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import gradio as gr |
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from keras.models import model_from_json |
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from tensorflow.keras.preprocessing import image |
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import numpy as np |
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def predict_age_gender(image): |
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json_file_2 = open('model_gender_2.json', 'r') |
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loaded_file_json_2 = json_file_2.read() |
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json_file_2.close() |
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model_2 = model_from_json(loaded_file_json_2) |
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model_2.load_weights('model_gender_2.h5') |
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json_file = open('xception.json', 'r') |
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loaded_file_json = json_file.read() |
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json_file.close() |
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model = model_from_json(loaded_file_json) |
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model.load_weights('xception_modelv2.h5') |
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images = np.resize(image, new_shape=(1, 128, 128, 3)) |
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img_pixels = np.expand_dims(images, axis=0) |
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img_pixels = img_pixels.astype('float64') |
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img_pixels = img_pixels.reshape((1, 128, 128, 3)) |
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img_pixels /= 255 |
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class_names_gender = ['Fmale', 'Male'] |
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gender_predict = model_2.predict(img_pixels) |
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predict = model.predict(img_pixels) |
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age_result = predict[1][0].round() |
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return 'Female' if gender_predict<0.5 else 'Male', age_result[0] |
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iface = gr.Interface(predict_age_gender, gr.Image(), [gr.Label(label='Gender'), gr.Text(label='Age')]) |
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iface.launch() |
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