| import gradio as gr | |
| from keras.models import model_from_json | |
| from tensorflow.keras.preprocessing import image | |
| import numpy as np | |
| def predict_age_gender(image): | |
| json_file = open('xception.json', 'r') | |
| loaded_file_json = json_file.read() | |
| json_file.close() | |
| model = model_from_json(loaded_file_json) | |
| model.load_weights('xception_modelv2.h5') | |
| images = np.resize(image, new_shape=(1, 128, 128, 3)) | |
| img_pixels = np.expand_dims(images, axis=0) | |
| img_pixels = images.astype('float') | |
| img_pixels = img_pixels.reshape((1, 128, 128, 3)) | |
| img_pixels /= 255 | |
| predict = model.predict(img_pixels) | |
| gender_predict = predict[0] | |
| age_predict = predict[1] | |
| return {'Gender': ['Fmale' if gender_predict > 0.5 else 'Male'], 'Age': age_predict[0][0]} | |
| iface = gr.Interface(predict_age_gender, gr.Image(), gr.Text()) | |
| iface.launch() | |