from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np
import gradio as gr
import pandas as pd
  
def format_label(label):
  """
  From '0 rùa khác\n' to 'rùa khác'
  """
  return label[label.find(" ")+1:-1]

def predict(image):

  # Load the model
  model = load_model('keras_model.h5')

  # Create the array of the right shape to feed into the keras model
  # The 'length' or number of images you can put into the array is
  # determined by the first position in the shape tuple, in this case 1.
  data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
  
  #resize the image to a 224x224 with the same strategy as in TM2:
  #resizing the image to be at least 224x224 and then cropping from the center
  size = (224, 224)
  image = ImageOps.fit(image, size, Image.ANTIALIAS)

  #turn the image into a numpy array
  image_array = np.asarray(image)
  # Normalize the image
  normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
  # Load the image into the array
  data[0] = normalized_image_array

  # run the inference
  pred = model.predict(data)
  pred = pred.tolist()

  with open('labels.txt','r') as f:
    labels = f.readlines()
  

  result = {format_label(labels[i]): round(pred[0][i],2) for i in range(len(pred[0]))}
  
  return result
  
  
description="""

Description

"""

title = """

Title

"""

examples = [['example1.jpg'], ['example2.jpg'], ['example3.jpg']]

gr.Interface(fn=predict, 
             inputs=gr.Image(type="pil", label="Input Image"),
             outputs=[gr.Label()],
             live=True,
             title=title,
             description=description,
             examples=examples).launch()