Update pages/✨second.py

pages/✨second.py

@@ -12,37 +12,35 @@ def clean(text):
     text = re.sub(r'@\w+',' ',text) # удаляем упоминания пользователей
     text = re.sub(r'#\w+', ' ', text) # удаляем хэштеги
     text = re.sub(r'\d+', ' ', text) # удаляем числа
-    text = text.translate(str.maketrans('', '', string.punctuation))
     return text
 
 # Загрузка весов модели
 
-model_filename = 'model_weights.pkl'
+model_filename = 'model_comments_weights.pkl'
 with open(model_filename, 'rb') as file:
     model = pickle.load(file)
 
 # Загрузка весов векторизатора
 vectorizer = CountVectorizer()
-vectorizer_filename = 'vectorizer_weights.pkl'
+vectorizer_filename = 'vectorizer_comments_weights.pkl'
 with open(vectorizer_filename, 'rb') as file:
     vectorizer = pickle.load(file)
 
 # Само приложение  
 
-st.title("CritiSense")
-st.subheader("Movie Review Sentiment Analyzer")
-st.write("CritiSense is a powerful app that analyzes the sentiment of movie reviews.") 
-st.write("Whether you want to know if a review is positive or negative, CritiSense has got you covered.") 
-st.write("Just enter the review, and our app will provide you with instant sentiment analysis.")
-st.write("Make informed decisions about movies with CritiSense!")
-user_review = st.text_input("Enter your review:", "")
+st.title("SafeTalk")
+st.write("Your Personal Comment Filter is an innovative application that harnesses the power of AI to distinguish toxic comments from the rest.") 
+st.write("Empowering users to navigate online discussions with confidence, SafeTalk ensures a more constructive and respectful online community by identifying and flagging harmful content.")
+user_review = st.text_input("Enter your comment:", "")
 user_review_clean = clean(user_review)
 user_features = vectorizer.transform([user_review_clean])
 prediction = model.predict(user_features)
 
-st.write("Review:", user_review)
+st.write("Comment:", user_review)
 
-if prediction == 1:
-    st.markdown("<p style='color: green;'>Sentiment: Positive</p>", unsafe_allow_html=True)
+if prediction == 0:
+    st.markdown("<p style='color: green;'>Non-toxic comment</p>", unsafe_allow_html=True)
 else:
-    st.markdown("<p style='color: red;'>Sentiment: Negative</p>", unsafe_allow_html=True)
+    st.markdown("<p style='color: red;'>Toxic comment</p>", unsafe_allow_html=True)
+
+