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from DistilBERT import DistilBERTClass
import streamlit as st
from transformers import DistilBertTokenizer, DistilBertModel
import logging
logging.basicConfig(level=logging.ERROR)
import torch
MAX_LEN = 100
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased', truncation=True, do_lower_case=True)
model_DB = DistilBERTClass()
loaded_model_path = './model_DB_1.pt'
model_DB.load_state_dict(torch.load(loaded_model_path, map_location=torch.device('cpu')))
model_DB.to(device)
def sentiment_analysis_DB(input):
inputs = tokenizer.encode_plus(
input,
None,
add_special_tokens=True,
max_length=MAX_LEN,
pad_to_max_length=True,
return_token_type_ids=True
)
ids = torch.tensor([inputs['input_ids']]) # Convert to PyTorch tensor
mask = torch.tensor([inputs['attention_mask']]) # Convert to PyTorch tensor
token_type_ids = torch.tensor([inputs["token_type_ids"]]) # Convert to PyTorch tensor
# Assuming model_DB is a PyTorch model
output = model_DB(ids, mask, token_type_ids)
final_outputs = output[0].item() # Extract the scalar value
if final_outputs == True:
result = 1
else:
result = 0
return result
# Streamlit app
st.title("Sentiment Analysis App")
# User input
user_input = st.text_area("Enter some text:")
# Button to trigger sentiment analysis
if st.button("Analyze Sentiment"):
# Perform sentiment analysis
result = sentiment_analysis_DB(user_input)
# Display result
if result == 1:
st.success("Positive sentiment detected!")
else:
st.error("Negative sentiment detected.") |