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 = inputs['input_ids']
    mask = inputs['attention_mask']
    token_type_ids = inputs["token_type_ids"]
    output = model_DB(ids, mask, token_type_ids)
    final_outputs = np.array(output)
    final_outputs = final_outputs[0]
    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.")