"""EmoSphere Session Report — visual summary after a live session.

Generates:
  - Emotion timeline chart (Plotly)
  - Dominant emotion distribution (pie chart)
  - Emotional shifts/peaks
  - Trigger words and topics
  - Full transcript with emotional highlights
  - Modality contribution breakdown
  - Session statistics
"""

from __future__ import annotations

from typing import Optional

import streamlit as st

from models import EmotionLabel, EMOTION_LABELS, EMOTION_EMOJI
from live_processor import LiveSessionProcessor, SessionSummary, TimelineEntry, TranscriptSegment


# ── Color Palette (matches app.py) ───────────────────────────────────

EMOTION_COLORS = {
    EmotionLabel.JOY:       "#FFD700",
    EmotionLabel.SADNESS:   "#4A90D9",
    EmotionLabel.SURPRISE:  "#FF8C00",
    EmotionLabel.FEAR:      "#8B5CF6",
    EmotionLabel.DISGUST:   "#10B981",
    EmotionLabel.NEUTRAL:   "#94A3B8",
    EmotionLabel.LOVE:      "#F472B6",
    EmotionLabel.CALM:      "#67E8F9",
}


def render_session_report(
    processor: LiveSessionProcessor,
    summary: Optional[SessionSummary] = None,
):
    """Render the full session report in Streamlit."""

    if summary is None:
        summary = processor.generate_summary()

    if summary is None:
        st.warning("No session data available to generate a report.")
        return

    timeline = processor.get_timeline()
    transcript = processor.get_transcript()

    st.markdown(
        '<div style="text-align: center; margin: 20px 0;">'
        '<h2>Session Report</h2>'
        '<p style="color: #6B7B9D;">Multimodal emotion analysis summary</p>'
        '</div>',
        unsafe_allow_html=True,
    )

    # ── Statistics Row ───────────────────────────────────────────────

    cols = st.columns(4)
    with cols[0]:
        mins = int(summary.duration_seconds) // 60
        secs = int(summary.duration_seconds) % 60
        st.metric("Duration", f"{mins}:{secs:02d}")
    with cols[1]:
        st.metric("Video Frames", str(summary.total_video_frames))
    with cols[2]:
        st.metric("Audio Chunks", str(summary.total_audio_chunks))
    with cols[3]:
        emoji = EMOTION_EMOJI.get(summary.dominant_emotion, "")
        st.metric("Dominant Emotion", f"{emoji} {summary.dominant_emotion.value}")

    st.divider()

    # ── Emotion Timeline Chart ───────────────────────────────────────

    if timeline and len(timeline) > 1:
        st.markdown("### Emotion Timeline")
        _render_timeline_chart(timeline)
        st.divider()

    # ── Emotion Distribution ─────────────────────────────────────────

    col_dist, col_mod = st.columns(2)

    with col_dist:
        st.markdown("### Emotion Distribution")
        _render_distribution(summary.emotion_distribution)

    with col_mod:
        st.markdown("### Modality Contribution")
        _render_modality_contribution(summary.modality_contribution)

    st.divider()

    # ── Emotional Shifts & Peaks ─────────────────────────────────────

    col_shift, col_peak = st.columns(2)

    with col_shift:
        st.markdown("### Emotional Shifts")
        if summary.emotional_shifts:
            for shift in summary.emotional_shifts[:10]:
                t = shift["timestamp"]
                from_emo = shift["from"]
                to_emo = shift["to"]
                from_emoji = EMOTION_EMOJI.get(EmotionLabel(from_emo), "")
                to_emoji = EMOTION_EMOJI.get(EmotionLabel(to_emo), "")
                snippet = shift.get("transcript", "")
                st.markdown(
                    '<div class="glass-card" style="padding: 10px; margin: 4px 0;">'
                    '<span style="color: #6B7B9D; font-size: 12px;">{:.0f}s</span> '
                    '<span style="font-size: 18px;">{}</span> '
                    '<span style="color: #6B7B9D; font-weight: 700;">{}</span>'
                    ' &rarr; '
                    '<span style="font-size: 18px;">{}</span> '
                    '<span style="color: #00D4FF; font-weight: 700;">{}</span>'
                    '{}'
                    '</div>'.format(
                        t, from_emoji, from_emo, to_emoji, to_emo,
                        f'<br/><span style="color: #6B7B9D; font-size: 11px;">"{snippet}"</span>'
                        if snippet else "",
                    ),
                    unsafe_allow_html=True,
                )
        else:
            st.markdown(
                '<p style="color: #6B7B9D;">No significant emotional shifts detected.</p>',
                unsafe_allow_html=True,
            )

