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( 'kidney2.h5')

def take_img(img):
  
  resize = tf.image.resize(img, (224,224))
  gray = tfio.experimental.color.bgr_to_rgb(resize)
  yhat = loaded_model.predict(np.expand_dims(gray/255, 0))
  label_names = {
  "1": "Cyst",
  "2": "Normal",
  "3": "Stone",
  "4": "Tumor"    
  }
  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]
  q= yhat[0][0]
  w = yhat[0][1]
  e = yhat[0][2]
  r = yhat[0][3] 

  q = str(q)
  w = str(w)
  e = str(e)
  r = str(r)  
 
  return {'cryst' : q ,'Normal' : w ,'Stone' : e ,'Tumour' : r }



image = gr.inputs.Image(shape=(224,224))

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