import streamlit as st
from transformers import pipeline

# Load sentiment analysis model
@st.cache_resource
def load_model():
    return pipeline(
        "sentiment-analysis",
        model="distilbert-base-uncased-finetuned-sst-2-english"
    )

sentiment_model = load_model()

# Main app
def main():
    # UI setup
    st.title("Sentiment Analyzer")
    st.write("Enter a sentence to analyze its sentiment (Positive/Negative)")

    # Text input
    user_input = st.text_area("Your Text", placeholder="Type your sentence here...", height=100)

    # Analyze button and result display
    if st.button("Analyze"):
        if user_input:
            try:
                result = sentiment_model(user_input)[0]
                sentiment = result['label']
                confidence = result['score']
                
                # Display results
                st.success(f"Sentiment: {sentiment}")
                st.info(f"Confidence: {confidence:.4f}")

                # Footer shown only after analysis
                st.markdown("""
                    <p style="font-size: small; color: grey; text-align: center;">
                        Developed By: Krishna Prakash
                        <a href="https://www.linkedin.com/in/krishnaprakash-profile/" target="_blank">
                            <img src="https://img.icons8.com/ios-filled/30/0077b5/linkedin.png" alt="LinkedIn" style="vertical-align: middle; margin: 0 5px;"/>
                        </a>
                    </p>
                """, unsafe_allow_html=True)

            except Exception as e:
                st.error(f"An error occurred: {str(e)}")
        else:
            st.warning("Please enter some text to analyze!")

if __name__ == "__main__":
    main()