app.py

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)

class DistilBERTClass(torch.nn.Module):
    def __init__(self):
        super(DistilBERTClass, self).__init__()
        self.l1 = DistilBertModel.from_pretrained("distilbert-base-uncased")
        self.pre_classifier = torch.nn.Linear(768, 768)
        self.dropout = torch.nn.Dropout(0.1)
        self.classifier = torch.nn.Linear(768, 1)

    def forward(self, input_ids, attention_mask, token_type_ids):
        output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask)
        hidden_state = output_1[0]
        pooler = hidden_state[:, 0]
        pooler = self.pre_classifier(pooler)
        pooler = torch.nn.ReLU()(pooler)
        pooler = self.dropout(pooler)
        output = self.classifier(pooler)
        return output


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=100,
        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)
    print('Raw output is ', output)
    
    sigmoid_output = torch.sigmoid(output)
    print('Sigmoid output is ', sigmoid_output)

    # Assuming you want to use a threshold of 0.5
    result = 1 if sigmoid_output.item() > 0.5 else 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.")