visualization/app.py

"""
Musora Sentiment Analysis Dashboard
Main Streamlit Application

Run with: streamlit run app.py
"""
import streamlit as st
import sys
from pathlib import Path
import json

# Add parent directory to path
parent_dir = Path(__file__).resolve().parent
sys.path.append(str(parent_dir))

from data.data_loader import SentimentDataLoader
from components.dashboard import render_dashboard
from components.sentiment_analysis import render_sentiment_analysis
from components.reply_required import render_reply_required


# Load configuration
config_path = parent_dir / "config" / "viz_config.json"
with open(config_path, 'r') as f:
    config = json.load(f)

# Page configuration
st.set_page_config(
    page_title=config['page_config']['page_title'],
    page_icon=config['page_config']['page_icon'],
    layout=config['page_config']['layout'],
    initial_sidebar_state=config['page_config']['initial_sidebar_state']
)


def main():
    """
    Main application function
    """
    # Sidebar
    with st.sidebar:
        st.image("visualization/img/musora.png", use_container_width=True)
        st.title("Navigation")

        # Page selection
        page = st.radio(
            "Select Page",
            ["📊 Dashboard", "🔍 Sentiment Analysis", "💬 Reply Required"],
            index=0
        )

        st.markdown("---")

        # Filters section
        st.markdown("### 🔍 Global Filters")

        # Initialize session state for filters
        if 'filters_applied' not in st.session_state:
            st.session_state.filters_applied = False

        # Load data first to get filter options
        with st.spinner("Loading data..."):
            data_loader = SentimentDataLoader()
            df = data_loader.load_data()

        if df.empty:
            st.error("No data available. Please check your Snowflake connection.")
            return

        # Get filter options
        filter_options = data_loader.get_filter_options(df)

        # Platform filter
        selected_platforms = st.multiselect(
            "Platforms",
            options=filter_options['platforms'],
            default=[]
        )

        # Brand filter
        selected_brands = st.multiselect(
            "Brands",
            options=filter_options['brands'],
            default=[]
        )

        # Sentiment filter
        selected_sentiments = st.multiselect(
            "Sentiments",
            options=filter_options['sentiments'],
            default=[]
        )

        # Date range filter (if available)
        if 'comment_timestamp' in df.columns and not df.empty:
            min_date = df['comment_timestamp'].min().date()
            max_date = df['comment_timestamp'].max().date()

            date_range = st.date_input(
                "Date Range",
                value=(min_date, max_date),
                min_value=min_date,
                max_value=max_date
            )
        else:
            date_range = None

        # Apply filters button
        if st.button("🔍 Apply Filters", use_container_width=True):
            st.session_state.filters_applied = True

        # Reset filters button
        if st.button("🔄 Reset Filters", use_container_width=True):
            st.session_state.filters_applied = False
            st.rerun()

        st.markdown("---")

        # Data refresh
        st.markdown("### 🔄 Data Management")

        if st.button("♻️ Reload Data", use_container_width=True):
            st.cache_data.clear()
            st.rerun()

        # Display data info
        st.markdown("---")
        st.markdown("### ℹ️ Data Info")
        st.info(f"**Total Records:** {len(df):,}")

        if 'processed_at' in df.columns and not df.empty:
            last_update = df['processed_at'].max()
            st.info(f"**Last Updated:** {last_update.strftime('%Y-%m-%d %H:%M')}")

    # Apply filters if needed
    if st.session_state.filters_applied:
        df = data_loader.apply_filters(
            df,
            platforms=selected_platforms if selected_platforms else None,
            brands=selected_brands if selected_brands else None,
            sentiments=selected_sentiments if selected_sentiments else None,
            date_range=date_range if date_range and len(date_range) == 2 else None
        )

        # Show filter summary
        if df.empty:
            st.warning("No data matches the selected filters. Please adjust your filters.")
            return
        else:
            st.info(f"Showing {len(df):,} records after applying filters")

    # Main content area - render selected page
    if page == "📊 Dashboard":
        render_dashboard(df)

    elif page == "🔍 Sentiment Analysis":
        render_sentiment_analysis(df)

    elif page == "💬 Reply Required":
        render_reply_required(df)

    # Footer
    st.markdown("---")
    st.markdown(
        """
        <div style='text-align: center; color: gray; padding: 20px;'>
            <p>Musora Sentiment Analysis Dashboard v1.0</p>
            <p>Powered by Streamlit | Data from Snowflake</p>
        </div>
        """,
        unsafe_allow_html=True
    )


if __name__ == "__main__":
    try:
        main()
    except Exception as e:
        st.error(f"An error occurred: {str(e)}")
        st.exception(e)