app.py

import streamlit as st
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
import subprocess

# Install TensorFlow
subprocess.run(["pip", "install", "tensorflow==2.0"])

# Install PyTorch
subprocess.run(["pip", "install", "torch"])



# Explicitly specify the sentiment analysis model
model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
classifier = pipeline("sentiment-analysis", model=model_name)

# Streamlit app
def main():
    st.title("Sentiment Analysis App")
    user_input = st.text_area("Enter your text:")
    
    if st.button("Analyze"):
        if user_input:
            # Make prediction
            prediction = classifier(user_input)
            st.write("Prediction:", prediction[0]["label"], "with confidence:", prediction[0]["score"])
        else:
            st.warning("Please enter some text.")

if __name__ == "__main__":
    main()