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| import streamlit as st | |
| import torch | |
| import re | |
| import json | |
| import time | |
| from nltk.corpus import stopwords | |
| from Models.model_file import data_preprocessing, preprocess_single_string, LSTMBahdanauAttention | |
| from nltk.corpus import stopwords | |
| stop_words = set(stopwords.words('russian')) | |
| with open('Weights/vocab_to_int.json', 'r') as file: | |
| vocab_to_int = json.load(file) | |
| SEQ_LEN = 96 | |
| model_bah = LSTMBahdanauAttention() | |
| model_bah.load_state_dict(torch.load('Weights/final_model_bah.pth')) | |
| model_bah.eval() | |
| def analyze_sentiment(text): | |
| preprocessed_text = data_preprocessing(text) | |
| sample = preprocess_single_string(preprocessed_text, SEQ_LEN, vocab_to_int) | |
| with torch.no_grad(): | |
| probability = model_bah(sample.unsqueeze(0))[0].sigmoid().item() | |
| return probability | |
| def lstm_model_page(): | |
| st.title("Классификация отзывов") | |
| user_input = st.text_area("Введите ваш отзыв:") | |
| if st.button("Классифицировать"): | |
| start_time = time.time() | |
| probability = analyze_sentiment(user_input) | |
| end_time = time.time() | |
| execution_time = end_time - start_time | |
| if probability > 0.5: | |
| st.write("Отзыв положительный 🌟") | |
| else: | |
| st.write("Отзыв отрицательный 😞") | |
| st.write(f'Время предсказания: {execution_time:.4f} секунд') | |