations, the model only recognizes paintings of [50 artists](https://huggingface.co/spaces/osydorchuk/painting_authors/blob/main/authors.csv).' | |
| def predict_author(input): | |
| if input is None: | |
| return 'Please upload an image' | |
| input = input.reshape((-1, 224, 224, 3)) | |
| prediction = model.predict(input).flatten() | |
| confidences = {labels[i]: float(prediction[i]) for i in range(50)} | |
| return confidences | |
| demo = gr.Interface( | |
| title='the AI art generator sources of inspiration', | |
| description=description, | |
| fn=predict_author, | |
| inputs=gr.Image(shape=(224, 224)), | |
| outputs=gr.Label(num_top_classes=3), | |
| examples=['test_pics/eva_miro.jpg', 'test_pics/eva_bosch.jpg', 'test_pics/eva_miro_2.jpg', 'test_pics/eva_rtology.jpg'] | |
| ) | |
| demo.launch() |