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import base64 |
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import json |
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import pickle |
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import pandas as pd |
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import streamlit as st |
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import torch |
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import torch.nn as nn |
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import transformers |
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from model.funcs import (create_model_and_tokenizer, execution_time, |
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load_model, predict_sentiment) |
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from model.model import LSTMConcatAttentionEmbed |
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from preprocessing.preprocessing import data_preprocessing |
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from preprocessing.rnn_preprocessing import preprocess_single_string |
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def get_base64(file_path): |
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with open(file_path, "rb") as file: |
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base64_bytes = base64.b64encode(file.read()) |
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base64_string = base64_bytes.decode("utf-8") |
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return base64_string |
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def set_background(png_file): |
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bin_str = get_base64(png_file) |
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page_bg_img = ( |
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""" |
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<style> |
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.stApp { |
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background-image: url("data:image/png;base64,%s"); |
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background-size: auto; |
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} |
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</style> |
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""" |
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% bin_str |
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) |
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st.markdown(page_bg_img, unsafe_allow_html=True) |
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set_background("main_background.png") |
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@st.cache_resource |
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def load_logreg(): |
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with open("vectorizer.pkl", "rb") as f: |
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logreg_vectorizer = pickle.load(f) |
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with open("logreg_model.pkl", "rb") as f: |
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logreg_predictor = pickle.load(f) |
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return logreg_vectorizer, logreg_predictor |
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logreg_vectorizer, logreg_predictor = load_logreg() |
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@st.cache_resource |
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def load_lstm(): |
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with open("model/vocab.json", "r") as f: |
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vocab_to_int = json.load(f) |
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with open("model/int_vocab.json", "r") as f: |
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int_to_vocab = json.load(f) |
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model_concat_embed = LSTMConcatAttentionEmbed() |
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model_concat_embed.load_state_dict(torch.load("model/model_weights.pt")) |
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return vocab_to_int, int_to_vocab, model_concat_embed |
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vocab_to_int, int_to_vocab, model_concat_embed = load_lstm() |
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@st.cache_resource |
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def load_bert(): |
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model_class = transformers.AutoModel |
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tokenizer_class = transformers.AutoTokenizer |
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pretrained_weights = "cointegrated/rubert-tiny2" |
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weights_path = "model/best_bert_weights.pth" |
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model = load_model(model_class, pretrained_weights, weights_path) |
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tokenizer = tokenizer_class.from_pretrained(pretrained_weights) |
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return model, tokenizer |
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model, tokenizer = load_bert() |
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@execution_time |
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def plot_and_predict(review: str, SEQ_LEN: int, model: nn.Module): |
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inp = preprocess_single_string(review, SEQ_LEN, vocab_to_int) |
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model.eval() |
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with torch.inference_mode(): |
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pred, _ = model(inp.long().unsqueeze(0)) |
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pred = pred.sigmoid().item() |
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return 1 if pred > 0.75 else 0 |
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def preprocess_text_logreg(text): |
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clean_text = data_preprocessing( |
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text |
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) |
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vectorized_text = logreg_vectorizer.transform([" ".join(clean_text)]) |
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return vectorized_text |
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@execution_time |
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def predict_sentiment_logreg(text): |
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processed_text = preprocess_text_logreg(text) |
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prediction = logreg_predictor.predict(processed_text) |
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return prediction |
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metrics = { |
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"Models": ["Logistic Regression", "LSTM + attention", "ruBERTtiny2"], |
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"f1-macro score": [0.94376, 0.93317, 0.94070], |
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} |
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df = pd.DataFrame(metrics) |
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df.set_index("Models", inplace=True) |
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df.index.name = "Model" |
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st.sidebar.title("Model Selection") |
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model_type = st.sidebar.radio("Select Model Type", ["Classic ML", "LSTM", "BERT"]) |
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styled_text = """ |
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<style> |
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.styled-title { |
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color: #FF00FF; |
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font-size: 40px; |
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text-shadow: -2px -2px 4px #000000; |
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-webkit-text-stroke-width: 1px; |
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-webkit-text-stroke-color: #000000; |
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} |
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.positive { |
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color: #00FF00; |
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font-size: 30px; |
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text-shadow: -2px -2px 4px #000000; |
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-webkit-text-stroke-width: 1px; |
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-webkit-text-stroke-color: #000000; |
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} |
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.negative { |
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color: #FF0000; |
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font-size: 30px; |
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text-shadow: -2px -2px 4px #000000; |
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-webkit-text-stroke-width: 1px; |
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-webkit-text-stroke-color: #000000; |
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} |
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</style> |
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""" |
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st.markdown(styled_text, unsafe_allow_html=True) |
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st.markdown('<div class="styled-title">Review Prediction</div>', unsafe_allow_html=True) |
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text_input = st.text_input("Enter your review:") |
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if st.button("Predict"): |
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if model_type == "Classic ML": |
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prediction = predict_sentiment_logreg(text_input) |
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elif model_type == "LSTM": |
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prediction = plot_and_predict( |
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review=text_input, SEQ_LEN=25, model=model_concat_embed |
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) |
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elif model_type == "BERT": |
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prediction = predict_sentiment(text_input, model, tokenizer, "cpu") |
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if prediction == 1: |
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st.markdown( |
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f'<div class="positive">Отзыв положительный</div>', unsafe_allow_html=True |
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) |
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elif prediction == 0: |
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st.markdown( |
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f'<div class="negative">Отзыв отрицательный</div>', unsafe_allow_html=True |
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) |
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st.write(df) |
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