import gradio as gr


import tensorflow as tf
import numpy as np
import pickle

# Load model, including its weights and the optimizer
model = tf.keras.models.load_model('core4.h5')

# load tokenizer
with open('tokenizer.pickle', 'rb') as handle:
    tokenize = pickle.load(handle)

text_labels = ['How to apply', 'how much can I get', 'who can apply']

# model.summary() # model architecture

def greet(name):

  tokenizedText = tokenize.texts_to_matrix([string])
  prediction = model.predict(np.array([tokenizedText[0]]))
  predicted_label = text_labels[np.argmax(prediction)]

  print(prediction[0][np.argmax(prediction)])
  print("Predicted label: " + predicted_label + "\n")

  return predicted_label


#One testing case


iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()