visualization/components/reply_required.py

"""
Reply Required Page
Displays comments that require replies with filtering and prioritisation.

Data is fetched on-demand: user sets filters then clicks "Fetch Data".
Platform, brand and date are pre-populated from global sidebar filters.
"""
import streamlit as st
import pandas as pd
import sys
from pathlib import Path

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

from utils.metrics import SentimentMetrics
from visualizations.sentiment_charts import SentimentCharts
from visualizations.distribution_charts import DistributionCharts
from visualizations.content_cards import ContentCards


def render_reply_required(data_loader):
    """
    Render the Reply Required page.

    Args:
        data_loader: SentimentDataLoader instance
    """
    st.title("⚠️ Comments Requiring Reply")
    st.markdown("Manage and prioritise comments that need responses")
    st.markdown("---")

    metrics = SentimentMetrics()

    # ── Get filter options from the lightweight dashboard df ──────────────────
    dashboard_df = st.session_state.get('dashboard_df')
    if dashboard_df is None or dashboard_df.empty:
        st.warning("Dashboard data not loaded yet. Please wait for the app to initialise.")
        return

    available_platforms = sorted(dashboard_df['platform'].dropna().unique().tolist())
    available_brands    = sorted(dashboard_df['brand'].dropna().unique().tolist())

    # ── Pre-populate from global sidebar filters ───────────────────────────────
    global_filters   = st.session_state.get('global_filters', {})
    global_platforms = global_filters.get('platforms', [])
    global_brands    = global_filters.get('brands', [])
    global_date      = global_filters.get('date_range')

    st.markdown("### 🔍 Query Filters")
    st.info(
        "⚡ **Performance**: Set your filters then click **Fetch Data** to run a targeted Snowflake query. "
        "Global sidebar filters are pre-populated below."
    )

    filter_col1, filter_col2, filter_col3 = st.columns(3)

    with filter_col1:
        selected_platforms = st.multiselect(
            "Platforms",
            options=available_platforms,
            default=[p for p in global_platforms if p in available_platforms],
            help="Leave empty to include all platforms"
        )

    with filter_col2:
        selected_brands = st.multiselect(
            "Brands",
            options=available_brands,
            default=[b for b in global_brands if b in available_brands],
            help="Leave empty to include all brands"
        )

    with filter_col3:
        # Date range — default from global filter or show no filter
        if global_date and len(global_date) == 2:
            default_date = (global_date[0], global_date[1])
        elif 'comment_timestamp' in dashboard_df.columns and not dashboard_df.empty:
            max_d = dashboard_df['comment_timestamp'].max().date()
            min_d = dashboard_df['comment_timestamp'].min().date()
            default_date = (min_d, max_d)
        else:
            default_date = None

        if default_date:
            date_range = st.date_input(
                "Date Range",
                value=default_date,
                help="Filter by comment timestamp"
            )
        else:
            date_range = None

    st.markdown("---")

    # ── Fetch button ───────────────────────────────────────────────────────────
    fetch_key = (
        tuple(sorted(selected_platforms)),
        tuple(sorted(selected_brands)),
        str(date_range) if date_range and len(date_range) == 2 else ''
    )

    has_data = (
        'rr_df' in st.session_state
        and st.session_state.get('rr_fetch_key') == fetch_key
        and not st.session_state['rr_df'].empty
    )

    fetch_col, status_col = st.columns([1, 3])
    with fetch_col:
        fetch_clicked = st.button("🚀 Fetch Data", use_container_width=True, type="primary")
    with status_col:
        if has_data:
            st.success(f"✅ Showing **{len(st.session_state['rr_df']):,}** comments requiring reply")
        elif not fetch_clicked:
            st.info("👆 Click **Fetch Data** to load reply-required comments from Snowflake.")

    if fetch_clicked:
        with st.spinner("Fetching reply-required comments from Snowflake…"):
            df = data_loader.load_reply_required_data(
                platforms=selected_platforms or None,
                brands=selected_brands or None,
                date_range=date_range if date_range and len(date_range) == 2 else None,
            )
        st.session_state['rr_df'] = df
        st.session_state['rr_fetch_key'] = fetch_key
        st.session_state['reply_page'] = 1
        st.rerun()

    if not has_data and not fetch_clicked:
        return

    # ── Work with fetched data ─────────────────────────────────────────────────
    reply_comments = st.session_state.get('rr_df', pd.DataFrame())

    if reply_comments.empty:
        st.success("🎉 No comments currently require replies with these filters.")
        return

    st.markdown("---")

