pages/comments.py

# pages/comments.py
"""
Comments view — Live Chat Feed.
Imports shared infrastructure from app.py via sys.path manipulation.
All session state values are set by app.py before this page runs.
"""
import streamlit as st
import pandas as pd
import re
import time

import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
from shared import (
    store_llen, load_stream_data,
    clean_sentiment, clean_topic, csv_download,
    TOPIC_LABELS, TOPIC_COLOR, SENT_COLORS, STREAM_NAMES,
)

# ── Get shared state from session ────────────────────────────
auto_refresh  = st.session_state.get("auto_refresh", True)
refresh_rate  = st.session_state.get("refresh_rate", 10)
msg_limit     = st.session_state.get("msg_limit", 50)
_primary_key  = st.session_state.get("_primary_key", "chat_messages")

# ── Load data ─────────────────────────────────────────────────
all_data = load_stream_data(_primary_key)
data     = all_data[-msg_limit:] if len(all_data) > msg_limit else all_data

if not all_data:
    st.markdown(
        '<div class="empty-state">'
        '<div class="empty-icon">📭</div>'
        '<div class="empty-title">No messages yet</div>'
        '<div class="empty-sub">Set a video ID in the sidebar, then click ▶ Start</div>'
        '</div>', unsafe_allow_html=True
    )
    if auto_refresh:
        time.sleep(refresh_rate)
        st.rerun()
    st.stop()

df = pd.DataFrame(data)
df["sentiment"] = df["sentiment"].apply(clean_sentiment)
df["topic"]     = df["topic"].apply(clean_topic) if "topic" in df.columns else "General"

# ── COMMENTS VIEW ─────────────────────────────────────────────
st.markdown('<div class="sec-hdr"><span class="sec-ttl">Live Chat Feed</span></div>', unsafe_allow_html=True)

# ── PINNED MESSAGES (shown above the feed) ────────────────────
if st.session_state.pinned_messages:
    st.markdown(
        '<div class="sec-hdr"><span class="sec-ttl">📌 Pinned Messages</span>'
        f'<span class="sec-pill">{len(st.session_state.pinned_messages)} pinned</span></div>',
        unsafe_allow_html=True
    )
    for _pidx, _pmsg in enumerate(st.session_state.pinned_messages):
        _ps       = _pmsg.get("sentiment", "Neutral")
        _ps_color = SENT_COLORS.get(_ps, "#6b7280")
        _pt_color = TOPIC_COLOR.get(_pmsg.get("topic", "General"), "#6b7280")
        _pcol1, _pcol2 = st.columns([10, 1])
        with _pcol1:
            st.markdown(
                f'<div class="chat-card chat-pinned">'
                f'<div class="chat-author">📌 {_pmsg.get("author", "Unknown")}</div>'
                f'<div class="chat-text">{_pmsg.get("text", "")}</div>'
                f'<div class="chat-badges">'
                f'<span class="badge pin-badge">Pinned</span>'
                f'<span class="badge" style="color:{_ps_color};">{_ps}</span>'
                f'<span class="badge" style="color:{_pt_color};">{_pmsg.get("topic","General")}</span>'
                f'<span class="badge">{_pmsg.get("time","")[:19]}</span>'
                f'</div></div>',
                unsafe_allow_html=True
            )
        with _pcol2:
            if st.button("\u2715", key=f"unpin_top_{_pidx}"):
                st.session_state.pinned_messages.pop(_pidx)
                st.rerun()
    st.divider()

# Build stream options
_feed_stream_options = {}
for _fs in st.session_state.streams:
    _fkey = _fs.get("redis_key", "")
    _flen = store_llen(_fkey)
    if _flen > 0:
        _fidx  = st.session_state.streams.index(_fs)
        _flabel = f"Stream {STREAM_NAMES[_fidx]} — {_fs.get('video_id', _fkey)[:20]}"
        _feed_stream_options[_flabel] = _fkey

