Update app.py

app.py

@@ -2,7 +2,7 @@
 """
 Created on Mon Jun  6 20:56:08 2022
 
-@author: User
+@author: Aziz Baran Kurtuluş
 """
 import os
 os.system('pip install nltk')
@@ -15,19 +15,18 @@ nltk.download('stopwords')
 nltk.download('wordnet')
 nltk.download('omw-1.4')
 
-# importing relevant python packages
+
 import streamlit as st
 import joblib
-# preprocessing
 import re
 import string
 import nltk
 from nltk.corpus import stopwords
 from nltk.stem import WordNetLemmatizer 
 from sklearn.feature_extraction.text import TfidfVectorizer
-# modeling
 
-# creating page sections
+
+
 site_header = st.container()
 business_context = st.container()
 data_desc = st.container()
@@ -44,8 +43,7 @@ with site_header:
 with tweet_input:
     st.header('Is Your Text Considered Toxic?')
     st.write("""*Please note that this prediction is based on how the model was trained, so it may not be an accurate representation.*""")
-    # user input here
-    user_text = st.text_input('Enter Text', max_chars=280) # setting input as user_text
+    user_text = st.text_input('Enter Text', max_chars=280)
 
 with model_results:    
     st.subheader('Prediction:')
@@ -64,7 +62,7 @@ with model_results:
         for word in stopwords_removed:
             lemmatized_output.append(lemmatizer.lemmatize(word))
 
-        # instantiating count vectorizor
+        # instantiating tfidf vectorizor
         tfidf = TfidfVectorizer(stop_words= stop_words, ngram_range=(1,2))
         X_train = joblib.load(open('resources/X_train.pickel', 'rb'))
         X_test = lemmatized_output
@@ -74,7 +72,7 @@ with model_results:
         # loading in model
         final_model = joblib.load(open('resources/final_bayes.pickel', 'rb'))
 
-        # apply model to make predictions
+        # applying the model to make predictions
         prediction = final_model.predict(X_test_count[0])
 
         if prediction == 0: