"""
HelpScout Dashboard Page
Full dedicated dashboard for HelpScout customer support conversation analysis.
"""
import sys
from pathlib import Path

import pandas as pd
import streamlit as st

parent_dir = Path(__file__).resolve().parent.parent
sys.path.append(str(parent_dir))

from utils.helpscout_utils import boolean_flag_counts, topic_label, load_topic_taxonomy
from visualizations.helpscout_charts import HelpScoutCharts
from visualizations.demographic_charts import DemographicCharts
from utils.data_processor import SentimentDataProcessor


def _sentiment_score(df) -> float:
    """Compute average sentiment score on a -2 to +2 scale."""
    score_map = {"very_positive": 2, "positive": 1, "neutral": 0,
                 "negative": -1, "very_negative": -2}
    if "sentiment_polarity" not in df.columns or df.empty:
        return 0.0
    scores = df["sentiment_polarity"].map(score_map).fillna(0)
    return float(scores.mean())


def render_helpscout_dashboard(data_loader, date_range=None):
    """
    Render the full HelpScout Dashboard page.

    Args:
        data_loader: HelpScoutDataLoader instance
        date_range: optional (start_date, end_date) tuple from global sidebar filters
    """
    st.title("🎧 HelpScout Support Dashboard")
    st.markdown("Customer support conversation analysis from HelpScout.")

    hs_df = st.session_state.get("helpscout_df")
    if hs_df is None or hs_df.empty:
        st.warning("No HelpScout data available. Please check your Snowflake connection.")
        return

    if date_range and len(date_range) == 2 and "first_message_at" in hs_df.columns:
        hs_df = hs_df[
            (hs_df["first_message_at"] >= pd.Timestamp(date_range[0])) &
            (hs_df["first_message_at"] <= pd.Timestamp(date_range[1]))
        ]
        if hs_df.empty:
            st.warning("No HelpScout conversations match the selected date range.")
            return
        st.info(f"Showing **{len(hs_df):,}** conversations filtered by date range "
                f"({date_range[0]} → {date_range[1]})")

    charts = HelpScoutCharts()
    taxonomy = load_topic_taxonomy()

    # ── Member Status Filter ───────────────────────────────────────────────────
    has_member_data = "is_member" in hs_df.columns
    if has_member_data:
        member_filter = st.radio(
            "Show conversations for:",
            options=["All Customers", "Members Only", "Non-Members Only"],
            horizontal=True,
            key="hs_dash_member_filter",
        )
        if member_filter == "Members Only":
            hs_df = hs_df[hs_df["is_member"]]
        elif member_filter == "Non-Members Only":
            hs_df = hs_df[~hs_df["is_member"]]
        if member_filter != "All Customers" and hs_df.empty:
            st.warning(f"No conversations found for {member_filter.lower().replace(' only', '')}.")
            return
        if member_filter != "All Customers":
            st.info(f"Filtered to **{len(hs_df):,}** {member_filter.lower().replace(' only', '')} conversations.")
    else:
        st.info("ℹ️ Member data not available — customer emails could not be matched to Musora user records.")

    # ── PDF Export ────────────────────────────────────────────────────────────
    with st.expander("📄 Export PDF Report", expanded=False):
        st.markdown(
            "Generate a comprehensive HelpScout support report. "
            "Covers sentiment, topics, billing flags, timelines, and demographics."
        )
        if st.button("Generate HelpScout PDF Report", type="primary",
                     use_container_width=True, key="hs_dash_pdf_btn"):
            with st.spinner("Generating HelpScout PDF report…"):
                try:
                    from utils.helpscout_pdf import HelpScoutDashboardPDF
                    exporter = HelpScoutDashboardPDF()
                    pdf_bytes = exporter.generate_report(hs_df)
                    import datetime
                    filename = f"helpscout_dashboard_{datetime.datetime.now().strftime('%Y%m%d_%H%M')}.pdf"
                    st.success("Report generated successfully!")
                    st.download_button(
                        label="Download HelpScout Dashboard PDF",
                        data=pdf_bytes,
                        file_name=filename,
                        mime="application/pdf",
                        use_container_width=True,
                    )
                except Exception as e:
                    st.error(f"Failed to generate report: {e}")
                    st.exception(e)

    st.markdown("---")