_cf0, _cf1, _cf2, _cf3, _cf4 = st.columns([1, 1, 1, 1, 2])
with _cf0:
    if len(_feed_stream_options) > 1:
        _selected_stream_label = st.selectbox(
            "Stream", list(_feed_stream_options.keys()), key="feed_stream_select"
        )
        _feed_key = _feed_stream_options[_selected_stream_label]
    else:
        _feed_key = _primary_key
        if _feed_stream_options:
            st.markdown(
                f'<div style="font-size:0.75rem;color:var(--text-2);padding-top:28px;">'
                f'{list(_feed_stream_options.keys())[0]}</div>',
                unsafe_allow_html=True
            )

if _feed_key == _primary_key:
    _feed_df = df.copy()
else:
    _feed_raw = load_stream_data(_feed_key, limit=msg_limit)
    _feed_df  = pd.DataFrame(_feed_raw) if _feed_raw else pd.DataFrame()
    if not _feed_df.empty:
        _feed_df["sentiment"] = _feed_df["sentiment"].apply(clean_sentiment)
        _feed_df["topic"]     = _feed_df["topic"].apply(clean_topic) if "topic" in _feed_df.columns else "General"

with _cf1:
    _sentiment_filter = st.selectbox("Sentiment", ["All", "Positive", "Neutral", "Negative"])
with _cf2:
    _topic_filter = st.selectbox("Topic", ["All"] + TOPIC_LABELS)
with _cf3:
    _all_action_types = [
        "General Appreciation", "Testimonials", "Faculty Request", "Faculty Feedback",
        "Content requests", "Content Feedback", "Academic / Lecture / Concept Doubts",
        "Academic requests", "Study Materials, Deliverables & Learning Resources",
        "Access & Support", "Batch details / structure / offerings (incl faculty)",
        "Schedule & logistics (Batch)", "Information- Exam", "Information- Post Exam",
        "Eligibility & audience fit - Can I take this?", "Suitability & Sufficiency (Is this enough?)",
        "Guidance- What should I take/do?", "Language Request", "Language medium",
        "Pricing, discounts, scholarships, offer validity", "Fees + Financial Queries",
        "Product/feature requests (non-content)", "Offline expansion & event-city requests",
        "Offers + Events", "General Feedback", "Others", "N/A",
    ]
    _action_type_filter = st.selectbox("Action Type", ["All"] + _all_action_types)
with _cf4:
    _search_term = st.text_input("Search messages", placeholder="Filter by keyword...")

# ── Smart filtering: when a filter is active, scan full history
# to find the last msg_limit matching messages instead of filtering
# only within the current window.
_any_filter = (
    _sentiment_filter != "All"
    or _topic_filter != "All"
    or _action_type_filter != "All"
    or bool(_search_term)
)

if _any_filter:
    # Load full history for the feed key
    _full_raw = load_stream_data(_feed_key)
    if _full_raw:
        _full_df = pd.DataFrame(_full_raw)
        _full_df["sentiment"] = _full_df["sentiment"].apply(clean_sentiment)
        _full_df["topic"]     = _full_df["topic"].apply(clean_topic) if "topic" in _full_df.columns else "General"
        # Apply filters on full history
        _filtered = _full_df.copy()
        if _sentiment_filter != "All":
            _filtered = _filtered[_filtered["sentiment"] == _sentiment_filter]
        if _topic_filter != "All":
            _filtered = _filtered[_filtered["topic"] == _topic_filter]
        if _action_type_filter != "All":
            if "action_type" in _filtered.columns:
                _filtered = _filtered[_filtered["action_type"] == _action_type_filter]
        if _search_term:
            _filtered = _filtered[_filtered["text"].str.contains(_search_term, case=False, na=False)]
        # Cap to last msg_limit matching results
        if len(_filtered) > msg_limit:
            _filtered = _filtered.iloc[-msg_limit:]
    else:
        _filtered = pd.DataFrame()
    _total_matching = len(_filtered)
    _total_scanned  = len(_full_raw) if _full_raw else 0
else:
    # No filter — just use the window
    _filtered = _feed_df.copy() if not _feed_df.empty else pd.DataFrame()
    _total_matching = len(_filtered)
    _total_scanned  = len(_feed_df)

