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| import os | |
| import gradio as gr | |
| import numpy as np | |
| from tensorflow.keras.models import load_model | |
| import cv2 | |
| ### | |
| def image_predict (image): | |
| model_path = 'resnet_ct.h5' | |
| h5_model = load_model(model_path) | |
| image = np.array(image) / 255 | |
| image = np.expand_dims(image, axis=0) | |
| h5_prediction = h5_model.predict(image) | |
| print('Prediction from h5 model: {}'.format(h5_prediction)) | |
| print(h5_prediction) | |
| probability = h5_prediction[0] | |
| print("H5 Predictions:") | |
| print (probability) | |
| if probability[0] > 0.5: | |
| covid_chest_pred = str('%.2f' % (probability[0] * 100) + '% COVID-Positive') | |
| probability = (probability[0] * 100) | |
| else: | |
| covid_chest_pred = str('%.2f' % ((1 - probability[0]) * 100) + '% COVID-Negative') | |
| probability = ((1 - probability[0]) * 100) | |
| return covid_chest_pred | |
| myApp = gr.Interface(fn=image_predict, inputs="image", outputs="text") | |
| myApp.launch(auth=("admin", "pass1234"))#share=True |