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pages/ml_reviews_class.py

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+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)

pages/model_comments_weights.pkl

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+oid sha256:9a2642dc31855483b855511afc099cd80d65aac75520e8460b01c4164ad48a40
+size 464784

pages/model_history.pt

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+oid sha256:7251915a6188c935eaa9d581c41f03c238a1058d3f7da77b95cd39252af42b13
+size 551321853

pages/model_weights.pkl

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+oid sha256:7b27177e32cde75392fe954cc9ccceb799dc09a65a9144d1c07df9726134e8ec
+size 1238083

pages/vectorizer_comments_weights.pkl

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+oid sha256:e6b8c9839ddfbe2a30d82701c713528ec1012bebe68bf3e156124d163f587c35
+size 1281514

pages/vectorizer_weights.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:045809c9e4cd5709117634017a3da547cd6a927a0380773dce4811ce0b97e015
+size 2466402