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import streamlit as st
from transformers import AutoTokenizer, AutoModel
import torch
from bert_file import BERTClassifier
import numpy as np
tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2")
model = BERTClassifier()
device = 'cpu'
model.load_state_dict(torch.load('BERTmodel_weights2.pth',map_location=torch.device('cpu')))
model.eval()
@st.cache_data
def predict_sentiment(text):
MAX_LEN = 100
encoded_review = tokenizer.encode_plus(
text,
max_length=MAX_LEN,
add_special_tokens=True,
return_token_type_ids=False,
pad_to_max_length=True,
return_attention_mask=True,
return_tensors='pt',
)
input_ids = encoded_review['input_ids'].to(device)
attention_mask = encoded_review['attention_mask'].to(device)
with torch.no_grad():
output = model(input_ids, attention_mask)
prediction = torch.round(output).cpu().numpy()[0][0]
if prediction == 1:
return "Позитивный отзыв 😀"
else:
return "Негативный отзыв 😟"
def bert_model_page():
st.title("Классификатор отзывов")
user_input = st.text_area("Введите отзыв:")
if st.button("Классифицировать"):
if user_input:
prediction = predict_sentiment(user_input)
st.write(prediction)
else:
st.write("Пожалуйста, введите отзыв для классификации.")
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