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from sklearn.feature_extraction.text import CountVectorizer | |
from sklearn.linear_model import LogisticRegression | |
import re | |
import string | |
import pickle | |
import time | |
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 = 'model_weights.pkl' | |
with open(model_filename, 'rb') as file: | |
model = pickle.load(file) | |
# Загрузка весов векторизатора | |
vectorizer = CountVectorizer() | |
vectorizer_filename = '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]) | |
start_ml=time.time() | |
prediction = model.predict(user_features) | |
end_ml=time.time() | |
st.write("Review:", user_review) | |
ml_time=end_ml-start_ml | |
execution_time_container = st.empty() # Создаем пустой контейнер для отображения времени выполнения | |
if st.button("Analyze Sentiment"): | |
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) | |
st.markdown(f"Execution Time: {ml_time:.5f} seconds") | |
execution_time_container.text(f"Execution Time: {ml_time:.5f} seconds") | |