    # ── Summary stats ──────────────────────────────────────────────────────────
    st.markdown("### 📊 Summary")
    col1, col2, col3, col4 = st.columns(4)

    with col1:
        st.metric("Total Replies Needed", len(reply_comments))
    with col2:
        urgency = metrics.calculate_response_urgency(reply_comments)
        st.metric("🔴 Urgent", urgency['urgent_count'], help="Negative sentiment")
    with col3:
        unique_contents = reply_comments['content_sk'].nunique() if 'content_sk' in reply_comments.columns else 0
        st.metric("Affected Contents", unique_contents)
    with col4:
        neg_cnt = reply_comments['sentiment_polarity'].isin(['negative', 'very_negative']).sum()
        neg_pct = neg_cnt / len(reply_comments) * 100 if len(reply_comments) > 0 else 0
        st.metric("Negative %", f"{neg_pct:.1f}%")

    st.markdown("---")

    # ── Urgency breakdown ──────────────────────────────────────────────────────
    st.markdown("### 🚨 Response Urgency Breakdown")
    urgency_metrics = metrics.calculate_response_urgency(reply_comments)
    uc1, uc2, uc3, uc4 = st.columns(4)
    uc1.metric("🔴 Urgent",       urgency_metrics['urgent_count'],       help="Negative — immediate action")
    uc2.metric("🟠 High Priority", urgency_metrics['high_priority_count'], help="Neutral + feedback/request — 24h")
    uc3.metric("🟡 Medium",        urgency_metrics['medium_priority_count'], help="Positive — 48h")
    uc4.metric("🟢 Low",           urgency_metrics['low_priority_count'],  help="Very positive — when convenient")

    st.markdown("---")

    # ── In-page filters (applied to already-fetched data) ─────────────────────
    st.markdown("### 🔍 Refine View")
    rf1, rf2, rf3, rf4 = st.columns(4)

    with rf1:
        priority_options = ['All', '🔴 Urgent', '🟠 High', '🟡 Medium', '🟢 Low']
        selected_priority = st.selectbox("Priority", priority_options, index=0)

    with rf2:
        platform_options = ['All'] + sorted(reply_comments['platform'].unique().tolist())
        view_platform = st.selectbox("Platform", platform_options, index=0)

    with rf3:
        brand_options = ['All'] + sorted(reply_comments['brand'].unique().tolist())
        view_brand = st.selectbox("Brand", brand_options, index=0)

    with rf4:
        intent_list = (
            reply_comments['intent'].str.split(',').explode().str.strip()
            .dropna().unique().tolist()
        )
        intent_options = ['All'] + sorted(intent_list)
        selected_intent = st.selectbox("Intent", intent_options, index=0)

    filtered_comments = reply_comments

    if selected_priority != 'All':
        if selected_priority == '🔴 Urgent':
            filtered_comments = filtered_comments[
                filtered_comments['sentiment_polarity'].isin(['negative', 'very_negative'])
            ]
        elif selected_priority == '🟠 High':
            filtered_comments = filtered_comments[
                (filtered_comments['sentiment_polarity'] == 'neutral') &
                (filtered_comments['intent'].str.contains('feedback_negative|request', na=False))
            ]
        elif selected_priority == '🟡 Medium':
            filtered_comments = filtered_comments[filtered_comments['sentiment_polarity'] == 'positive']
        elif selected_priority == '🟢 Low':
            filtered_comments = filtered_comments[filtered_comments['sentiment_polarity'] == 'very_positive']

    if view_platform != 'All':
        filtered_comments = filtered_comments[filtered_comments['platform'] == view_platform]

    if view_brand != 'All':
        filtered_comments = filtered_comments[filtered_comments['brand'] == view_brand]

    if selected_intent != 'All':
        filtered_comments = filtered_comments[
            filtered_comments['intent'].str.contains(selected_intent, na=False)
        ]

    st.markdown(f"**Showing {len(filtered_comments):,} comments after filtering**")
    st.markdown("---")

    # ── Charts ─────────────────────────────────────────────────────────────────
    if not filtered_comments.empty:
        st.markdown("### 📈 Analysis")
        viz_col1, viz_col2 = st.columns(2)
        with viz_col1:
            sentiment_charts = SentimentCharts()
            st.plotly_chart(
                sentiment_charts.create_sentiment_pie_chart(filtered_comments, title="Sentiment Distribution"),
                use_container_width=True
            )
        with viz_col2:
            distribution_charts = DistributionCharts()
            st.plotly_chart(
                distribution_charts.create_intent_bar_chart(
                    filtered_comments, title="Intent Distribution", orientation='h'
                ),
                use_container_width=True
            )
        st.markdown("---")