_feed_hdr, _feed_dl = st.columns([3, 1])
with _feed_hdr:
    if _any_filter:
        st.markdown(
            f'<div style="font-size:0.78rem;color:var(--text-3);margin-bottom:12px;">'
            f'Showing {len(_filtered)} matching messages (scanned all {_total_scanned}, capped at {msg_limit})</div>',
            unsafe_allow_html=True
        )
    else:
        st.markdown(
            f'<div style="font-size:0.78rem;color:var(--text-3);margin-bottom:12px;">'
            f'Showing {len(_filtered)} of {len(_feed_df)} messages</div>',
            unsafe_allow_html=True
        )
with _feed_dl:
    if not _filtered.empty:
        _export_cols = [c for c in ["author", "text", "sentiment", "confidence", "topic", "time"] if c in _filtered.columns]
        csv_download(_filtered[_export_cols], "Download Feed CSV", "chat_feed.csv")

_SENT_ICON = {"Positive": "🟢", "Negative": "🔴", "Neutral": "🟡"}
_pinned_texts = {m.get("text", "") for m in st.session_state.pinned_messages}

for _i, (_, _row) in enumerate(_filtered.iloc[::-1].iterrows()):
    _s         = _row.get("sentiment", "Neutral")
    _conf_pct  = int(_row.get("confidence", 0) * 100)
    _topic     = clean_topic(_row.get("topic", "General"))
    _t_color   = TOPIC_COLOR.get(_topic, "#6b7280")
    _s_color   = SENT_COLORS.get(_s, "#6b7280")
    _s_icon    = _SENT_ICON.get(_s, "⚪")
    _conf_color = "#22c55e" if _conf_pct >= 70 else "#eab308" if _conf_pct >= 40 else "#ef4444"
    _msg_text  = _row.get("text", "")
    _display_text = re.sub(r":[a-zA-Z0-9_\-]+:", "", _msg_text).strip() or _msg_text
    _is_pinned = _msg_text in _pinned_texts
    _action_type = _row.get("action_type", "N/A") or "N/A"
    _card_class = f"chat-card chat-{_s.lower()}" + (" chat-pinned" if _is_pinned else "")

    _msg_col, _pin_col = st.columns([11, 1])
    with _msg_col:
        _ab = (
            f'<span class="badge" style="color:#a78bfa;border-color:#a78bfa33;">🏷 {_action_type}</span>'
            if _action_type not in ("N/A", "", None) else ""
        )
        st.markdown(
            f'<div class="{_card_class}">'
            f'<div class="chat-author">{_s_icon} {_row.get("author", "Unknown")}'
            + (' <span style="font-size:0.7rem;color:#eab308;">📌</span>' if _is_pinned else '') +
            f'</div>'
            f'<div class="chat-text">{_display_text}</div>'
            f'<div class="chat-badges">'
            f'<span class="badge" style="color:{_s_color};border-color:{_s_color}33;">{_s}</span>'
            f'<span class="badge" style="color:{_conf_color};">Confidence: {_conf_pct}%</span>'
            f'<span class="badge" style="color:{_t_color};border-color:{_t_color}33;">{_topic}</span>'
            f'{_ab}'
            f'</div></div>',
            unsafe_allow_html=True
        )
    with _pin_col:
        if _is_pinned:
            if st.button("📌", key=f"unpin_feed_{_i}", help="Unpin this message"):
                st.session_state.pinned_messages = [
                    m for m in st.session_state.pinned_messages if m.get("text") != _msg_text
                ]
                st.rerun()
        else:
            if st.button("📍", key=f"pin_{_i}", help="Pin this message"):
                _msg_dict = _row.to_dict()
                if _msg_dict not in st.session_state.pinned_messages:
                    st.session_state.pinned_messages.append(_msg_dict)
                st.rerun()

# ── AUTO REFRESH ──────────────────────────────────────────────
if auto_refresh:
    time.sleep(refresh_rate)
    st.rerun()