    # ── KPI Row ───────────────────────────────────────────────────────────────
    total = len(hs_df)
    escalation_count = int(hs_df["is_escalation"].sum()) if "is_escalation" in hs_df.columns else 0
    flags = boolean_flag_counts(hs_df)
    neg_pct = (hs_df["sentiment_polarity"].isin(["negative", "very_negative"]).sum() / total * 100) if total else 0
    avg_duration = float(hs_df["duration_hours"].mean()) if "duration_hours" in hs_df.columns else 0.0

    k1, k2, k3, k4, k5, k6 = st.columns(6)
    k1.metric("Total Conversations", f"{total:,}")
    k2.metric("Avg Duration (h)", f"{avg_duration:.1f}")
    k3.metric("Escalations", f"{escalation_count:,}", delta=f"{escalation_count/total*100:.1f}% of total" if total else None, delta_color="inverse")
    k4.metric("Refund Requests", f"{flags['is_refund_request']:,}")
    k5.metric("Cancellations",   f"{flags['is_cancellation']:,}")
    k6.metric("Membership Joins",f"{flags['is_membership']:,}")

    st.markdown("---")

    # ── Sentiment ─────────────────────────────────────────────────────────────
    st.markdown("## 🎯 Sentiment Distribution")
    col1, col2 = st.columns(2)
    with col1:
        st.plotly_chart(charts.create_sentiment_pie_chart(hs_df), use_container_width=True)
    with col2:
        avg_score = _sentiment_score(hs_df)
        st.plotly_chart(charts.create_sentiment_score_gauge(avg_score), use_container_width=True)
        m1, m2 = st.columns(2)
        pos_pct = hs_df["sentiment_polarity"].isin(["positive", "very_positive"]).sum() / total * 100 if total else 0
        m1.metric("Positive %", f"{pos_pct:.1f}%")
        m2.metric("Negative %", f"{neg_pct:.1f}%")

    st.markdown("---")

    # ── Topics ────────────────────────────────────────────────────────────────
    st.markdown("## 🏷️ Topic Analysis")
    col1, col2 = st.columns(2)
    with col1:
        st.plotly_chart(charts.create_topic_bar_chart(hs_df, title="Conversations by Topic"),
                        use_container_width=True)
    with col2:
        st.plotly_chart(charts.create_topic_pie_chart(hs_df, title="Topic Share"),
                        use_container_width=True)

    st.plotly_chart(charts.create_topic_sentiment_heatmap(hs_df), use_container_width=True)

    st.markdown("---")

    # ── Emotions ─────────────────────────────────────────────────────────────
    if "emotions" in hs_df.columns and hs_df["emotions"].notna().any():
        st.markdown("## 💭 Emotion Analysis")
        col1, col2 = st.columns(2)
        with col1:
            st.plotly_chart(charts.create_emotion_bar_chart(hs_df, title="Emotion Distribution"),
                            use_container_width=True)
        with col2:
            # Reuse the existing DistributionCharts emotion pie (same df structure with emotions col)
            from visualizations.distribution_charts import DistributionCharts
            dist_charts = DistributionCharts()
            st.plotly_chart(dist_charts.create_emotion_pie_chart(hs_df, title="Emotion Share"),
                            use_container_width=True)
        st.markdown("---")

    # ── Billing Flags ─────────────────────────────────────────────────────────
    st.markdown("## 💳 Billing & Membership Flags")
    col1, col2 = st.columns(2)
    with col1:
        st.plotly_chart(charts.create_boolean_flags_chart(hs_df), use_container_width=True)
    with col2:
        st.plotly_chart(charts.create_escalation_breakdown(hs_df), use_container_width=True)

    st.markdown("---")

