import gradio as gr
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing import image

model = tf.keras.models.load_model('model.hdf5')

def predict(img_path):
    test_image = image.load_img(img_path, target_size=(224,224))
    test_image = image.img_to_array(test_image)
    test_image = test_image/255.0
    test_image = np.expand_dims(test_image, axis = 0) 
    prediction = model.predict(test_image)
    result = np.argmax(prediction, axis=1)
    if result[0] == 0:
        prediction = 'COVID DETECTED'
    elif result[0] == 1:
        prediction = 'HEALTHY'
    elif result[0] == 2:
        prediction = 'LUNG CANCER DETECTED'
    else:
        prediction = 'PNEUMONIA DETECTED'
    return prediction

iface = gr.Interface(fn=predict, inputs="text", outputs="text")
iface.launch(share=True)