from PIL import Image
import cv2
import numpy as np

import tensorflow as tf
from keras.models import load_model

import gradio as gr

model =load_model('BrainTumor10Epochs.h5')

def getResult(inp):
    
    inp=np.array(inp)
    input_img = np.expand_dims(inp, axis=0)
    result=np.max(model.predict(input_img))

    if result==0:
        return "No Brain Tumor"
    elif result==1:
        
        return "Yes Brain Tumor"
    


examples = [
    ["example_images/No_1.jpg"],
    ["example_images/No_2.jpg"],
    ["example_images/No_3.jpg"],
    ["example_images/Yes_1.jpg"],
    ["example_images/Yes_2.jpg"],
    ["example_images/Yes_3.jpg"]
]


iface = gr.Interface(
    fn=getResult, 
    inputs=gr.Image(shape=(64, 64)),
    outputs=gr.Label(num_top_classes=2),
    title="Brain Tumor Classification",
    description="Upload the MRI Image of the Brain and it will tell whether it has a Brain Tumor or not",
    examples=examples
)
if __name__ == "__main__":
    iface.launch()