app.py

import os
os.system('pip install transformers')
os.system('pip install torch')

# Import required libraries
import torch
import transformers
import streamlit as st
from transformers import pipeline

# Initialize the Streamlit app
st.set_page_config(layout="wide")
st.header("Sentiment Analysis App")

# Define the function for performing sentiment analysis
def analyze_sentiment(text):
    # Load the pre-trained sentiment analysis model and tokenizer
    model = pipeline('sentiment-analysis')
    
    # Perform sentiment analysis on the input text
    result = model(text)[0]['label']
    
    # Return the predicted sentiment label
    return result

# Create the user input form
col1, col2 = st.beta_columns(2)
with col1:
    text_input = st.text_area("Enter Text:", "", key="my_input")
with col2:
    submitted = st.form_submit_button("Submit")

# Display the sentiment analysis results
if submitted:
    sentiment = analyze_sentiment(text_input)
    st.subheader("Sentiment Analysis Result:")
    st.write(f"**Predicted Sentiment:** {sentiment}")