import gradio as gr
import tensorflow as tf 
import tensorflow.keras 
import matplotlib.pyplot as plt
import cv2
import tensorflow_io as tfio
import numpy as np

loaded_model = tf.keras.models.load_model( 'brain1.h5')

def take_img(img):
  
  resize = tf.image.resize(img, (128,128))
  gray = tfio.experimental.color.bgr_to_rgb(resize)
  yhat = loaded_model.predict(np.expand_dims(gray/255, 0))
  label_names = {
  "1": "Tumor",
  "2": "Normal"}
  classes_x=np.argmax(yhat,axis=1)
  a = classes_x[0]
  input_value = a + 1
  input_str = str(input_value)
  predicted_label = label_names[input_str]
  tumor = yhat[0][0]
  tumor = str(tumor)
  normal = yhat[0][1]
  normal = str(normal)
  return {'Tumour': tumor, 'Normal':normal}



image = gr.inputs.Image(shape=(128,128))

label = gr.outputs.Label('ok')
gr.Interface(fn=take_img, inputs=image, outputs="label",interpretation='default').launch(debug='True')