app.py

import streamlit as st
from transformers import pipeline

model_path = "citizenlab/twitter-xlm-roberta-base-sentiment-finetunned"

st.set_page_config(page_title="Sentiment Analysis App")

sentiment_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path)

st.title("Sentiment Analysis App")

user_input = st.text_area("Enter a message:", height=150)

if st.button("Analyze Sentiment 🚀", key="analyze_button", help="Click to analyze sentiment"):
    if user_input:
        # Perform sentiment analysis
        st.markdown("---")
        with st.spinner('Analyzing...'):
            results = sentiment_classifier(user_input)
            sentiment_label = results[0]["label"]
            sentiment_score = results[0]["score"]

        st.success("Analysis Complete! 🎉")
        st.write("")

        st.subheader("Sentiment Analysis Result")
        st.write(f"**Sentiment:** {sentiment_label}")
        st.write(f"**Confidence Score:** {sentiment_score:.2f}")

        # Colorful styled button
        if sentiment_label == "positive":
            button_color = "#33cc33"  # green color for positive sentiment
        elif sentiment_label == "negative":
            button_color = "#ff3333"  # red color for negative sentiment
        else:
            button_color = "#3399ff"  # blue color for neutral sentiment

        st.markdown(
            f'<a style="background-color:{button_color};color:white;text-decoration:none;padding:8px 12px;'
            f"border-radius:5px;display:inline-block;margin-top:15px;"
            f'font-weight:bold;" href="https://www.streamlit.io/">'
            f'Share Analysis on Streamlit <span style="font-size:20px;">🔗</span></a>',
            unsafe_allow_html=True,
        )