import streamlit as st
from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Load the fine-tuned model and tokenizer
model_path = 'path/to/your/fine-tuned/model'
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path)

# Set the model to evaluation mode
model.eval()

# Function to perform sentiment analysis
def analyze_sentiment(text):
    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax().item()
    return predicted_class

# Create a Streamlit app
st.title("Sentiment Analysis App")

# Get the user input
text = st.text_area("Enter text for sentiment analysis")

if st.button("Analyze Sentiment"):
    if text:
        sentiment_class = analyze_sentiment(text)
        sentiment_labels = ['Negative', 'Neutral', 'Positive']  # Adjust as needed
        sentiment = sentiment_labels[sentiment_class]
        st.write(f"Sentiment: {sentiment}")
    else:
        st.warning("Please enter text for analysis.")