    # ── Status / Source ───────────────────────────────────────────────────────
    st.markdown("## 📬 Status & Source Distribution")
    col1, col2 = st.columns(2)
    with col1:
        st.plotly_chart(charts.create_status_distribution(hs_df), use_container_width=True)
    with col2:
        st.plotly_chart(charts.create_source_distribution(hs_df), use_container_width=True)

    st.markdown("---")

    # ── Volume & Timelines ────────────────────────────────────────────────────
    with st.expander("📈 Volume & Trends", expanded=False):
        freq_col, _ = st.columns([1, 3])
        with freq_col:
            freq = st.selectbox("Time Granularity", ["D", "W", "M"],
                                format_func=lambda x: {"D": "Daily", "W": "Weekly", "M": "Monthly"}[x],
                                index=1, key="hs_dash_freq")
        st.plotly_chart(charts.create_volume_timeline(hs_df, freq=freq), use_container_width=True)
        st.plotly_chart(charts.create_sentiment_timeline(hs_df, freq=freq), use_container_width=True)

        all_topics_ranked = charts.get_all_topics_ranked(hs_df)
        topic_options = {t: topic_label(t, charts.taxonomy) for t in all_topics_ranked}
        default_topics = all_topics_ranked[:5]
        selected_topics = st.multiselect(
            "Topics to display",
            options=list(topic_options.keys()),
            default=default_topics,
            format_func=lambda t: topic_options[t],
            key="hs_dash_topic_select",
        )
        st.plotly_chart(
            charts.create_topic_timeline(hs_df, freq=freq, selected_topics=selected_topics or default_topics),
            use_container_width=True,
        )
        st.plotly_chart(charts.create_refund_cancel_timeline(hs_df, freq=freq), use_container_width=True)

    # ── Duration & Thread Count ───────────────────────────────────────────────
    with st.expander("📊 Conversation Depth", expanded=False):
        col1, col2 = st.columns(2)
        with col1:
            st.plotly_chart(charts.create_duration_histogram(hs_df), use_container_width=True)
        with col2:
            st.plotly_chart(charts.create_thread_count_histogram(hs_df), use_container_width=True)

    # ── Member vs Non-Member ─────────────────────────────────────────────────
    if "is_member" in hs_df.columns:
        st.markdown("---")
        st.markdown("## 👤 Member vs Non-Member")
        st.caption(
            "Conversations are classified as **Member** when the customer email matches "
            "a Musora user account, and **Non-Member** otherwise."
        )

        member_count     = int(hs_df["is_member"].sum())
        non_member_count = total - member_count
        match_pct        = member_count / total * 100 if total else 0

        mv1, mv2, mv3 = st.columns(3)
        mv1.metric("Members",      f"{member_count:,}",
                   f"{match_pct:.1f}% of conversations" if total else None)
        mv2.metric("Non-Members",  f"{non_member_count:,}",
                   f"{100 - match_pct:.1f}% of conversations" if total else None)
        mv3.metric("Email Match Rate", f"{match_pct:.1f}%")

        mem_col1, mem_col2 = st.columns(2)
        with mem_col1:
            st.plotly_chart(charts.create_member_status_chart(hs_df),
                            use_container_width=True, key="hs_dash_member_pie")
        with mem_col2:
            st.plotly_chart(charts.create_member_sentiment_chart(hs_df),
                            use_container_width=True, key="hs_dash_member_sentiment")

        st.plotly_chart(charts.create_member_topic_chart(hs_df),
                        use_container_width=True, key="hs_dash_member_topics")

    # ── Demographics ─────────────────────────────────────────────────────────
    has_demographics = (
        "age_group" in hs_df.columns
        and "timezone_region" in hs_df.columns
        and (hs_df["age_group"] != "Unknown").any()
    )
    if has_demographics:
        st.markdown("---")
        st.markdown("## 👥 Customer Demographics")
        st.info(f"Demographics available for customers whose email matched Musora user records.")