    with col_peak:
        st.markdown("### Emotional Peaks")
        if summary.peaks:
            for peak in summary.peaks[:10]:
                t = peak["timestamp"]
                emo = peak["emotion"]
                score = peak["score"]
                emoji = EMOTION_EMOJI.get(EmotionLabel(emo), "")
                color = EMOTION_COLORS.get(EmotionLabel(emo), "#94A3B8")
                snippet = peak.get("transcript", "")
                st.markdown(
                    '<div class="glass-card" style="padding: 10px; margin: 4px 0;">'
                    '<span style="color: #6B7B9D; font-size: 12px;">{:.0f}s</span> '
                    '<span style="font-size: 18px;">{}</span> '
                    '<span style="color: {}; font-weight: 700;">{}</span> '
                    '<span style="color: #B0BCD0;">{:.0f}%</span>'
                    '{}'
                    '</div>'.format(
                        t, emoji, color, emo, score * 100,
                        f'<br/><span style="color: #6B7B9D; font-size: 11px;">"{snippet}"</span>'
                        if snippet else "",
                    ),
                    unsafe_allow_html=True,
                )
        else:
            st.markdown(
                '<p style="color: #6B7B9D;">No significant peaks detected.</p>',
                unsafe_allow_html=True,
            )

    st.divider()

    # ── Topics & Triggers ────────────────────────────────────────────

    if summary.topics_detected:
        st.markdown("### Topics & Triggers Detected")
        topic_html = " ".join(
            '<span style="display: inline-block; background: rgba(0,212,255,0.15); '
            'border: 1px solid rgba(0,212,255,0.3); border-radius: 20px; '
            'padding: 4px 14px; margin: 4px; font-size: 13px; color: #00D4FF; '
            'font-weight: 600;">{}</span>'.format(t.replace("_", " ").title())
            for t in summary.topics_detected
        )
        st.markdown(
            '<div class="glass-card">{}</div>'.format(topic_html),
            unsafe_allow_html=True,
        )
        st.divider()

    # ── Transcript ───────────────────────────────────────────────────

    if transcript:
        st.markdown("### Session Transcript")
        _render_transcript(transcript)


def _render_timeline_chart(timeline: list[TimelineEntry]):
    """Render emotion timeline using Plotly."""
    try:
        import plotly.graph_objects as go

        timestamps = [e.timestamp for e in timeline]

        fig = go.Figure()

        for label in EMOTION_LABELS:
            values = []
            for entry in timeline:
                score_map = {s.label: s.score for s in entry.fused_result.scores}
                values.append(score_map.get(label, 0.0))

            color = EMOTION_COLORS.get(label, "#94A3B8")
            emoji = EMOTION_EMOJI.get(label, "")

            fig.add_trace(go.Scatter(
                x=timestamps,
                y=values,
                mode="lines",
                name=f"{emoji} {label.value}",
                line=dict(color=color, width=2),
                fill="none",
                hovertemplate="%{y:.1%}<extra>%{fullData.name}</extra>",
            ))

        fig.update_layout(
            plot_bgcolor="rgba(10,10,26,0.3)",
            paper_bgcolor="rgba(0,0,0,0)",
            font=dict(color="#B0BCD0"),
            xaxis=dict(
                title="Time (seconds)",
                gridcolor="rgba(255,255,255,0.05)",
                color="#6B7B9D",
            ),
            yaxis=dict(
                title="Score",
                gridcolor="rgba(255,255,255,0.05)",
                range=[0, 1],
                color="#6B7B9D",
            ),
            legend=dict(
                bgcolor="rgba(10,10,26,0.5)",
                bordercolor="rgba(255,255,255,0.1)",
                borderwidth=1,
            ),
            height=350,
            margin=dict(l=40, r=20, t=10, b=40),
        )

        st.plotly_chart(fig, use_container_width=True)

    except ImportError:
        # Fallback: render as HTML bars if plotly not available
        _render_timeline_html(timeline)


def _render_timeline_html(timeline: list[TimelineEntry]):
    """Fallback timeline rendering using pure HTML/CSS."""
    st.markdown(
        '<div class="glass-card" style="overflow-x: auto;">',
        unsafe_allow_html=True,
    )
    for entry in timeline[-20:]:  # Last 20 entries
        dom = entry.fused_result.dominant
        score = entry.fused_result.dominant_score
        color = EMOTION_COLORS.get(dom, "#94A3B8")
        emoji = EMOTION_EMOJI.get(dom, "")
        pct = max(score * 100, 5)
        st.markdown(
            '<div style="display: flex; align-items: center; margin: 2px 0; font-size: 12px;">'
            '<span style="color: #6B7B9D; width: 40px; flex-shrink: 0;">{:.0f}s</span>'
            '<div style="flex: 1; background: rgba(255,255,255,0.05); border-radius: 4px; height: 20px; overflow: hidden;">'
            '<div style="width: {:.0f}%; height: 100%; background: {}; border-radius: 4px; '
            'display: flex; align-items: center; padding-left: 6px; color: white; font-weight: 600;">'
            '{} {}'
            '</div></div></div>'.format(entry.timestamp, pct, color, emoji, dom.value),
            unsafe_allow_html=True,
        )
    st.markdown('</div>', unsafe_allow_html=True)


