#import tensorflow_addons as tfa
import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras.models import load_model
import tensorflow_addons as tfa
import os
import numpy as np


labels= {'Burger King': 0, 'KFC': 1,'McDonalds': 2,'Other': 3,'Starbucks': 4,'Subway': 5}
HEIGHT,WIDTH=224,224
NUM_CLASSES=6

model=load_model('best_model.h5')

def classify_image(inp):
  np.random.seed(143)
  inp = inp.reshape((-1, HEIGHT,WIDTH, 3))
  inp = tf.keras.applications.nasnet.preprocess_input(inp) 
  prediction = model.predict(inp)
  #label = dict((v,k) for k,v in labels.items())
  predicted_class_indices=np.argmax(prediction,axis=1)
  return {labels[i]: float(predicted_class_indices[i]) for i in range(NUM_CLASSES)}


    
image = gr.Image(shape=(HEIGHT,WIDTH),label='Input')
label = gr.Textbox()

gr.Interface(fn=classify_image, inputs=image, outputs=label, title='Brand Logo Detection').launch(debug=False)