Delete pages/ml_reviews_class.py

pages/ml_reviews_class.py

@@ -1,48 +0,0 @@
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.linear_model import LogisticRegression
-import re
-import string
-import pickle
-import streamlit as st
-
-# Функция очистки текста
-def clean(text):
-    text = text.lower() # нижний регистр
-    text = re.sub(r'http\S+', " ", text) # удаляем ссылки
-    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 = '/home/nika/ds-phase-2/10-nlp/model_weights.pkl'
-with open(model_filename, 'rb') as file:
-    model = pickle.load(file)
-
-# Загрузка весов векторизатора
-vectorizer = CountVectorizer()
-vectorizer_filename = '/home/nika/ds-phase-2/10-nlp/vectorizer_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:", "")
-user_review_clean = clean(user_review)
-user_features = vectorizer.transform([user_review_clean])
-prediction = model.predict(user_features)
-
-st.write("Review:", user_review)
-
-if prediction == 1:
-    st.markdown("<p style='color: green;'>Sentiment: Positive</p>", unsafe_allow_html=True)
-else:
-    st.markdown("<p style='color: red;'>Sentiment: Negative</p>", unsafe_allow_html=True)