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| '''Artist Classifier | |
| prototype | |
| --- | |
| - 2022-01-18 jkang first created | |
| ''' | |
| import matplotlib.pyplot as plt | |
| import matplotlib.image as mpimg | |
| import seaborn as sns | |
| import json | |
| import skimage.io | |
| from loguru import logger | |
| from huggingface_hub import from_pretrained_keras | |
| import gradio as gr | |
| import tensorflow as tf | |
| tfk = tf.keras | |
| from gradcam_utils import get_img_4d_array, make_gradcam_heatmap, align_image_with_heatmap | |
| # ---------- Settings ---------- | |
| ARTIST_META = 'artist.json' | |
| TREND_META = 'trend.json' | |
| EXAMPLES = ['monet.jpg'] | |
| # ---------- Logging ---------- | |
| logger.add('app.log', mode='a') | |
| logger.info('============================= App restarted =============================') | |
| # ---------- Model ---------- | |
| logger.info('loading models...') | |
| artist_model = from_pretrained_keras("jkang/drawing-artist-classifier") | |
| trend_model = from_pretrained_keras("jkang/drawing-artistic-trend-classifier") | |
| logger.info('both models loaded') | |
| def load_image_as_array(image_file): | |
| img = skimage.io.imread(image_file, as_gray=False, plugin='matplotlib') | |
| if (img.shape[-1] > 3) & (remove_alpha_channel): # if RGBA | |
| img = img[..., :-1] | |
| return img | |
| def load_image_as_tensor(image_file): | |
| img = tf.io.read_file(image_file) | |
| img = tf.io.decode_jpeg(img, channels=3) | |
| return img | |
| def predict(input_image): | |
| img_3d_array = load_image_as_array(input_image) | |
| img_4d_tensor = load_image_as_tensor(input_image) | |
| logger.info(f'--- {input_image} loaded') | |
| artist_model(img_4d_tensor); | |
| trend_model(img_4d_tensor); | |
| return img_3d_array | |
| iface = gr.Interface( | |
| predict, | |
| title='Predict Artist and Artistic Trend of Drawings 🎨👨🏻🎨 (prototype)', | |
| description='Upload a drawing and the model will predict how likely it seems given 10 artists and their trend/style', | |
| inputs=[ | |
| gr.inputs.Image(label='Upload a drawing/image', type='file') | |
| ], | |
| outputs=[ | |
| gr.outputs.Image(label='Prediction') | |
| ], | |
| examples=EXAMPLES, | |
| ) | |
| iface.launch(debug=True, enable_queue=True) |