        processor = SentimentDataProcessor()
        demo_charts = DemographicCharts()

        demo_col1, demo_col2, demo_col3, demo_col4 = st.columns(4)
        known_demo = int((hs_df["age_group"] != "Unknown").sum())
        demo_col1.metric("With Demographics", f"{known_demo:,}", f"{known_demo/total*100:.1f}% matched")

        avg_age = hs_df["age"].mean() if "age" in hs_df.columns else None
        demo_col2.metric("Average Age", f"{avg_age:.1f}" if avg_age else "N/A")

        top_region = hs_df["timezone_region"].value_counts().index[0] if "timezone_region" in hs_df.columns and not hs_df.empty else "N/A"
        demo_col3.metric("Top Region", str(top_region))

        avg_exp = hs_df["experience_level"].mean() if "experience_level" in hs_df.columns else None
        demo_col4.metric("Avg Experience", f"{avg_exp:.1f}/10" if avg_exp else "N/A")

        st.markdown("---")
        age_dist = processor.get_demographics_distribution(hs_df, "age_group")
        if not age_dist.empty:
            st.markdown("### Age Distribution")
            col1, col2 = st.columns(2)
            with col1:
                st.plotly_chart(demo_charts.create_age_distribution_chart(age_dist), use_container_width=True)
            with col2:
                age_sent = processor.get_demographics_by_sentiment(hs_df, "age_group")
                if not age_sent.empty:
                    st.plotly_chart(demo_charts.create_age_sentiment_chart(age_sent), use_container_width=True)

        region_dist = processor.get_timezone_regions_distribution(hs_df)
        if not region_dist.empty:
            st.markdown("### Geographic Distribution")
            col1, col2 = st.columns(2)
            with col1:
                st.plotly_chart(demo_charts.create_region_distribution_chart(region_dist), use_container_width=True)
            with col2:
                region_sent = processor.get_demographics_by_sentiment(hs_df, "timezone_region")
                if not region_sent.empty:
                    st.plotly_chart(demo_charts.create_region_sentiment_chart(region_sent), use_container_width=True)

    st.markdown("---")
    st.caption(
        "Data source: SOCIAL_MEDIA_DB.ML_FEATURES.HELPSCOUT_CONVERSATION_FEATURES | "
        f"Last processed: {hs_df['processed_at'].max().strftime('%Y-%m-%d %H:%M') if 'processed_at' in hs_df.columns and not hs_df.empty else 'Unknown'}"
    )


# ─────────────────────────────────────────────────────────────────────────────
# Compact summary for embedding in the main Sentiment Dashboard
# ─────────────────────────────────────────────────────────────────────────────

def render_helpscout_compact_summary(hs_df):
    """
    A one-screen HelpScout summary section embedded at the bottom of the
    main Sentiment Dashboard. Kept purposely brief.
    """
    st.markdown("---")
    st.markdown("## 🎧 HelpScout Support — Quick View")
    st.caption(f"{len(hs_df):,} processed customer conversations")

    total = len(hs_df)
    if total == 0:
        st.info("No HelpScout conversations available.")
        return

    charts = HelpScoutCharts()
    flags  = boolean_flag_counts(hs_df)
    escalation_count = int(hs_df["is_escalation"].sum()) if "is_escalation" in hs_df.columns else 0
    avg_dur = float(hs_df["duration_hours"].mean()) if "duration_hours" in hs_df.columns else 0.0

    k1, k2, k3, k4 = st.columns(4)
    k1.metric("Conversations", f"{total:,}")
    k2.metric("Escalations",   f"{escalation_count:,}", delta=f"{escalation_count/total*100:.1f}%", delta_color="inverse")
    k3.metric("Refund Requests", f"{flags['is_refund_request']:,}")
    k4.metric("Avg Duration (h)", f"{avg_dur:.1f}")

    col1, col2 = st.columns(2)
    with col1:
        st.plotly_chart(
            charts.create_sentiment_pie_chart(hs_df, title="HelpScout Sentiment"),
            use_container_width=True,
            key="hs_compact_sentiment_pie",
        )
    with col2:
        st.plotly_chart(
            charts.create_topic_bar_chart(hs_df, title="Top Topics", top_n=5),
            use_container_width=True,
            key="hs_compact_topic_bar",
        )

    st.info("👉 Navigate to **🎧 HelpScout Dashboard** for the full analysis.")