app.py

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 = gr.Interface(fn=image_predict, inputs=gr.Image(type="numpy"), outputs=gr.Text()).launch()
myApp.launch(share=True)#share=True