import numpy as np 
import tensorflow as tf
import pickle
import string
import pandas as pd
import gradio as gr

tr_stop_words = pd.read_csv('tr_stop_word.txt',header=None)

@tf.keras.utils.register_keras_serializable()
def custom_standardization(input_string):
    """ Remove html line-break tags and handle punctuation """
    no_uppercased = tf.strings.lower(input_string, encoding='utf-8')
    no_stars = tf.strings.regex_replace(no_uppercased, "\*", " ")
    no_repeats = tf.strings.regex_replace(no_stars, "devamını oku", "")    
    no_html = tf.strings.regex_replace(no_repeats, "<br />", "")
    no_digits = tf.strings.regex_replace(no_html, "\w*\d\w*","")
    no_punctuations = tf.strings.regex_replace(no_digits, f"([{string.punctuation}])", r" ")
    #remove stop words
    no_stop_words = ' '+no_punctuations+ ' '
    for each in tr_stop_words.values:
      no_stop_words = tf.strings.regex_replace(no_stop_words, ' '+each[0]+' ' , r" ")
    no_extra_space = tf.strings.regex_replace(no_stop_words, " +"," ")
    #remove Turkish chars
    no_I = tf.strings.regex_replace(no_extra_space, "ı","i")
    no_O = tf.strings.regex_replace(no_I, "ö","o")
    no_C = tf.strings.regex_replace(no_O, "ç","c")
    no_S = tf.strings.regex_replace(no_C, "ş","s")
    no_G = tf.strings.regex_replace(no_S, "ğ","g")
    no_U = tf.strings.regex_replace(no_G, "ü","u")

    return no_U
loaded_end_to_end_model = tf.keras.models.load_model("end_to_end_model")
pkl_file = open("id_to_category.pkl", "rb")
id_to_category = pickle.load(pkl_file)

def classify (text):
  pred=loaded_end_to_end_model.predict([text])
  return id_to_category[np.argmax(pred)]

examples=['Dün aldığım samsung telefon bugün şarj tutmuyor',
          'THY Uçak biletimi değiştirmek için başvurdum.  Kimse geri dönüş yapmadı!']

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