app.py

import streamlit as st
import transformers
import torch
import altair as alt

# Load the model and tokenizer
model = transformers.AutoModelForSequenceClassification.from_pretrained("RICHARDMENSAH/twitter_xlm_roberta_base")
tokenizer = transformers.AutoTokenizer.from_pretrained("RICHARDMENSAH/twitter_xlm_roberta_base")

# Define the function for sentiment analysis
def predict_sentiment(text):
    result = model([text])
    labell = result[0]['label']
    if labell == 'label_0':
        sentiment = 'Negative'
    elif labell == 'label_1':
        sentiment = 'Neutral'
    elif labell == 'label_2':
        sentiment = 'Positive'
    else:
        sentiment = labell
    score = result[0]['score']
    return sentiment, score

# Setting the page configurations
st.set_page_config(
    page_title="Sentiment Analysis App",
    page_icon=":smile:",
    layout="wide",
    initial_sidebar_state="auto",
)

# Add description and title
st.write("""
# How Positive or Negative is your Text?
Enter some text and we'll tell you if it has a positive, negative, or neutral sentiment!
""" )

# Add image
image = st.image("https://i0.wp.com/thedatascientist.com/wp-content/uploads/2018/10/sentiment-analysis.png", width=400)

# Get user input
text = st.text_input("Enter some text here:")

# Define the CSS style for the app
st.markdown(
"""
<style>
body {
    background-color: #f5f5f5;
}

h1 {
    color: #4e79a7;
}
</style>
""",
unsafe_allow_html=True
)

# Show sentiment output
if text:
    sentiment, score = predict_sentiment(text)
    if sentiment == "Positive":
        st.success(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
    elif sentiment == "Negative":
        st.error(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
    else:
        st.warning(f"The sentiment is {sentiment} with a score of {score*100:.