app.py

import streamlit as st
from transformers import pipeline

# Load the models
pipe_5_star = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")
pipe_pos_neg = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")

# Streamlit app
st.title("Sentiment Analysis")

# Dropdown menu for model selection
model_choice = st.selectbox("Choose a model:", ["5-Star Sentiment", "Positive/Negative Sentiment"])

# Input text
user_message = st.text_area("Enter your text:", "")

if st.button("Analyze"):
    if user_message:
        if model_choice == "5-Star Sentiment":
            result = pipe_5_star(user_message)
            st.write("Sentiment Analysis (5-Star):")
            st.write(result[0]['label'])
        elif model_choice == "Positive/Negative Sentiment":
            result = pipe_pos_neg(user_message)
            st.write("Sentiment Analysis (Positive/Negative):")
            st.write(result[0]['label'])
    else:
        st.error("Please enter a message before analyzing.")