app.py

import streamlit as st
import transformers
import torch

st.title("Sentiment Analysis App")

# Set default text
default_text = "Cody Jiang is a happy boy!"

# Select pretrained model
model_names = ['distilbert-base-uncased-finetuned-sst-2-english', 'bert-base-uncased', 'roberta-base']
model_name = st.selectbox("Select a pretrained model", model_names)

# Load selected model
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = transformers.AutoModelForSequenceClassification.from_pretrained(model_name)

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Define sentiment labels
sentiment_labels = ['Negative', 'Positive']

# Analyze sentiment
if st.button("Submit"):
    with st.spinner("Analyzing..."):
        text = st.text_input("Enter text to analyze", default_text)
        inputs = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
        inputs = inputs.to(device)
        outputs = model(**inputs)
        logits = outputs.logits
        predictions = torch.argmax(logits, dim=1).cpu().numpy()
        sentiment = sentiment_labels[predictions[0]]

    st.success(f"Sentiment: {sentiment}")