import streamlit as st import torch import torch.nn as nn import pandas as pd import numpy as np import pickle from nltk.tokenize import RegexpTokenizer from nltk.corpus import stopwords from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.linear_model import LogisticRegression import re import string from nltk.stem import WordNetLemmatizer import time import transformers import json from biLSTM1 import biLSTM from lstm_preprocessing import ( data_preprocessing, get_words_by_freq, padding, preprocess_single_string ) # 1-Lesha, 2-Lena, 3-Gal # +++++++++++ # 1 -Lesha # Load the saved model with open('logistic_regression_model.pkl', 'rb') as file: loaded_model_1 = pickle.load(file) with open('tfidf_vectorizer.pkl', 'rb') as file: vectorizer_1 = pickle.load(file) # Load the stop words stop_words = stopwords.words('english') # Create a tokenizer tokenizer = RegexpTokenizer(r'\w+') def data_preprocessing(text: str) -> str: """preprocessing string: lowercase, removing html-tags, punctuation and stopwords Args: text (str): input string for preprocessing Returns: str: preprocessed string """ text = text.lower() text = re.sub('<.*?>', '', text) # html tags text = ''.join([c for c in text if c not in string.punctuation])# Remove punctuation lemmatizer = WordNetLemmatizer() tokens = tokenizer.tokenize(text) tokens = [lemmatizer.lemmatize(word) for word in tokens if not word.isdigit() and word not in stop_words] return ' '.join(tokens) # ++++ # Lena def load_model_l(): model_finetuned = transformers.AutoModel.from_pretrained( "nghuyong/ernie-2.0-base-en", output_attentions = False, output_hidden_states = False ) model_finetuned.load_state_dict(torch.load('ErnieModel_imdb.pt', map_location=torch.device('cpu'))) tokenizer = transformers.AutoTokenizer.from_pretrained("nghuyong/ernie-2.0-base-en") return model_finetuned, tokenizer def preprocess_text(text_input, max_len, tokenizer): input_tokens = tokenizer( text_input, return_tensors='pt', padding=True, max_length=max_len, truncation = True ) return input_tokens def predict_sentiment(model, input_tokens): id2label = {0: "negative", 1: "positive"} output = model(**input_tokens).pooler_output.detach().numpy() with open('LogReg_imdb_Ernie.pkl', 'rb') as file: cls = pickle.load(file) result = id2label[int(cls.predict(output))] return result # ++++ # Gala with open('/home/galkalin/nlp_project/vocab_to_int.json', 'r') as fp: vocab_to_int = json.load(fp) VOCAB_SIZE = len(vocab_to_int)+1 EMBEDDING_DIM = 32 HIDDEN_DIM = 64 N_LAYERS = 3 SEQ_LEN = 128 def load_model_g(): model = biLSTM( vocab_size=VOCAB_SIZE, embedding_dim=EMBEDDING_DIM, hidden_dim=HIDDEN_DIM, n_layers=N_LAYERS ) model.load_state_dict(torch.load('biLSTM_model_do_05_lr001_best.pt', map_location=torch.device('cpu'))) return model device = 'cuda' if torch.cuda.is_available() else 'cpu' def predict_sentence(text: str, model: nn.Module) -> str: id2label = {0: "negative", 1: "positive"} output = model.to(device)(preprocess_single_string(text, SEQ_LEN, vocab_to_int).unsqueeze(0).to(device)) pred = int(output.round().item()) result = id2label[pred] return result # ++++++ # Lesha # Create the Streamlit app def main(): st.title('Sentiment Analysis App') st.header('Classic ML, ErnieModel, bidirectional LSTM') user_input = st.text_area('Please enter your review:') st.write(user_input) submit = st.button("Predict!") col1, col2,col3 = st.columns(3) if user_input is not None and submit: with col1: # Preprocess the user input preprocessed_input_1 = data_preprocessing(user_input) # Vectorize the preprocessed input input_vector = vectorizer_1.transform([preprocessed_input_1]) start_time = time.time() proba_1 = loaded_model_1.predict_proba(input_vector)[:, 1] # Predict the sentiment using the loaded model #prediction = loaded_model.predict(input_vector)[0] prediction_1 = round(proba_1[0]) end_time = time.time() # Display the predicted sentiment if prediction_1 == 0: st.write('The sentiment of your review is negative.') st.write('Predicted probability:', (1 - round(proba_1[0], 2))*100, '%') else: st.write('The sentiment of your review is positive.') st.write('Predicted probability:', (round(proba_1[0], 2))*100, '%') st.write('Processing time:', round(end_time - start_time, 4), 'seconds') # Lena if user_input is not None and submit: with col2: model2, tokenizer = load_model_l() start_time = time.time() input_tokens = preprocess_text(user_input, 500, tokenizer) output = predict_sentiment(model2, input_tokens) end_time = time.time() st.write('The sentiment of your review is', output) st.write('Processing time:', round(end_time - start_time, 4), 'seconds') # Gala if user_input is not None and submit: with col3: model3 = load_model_g() start_time = time.time() output = predict_sentence(user_input,model3) end_time = time.time() st.write('The sentiment of your review is', output) st.write('Processing time:', round(end_time - start_time, 4), 'seconds') if __name__ == '__main__': main()