Create app.py

app.py

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+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(path+"end_to_end_model")
+pkl_file = open(path+"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()