def _render_distribution(emotion_dist: dict[str, float]):
    """Render emotion distribution as horizontal bars."""
    sorted_items = sorted(emotion_dist.items(), key=lambda x: -x[1])
    for emo_val, score in sorted_items:
        try:
            label = EmotionLabel(emo_val)
        except ValueError:
            continue
        color = EMOTION_COLORS.get(label, "#94A3B8")
        emoji = EMOTION_EMOJI.get(label, "")
        pct = max(score * 100, 2)
        st.markdown(
            '<div class="emotion-bar">'
            '<div class="emotion-fill" style="width: {:.0f}%; background: {};">'
            '{} {} - {:.1f}%'
            '</div></div>'.format(pct, color, emoji, emo_val, score * 100),
            unsafe_allow_html=True,
        )


def _render_modality_contribution(modality_contrib: dict[str, float]):
    """Render modality weight breakdown."""
    mod_icons = {
        "face": "&#128247;",
        "voice": "&#127908;",
        "text": "&#128172;",
        "posture": "&#129497;",
        "posture/gesture": "&#129497;",
    }
    mod_colors = {
        "face": "#E948A0",
        "voice": "#FFD700",
        "text": "#00D4FF",
        "posture": "#10B981",
        "posture/gesture": "#10B981",
    }
    # Display-friendly names
    mod_display = {
        "face": "Face",
        "voice": "Voice",
        "text": "Speech",
        "posture": "Posture/Gesture",
        "posture/gesture": "Posture/Gesture",
    }

    if not modality_contrib:
        st.markdown(
            '<p style="color: #6B7B9D;">No modality data available.</p>',
            unsafe_allow_html=True,
        )
        return

    for mod, weight in sorted(modality_contrib.items(), key=lambda x: -x[1]):
        icon = mod_icons.get(mod, "&#128302;")
        color = mod_colors.get(mod, "#94A3B8")
        pct = weight * 100
        st.markdown(
            '<div style="margin: 6px 0;">'
            '<div style="display: flex; align-items: center; margin-bottom: 2px;">'
            '<span style="font-size: 18px;">{}</span>'
            '<span style="color: #B0BCD0; font-weight: 600; margin-left: 8px; text-transform: capitalize;">{}</span>'
            '<span style="color: {}; margin-left: auto; font-weight: 700;">{:.1f}%</span>'
            '</div>'
            '<div style="background: rgba(255,255,255,0.05); border-radius: 6px; height: 8px; overflow: hidden;">'
            '<div style="width: {:.0f}%; height: 100%; background: {}; border-radius: 6px;"></div>'
            '</div></div>'.format(icon, mod_display.get(mod, mod.title()), color, pct, pct, color),
            unsafe_allow_html=True,
        )


def _render_transcript(transcript: list[TranscriptSegment]):
    """Render transcript with emotional annotations."""
    if not transcript:
        st.markdown(
            '<p style="color: #6B7B9D;">No transcript data available.</p>',
            unsafe_allow_html=True,
        )
        return

    html_parts = ['<div class="glass-card" style="max-height: 400px; overflow-y: auto;">']

    for seg in transcript:
        emoji = ""
        color = "#B0BCD0"
        if seg.emotion:
            emoji = EMOTION_EMOJI.get(seg.emotion, "")
            color = EMOTION_COLORS.get(seg.emotion, "#B0BCD0")

        mins = int(seg.timestamp) // 60
        secs = int(seg.timestamp) % 60

        html_parts.append(
            '<div style="padding: 8px 0; border-bottom: 1px solid rgba(255,255,255,0.05);">'
            '<span style="color: #6B7B9D; font-size: 11px; font-weight: 600;">'
            '{}:{:02d}</span> '
            '<span style="font-size: 16px;">{}</span> '
            '<span style="color: {};">{}</span>'
            '</div>'.format(mins, secs, emoji, color, seg.text)
        )

    html_parts.append('</div>')
    st.markdown("".join(html_parts), unsafe_allow_html=True)