import streamlit as st
from textblob import TextBlob

def main():
    # Page configuration
    st.set_page_config(
        page_title="Basic Sentiment Analysis",
        page_icon="🔄",
        layout="centered"
    )

    # Custom CSS for styling
    st.markdown("""
    <style>
        .main {
            background-color: #f8f9fa;
        }
        .stTextInput > div > div > input {
            border-radius: 15px;
            padding: 10px;
        }
        .reportview-container .main .block-container {
            padding: 2rem;
        }
        .result-box {
            padding: 20px;
            border-radius: 10px;
            margin-top: 20px;
            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
        }
        .positive {
            background-color: #d4edda;
            color: #155724;
        }
        .neutral {
            background-color: #e2e3e5;
            color: #383d41;
        }
        .negative {
            background-color: #f8d7da;
            color: #721c24;
        }
    </style>
    """, unsafe_allow_html=True)

    # Header
    st.title("Basic Sentiment Analysis")
    st.markdown("Enter text below to analyze its sentiment (without using pre-trained models).")

    # Text input
    user_input = st.text_area("Enter your text:", height=100, 
                             placeholder="Type something like 'I love this!' or 'This is terrible.'")
    
    if st.button("Analyze Sentiment"):
        if user_input:
            # Analyze sentiment using TextBlob
            analysis = TextBlob(user_input)
            polarity = analysis.sentiment.polarity
            
            # Determine sentiment category
            if polarity > 0.2:
                sentiment = "Positive 😊"
                emotion_class = "positive"
            elif polarity < -0.2:
                sentiment = "Negative 😞"
                emotion_class = "negative"
            else:
                sentiment = "Neutral 😐"
                emotion_class = "neutral"
            
            # Display results
            st.markdown(f"<div class='result-box {emotion_class}'>", unsafe_allow_html=True)
            
            st.subheader("Sentiment Analysis Results:")
            col1, col2 = st.columns(2)
            
            with col1:
                st.metric("Sentiment", sentiment)
                
            with col2:
                st.metric("Polarity Score", round(polarity, 3))
            
            st.progress((polarity + 1) / 2)
            
            st.markdown("""
                **Polarity Scale:**  
                -1.0 (Very Negative) —— 0.0 (Neutral) —— +1.0 (Very Positive)
            """)
            
            st.markdown("</div>", unsafe_allow_html=True)
            
            # Additional analysis
            with st.expander("Detailed Analysis:"):
                st.write(f"- **Subjectivity:** {'Subjective' if analysis.sentiment.subjectivity > 0.5 else 'Objective'} "
                        f"(Score: {round(analysis.sentiment.subjectivity, 3)})")
                st.write("- **Word Count:**", len(analysis.words))
                
        else:
            st.warning("Please enter some text to analyze.")

if __name__ == "__main__":
    main()