    # ── Paginated comment list ─────────────────────────────────────────────────
    st.markdown("### 💬 Comments Requiring Reply")

    items_per_page = 10
    total_pages = max(1, (len(filtered_comments) - 1) // items_per_page + 1)

    if 'reply_page' not in st.session_state:
        st.session_state.reply_page = 1

    # Clamp page if filters reduced the total
    st.session_state.reply_page = min(st.session_state.reply_page, total_pages)

    if total_pages > 1:
        pc1, pc2, pc3 = st.columns([1, 2, 1])
        with pc1:
            if st.button("⬅️ Previous", key="prev_top",
                         disabled=st.session_state.reply_page <= 1):
                st.session_state.reply_page -= 1
                st.rerun()
        with pc2:
            st.markdown(f"<center>Page {st.session_state.reply_page} of {total_pages}</center>",
                        unsafe_allow_html=True)
        with pc3:
            if st.button("Next ➡️", key="next_top",
                         disabled=st.session_state.reply_page >= total_pages):
                st.session_state.reply_page += 1
                st.rerun()
        st.markdown("---")

    start_idx = (st.session_state.reply_page - 1) * items_per_page
    paginated  = filtered_comments.iloc[start_idx: start_idx + items_per_page]

    if paginated.empty:
        st.info("No comments match the selected filters.")
    else:
        for idx, (_, comment) in enumerate(paginated.iterrows(), start=start_idx + 1):
            sp = comment['sentiment_polarity']
            if sp in ['negative', 'very_negative']:
                priority_emoji = "🔴"
            elif sp == 'neutral' and any(i in comment['intent'] for i in ['feedback_negative', 'request']):
                priority_emoji = "🟠"
            elif sp == 'positive':
                priority_emoji = "🟡"
            else:
                priority_emoji = "🟢"

            st.markdown(f"#### {priority_emoji} Comment #{idx}")
            ContentCards.display_comment_card(comment, show_original=True)

    if total_pages > 1:
        st.markdown("---")
        pb1, pb2, pb3 = st.columns([1, 2, 1])
        with pb1:
            if st.button("⬅️ Previous", key="prev_bottom",
                         disabled=st.session_state.reply_page <= 1):
                st.session_state.reply_page -= 1
                st.rerun()
        with pb2:
            st.markdown(f"<center>Page {st.session_state.reply_page} of {total_pages}</center>",
                        unsafe_allow_html=True)
        with pb3:
            if st.button("Next ➡️", key="next_bottom",
                         disabled=st.session_state.reply_page >= total_pages):
                st.session_state.reply_page += 1
                st.rerun()

    st.markdown("---")

    # ── Export ─────────────────────────────────────────────────────────────────
    st.markdown("### 💾 Export Data")
    col1, col2 = st.columns([1, 3])
    with col1:
        export_columns = [
            'comment_id', 'author_name', 'platform', 'brand', 'comment_timestamp',
            'display_text', 'original_text', 'detected_language', 'sentiment_polarity',
            'intent', 'sentiment_confidence', 'content_description', 'permalink_url'
        ]
        available_cols = [c for c in export_columns if c in filtered_comments.columns]
        csv = filtered_comments[available_cols].to_csv(index=False)
        st.download_button(
            label="📥 Download as CSV",
            data=csv,
            file_name="comments_requiring_reply.csv",
            mime="text/csv"
        )
    with col2:
        st.info("Download the filtered comments for team collaboration or CRM import.")

    st.markdown("---")

    # ── Reply requirements by content (top 10) ─────────────────────────────────
    st.markdown("### 📋 Reply Requirements by Content")

    if 'content_sk' in filtered_comments.columns:
        content_reply_summary = (
            filtered_comments
            .groupby('content_sk', as_index=False)
            .agg(
                replies_needed=('comment_sk', 'count') if 'comment_sk' in filtered_comments.columns
                               else ('sentiment_polarity', 'count'),
                content_description=('content_description', 'first'),
                permalink_url=('permalink_url', 'first')
            )
            .sort_values('replies_needed', ascending=False)
            .head(10)
        )

        for i, (_, content) in enumerate(content_reply_summary.iterrows(), 1):
            with st.expander(f"📝 Content #{i} — {content['replies_needed']} replies needed"):
                st.markdown(f"**Description:** {content['content_description']}")
                if pd.notna(content.get('permalink_url')):
                    st.markdown(f"**Link:** [View Content]({content['permalink_url']})")

                top_comments = filtered_comments[
                    filtered_comments['content_sk'] == content['content_sk']
                ].head(3)
                st.markdown(f"**Top {len(top_comments)} comments:**")
                for _, c in top_comments.iterrows():
                    ContentCards.display_comment_card(c, show_original=True)