app.py

import streamlit as st
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load the pretrained model and tokenizer
model_name = "distilbert-base-uncased-finetuned-sst-2-english"  # Example model
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Set up the device (GPU or CPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Function to perform sentiment analysis
def perform_sentiment_analysis(text):
    inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
    inputs = inputs.to(device)
    outputs = model(**inputs)
    logits = outputs.logits
    probabilities = torch.softmax(logits, dim=1).detach().cpu().numpy()[0]
    sentiment_label = "Positive" if probabilities[1] > probabilities[0] else "Negative"
    return sentiment_label, probabilities

# Streamlit app
def main():
    st.title("Sentiment Analysis App")
    st.write("Enter a text and select a pretrained model to perform sentiment analysis.")

    text = st.text_area("Enter text", value="")

    model_options = {
        "distilbert-base-uncased-finetuned-sst-2-english": "DistilBERT (SST-2)",
        # Add more models here if desired
    }

    selected_model = st.selectbox("Select a pretrained model", list(model_options.keys()))

    if st.button("Analyze"):
        sentiment_label, probabilities = perform_sentiment_analysis(text)
        st.write(f"Sentiment: {sentiment_label}")
        st.write(f"Positive probability: {probabilities[1]}")
        st.write(f"Negative probability: {probabilities[0]}")

if __name__ == "__main__":
    main()