import streamlit as st
from transformers import pipeline

# Model path
model_path = "citizenlab/twitter-xlm-roberta-base-sentiment-finetuned"

# Set Streamlit page config
st.set_page_config(page_title="Sentiment Analysis App")

# Load sentiment analysis model
sentiment_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path)

# Title and user input
st.title("Sentiment Analysis App")
user_input = st.text_area("Enter a message:")

# Function to add CSS style and icons
def custom_css():
    st.markdown(
        """
        <style>
        /* Add some custom CSS */
        .btn {
            background-color: #008CBA;
            color: white;
            padding: 8px 20px;
            text-align: center;
            text-decoration: none;
            display: inline-block;
            font-size: 16px;
            margin: 4px 2px;
            transition-duration: 0.4s;
            cursor: pointer;
            border-radius: 8px;
        }
        /* Add an icon to the button */
        .icon {
            display: inline-block;
            vertical-align: middle;
            width: 20px;
            height: 20px;
            margin-right: 5px;
        }
        </style>
        """,
        unsafe_allow_html=True,
    )

# Render the custom CSS
custom_css()

# Analyze sentiment button
if st.button("Analyze Sentiment"):
    if user_input:
        # Perform sentiment analysis
        results = sentiment_classifier(user_input)
        sentiment_label = results[0]["label"]
        sentiment_score = results[0]["score"]

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