app.py

import streamlit as st
from transformers import AutoTokenizer, RobertaForSequenceClassification
import numpy as np
import torch

st.title("CS482 Project Sentiment Analysis")

text = st.text_area(label="Text to be analyzed", value="This sentiment analysis app is great!")

selected_model = st.radio(label="Model", options=["Model 1", "Model 2"])

analyze_button = st.button(label="Analyze")

st.markdown("**:red[Sentiment:]**")

if analyze_button:
    if selected_model=="Model 1":
        tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
        model = RobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
    else:
        tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
        model = RobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        logits = model(**inputs).logits
    prediction_id = logits.argmax().item()
    results = model.config.id2label[prediction_id]
    st.write(results)