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 data.helpscout_data_loader import HelpScoutDataLoader
from data.learning_paths_data_loader import LearningPathsDataLoader
from components.dashboard import render_dashboard
from components.sentiment_analysis import render_sentiment_analysis
from components.reply_required import render_reply_required
from components.helpscout_dashboard import render_helpscout_dashboard
from components.helpscout_analysis import render_helpscout_analysis
from components.learning_paths import render_learning_paths
from utils.auth import check_authentication, render_login_page, logout, get_current_user

# ── 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']
)

st.markdown("""
<style>
    .block-container {
        max-width: 100% !important;
        padding-left: 2rem !important;
        padding-right: 2rem !important;
    }
</style>
""", unsafe_allow_html=True)

# ── Authentication gate ───────────────────────────────────────────────────────
# render_login_page() calls st.stop() when the user is not authenticated,
# so nothing below this point executes until login succeeds.
if not check_authentication():
    render_login_page()

# ── Data loader instances (cheap: just read config) ───────────────────────────
data_loader = SentimentDataLoader()
helpscout_loader = HelpScoutDataLoader()
lp_loader = LearningPathsDataLoader()


def _ensure_dashboard_data():
    """Load comment dashboard data once and store in session_state."""
    if 'dashboard_df' not in st.session_state or st.session_state['dashboard_df'] is None:
        with st.spinner("Loading dashboard data…"):
            df = data_loader.load_dashboard_data()
        st.session_state['dashboard_df'] = df
    return st.session_state['dashboard_df']


def _ensure_helpscout_data():
    """Load HelpScout dashboard data once and store in session_state."""
    if 'helpscout_df' not in st.session_state or st.session_state['helpscout_df'] is None:
        with st.spinner("Loading HelpScout data…"):
            hs_df = helpscout_loader.load_dashboard_data()
        st.session_state['helpscout_df'] = hs_df
    return st.session_state['helpscout_df']


def main():
    # ── Sidebar ───────────────────────────────────────────────────────────────
    with st.sidebar:
        st.image("visualization/img/musora.png", use_container_width=True)

        # User info + logout
        current_user = get_current_user()
        if current_user:
            st.caption(f"Logged in as **{current_user}**")
        if st.button("🔓 Logout", use_container_width=True):
            logout()
            st.rerun()

        st.markdown("---")
        st.title("Navigation")

        page = st.radio(
            "Select Page",
            [
                "📊 Sentiment Dashboard",
                "🔍 Custom Sentiment Queries",
                "💬 Reply Required",
                "🎧 HelpScout Dashboard",
                "🔬 HelpScout Analysis",
                "📚 Learning Paths",
            ],
            index=0
        )

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

        # Load both data sources at startup
        dashboard_df = _ensure_dashboard_data()
        _ensure_helpscout_data()

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

        filter_options = data_loader.get_filter_options(dashboard_df)

        # Restore previous filter values from session_state so widgets keep state
        prev = st.session_state.get('global_filters', {})

        selected_platforms = st.multiselect(
            "Platforms",
            options=filter_options['platforms'],
            default=prev.get('platforms', [])
        )
        selected_brands = st.multiselect(
            "Brands",
            options=filter_options['brands'],
            default=prev.get('brands', [])
        )
        selected_sentiments = st.multiselect(
            "Sentiments",
            options=filter_options['sentiments'],
            default=prev.get('sentiments', [])
        )

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

            prev_range = prev.get('date_range')
            default_range = (
                (prev_range[0], prev_range[1]) if prev_range and len(prev_range) == 2
                else (min_date, max_date)
            )
            date_range = st.date_input(
                "Date Range",
                value=default_range,
                min_value=min_date,
                max_value=max_date
            )
        else:
            date_range = None

        # Apply / Reset
        if st.button("🔍 Apply Filters", use_container_width=True):
            st.session_state['global_filters'] = {
                'platforms':  selected_platforms,
                'brands':     selected_brands,
                'sentiments': selected_sentiments,
                'date_range': date_range if date_range and len(date_range) == 2 else None,
            }
            st.session_state['filters_applied'] = True

        if st.button("🔄 Reset Filters", use_container_width=True):
            st.session_state['global_filters'] = {}
            st.session_state['filters_applied'] = False
            st.rerun()

        st.markdown("---")

        # Data management
        st.markdown("### 🔄 Data Management")
        if st.button("♻️ Reload Data", use_container_width=True):
            st.cache_data.clear()
            st.session_state.pop('dashboard_df', None)
            st.session_state.pop('helpscout_df', None)
            st.rerun()

        # Data info
        st.markdown("---")
        st.markdown("### ℹ️ Data Info")
        st.info(f"**Comments:** {len(dashboard_df):,}")
        hs_df_info = st.session_state.get('helpscout_df')
        if hs_df_info is not None and not hs_df_info.empty:
            st.info(f"**HelpScout:** {len(hs_df_info):,} conversations")
        if 'processed_at' in dashboard_df.columns and not dashboard_df.empty:
            last_update = dashboard_df['processed_at'].max()
            if hasattr(last_update, 'strftime'):
                st.info(f"**Last Updated:** {last_update.strftime('%Y-%m-%d %H:%M')}")

    # ── Build filtered dashboard_df (only applies to comment pages) ─────────
    _hs_page = page in ("🎧 HelpScout Dashboard", "🔬 HelpScout Analysis", "📚 Learning Paths")
    filters_applied = st.session_state.get('filters_applied', False)
    global_filters = st.session_state.get('global_filters', {})

    if not _hs_page and filters_applied and global_filters:
        filtered_df = data_loader.apply_filters(
            dashboard_df,
            platforms=global_filters.get('platforms') or None,
            brands=global_filters.get('brands') or None,
            sentiments=global_filters.get('sentiments') or None,
            date_range=global_filters.get('date_range') or None,
        )
        if filtered_df.empty:
            st.warning("No data matches the selected filters. Please adjust your filters.")
            return
        st.info(f"Showing **{len(filtered_df):,}** records after applying filters")
    else:
        filtered_df = dashboard_df

    # ── Render selected page ──────────────────────────────────────────────────
    if page == "📊 Sentiment Dashboard":
        render_dashboard(filtered_df)

    elif page == "🔍 Custom Sentiment Queries":
        # SA page fetches its own data on demand; receives only data_loader
        render_sentiment_analysis(data_loader)

    elif page == "💬 Reply Required":
        # RR page fetches its own data on demand; receives only data_loader
        render_reply_required(data_loader)

    elif page == "🎧 HelpScout Dashboard":
        hs_date_range = global_filters.get('date_range') if filters_applied else None
        render_helpscout_dashboard(helpscout_loader, date_range=hs_date_range)

    elif page == "🔬 HelpScout Analysis":
        render_helpscout_analysis(helpscout_loader)

    elif page == "📚 Learning Paths":
        render_learning_paths(lp_loader)

    # ── 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)