from typing import Dict, List, Tuple, Optional
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import numpy as np

CREAM        = "#FFFFE3"
CARD_BG      = "#FFFFFF"
BORDER       = "#BDDDFC"
TEXT_MAIN    = "#4A4A4A"
TEXT_DIM     = "#7b7b7b"
INK_DARK     = "#384959"
PRIMARY      = "#269ccc"
STORMY_SKY   = "#88BDF2"
STORMY_SLATE = "#6A89A7"
INK_GREY     = "#CBCBCB"
GREEN        = "#2e9e6b"
RED          = "#c0392b"
AMBER        = "#d4841a"
PURPLE       = "#7C6DB5"
BLUE         = "#269ccc"

PLOTLY_LAYOUT = dict(
    paper_bgcolor="rgba(0,0,0,0)",
    plot_bgcolor="rgba(189,221,252,0.13)",
    font=dict(family="'DM Mono', monospace", color=TEXT_MAIN, size=12),
    margin=dict(l=20, r=20, t=40, b=20),
)


def misinfo_gauge(score: float, label: str) -> go.Figure:
    pct = score * 100
    if score < 0.35:
        bar_color = GREEN
    elif score < 0.65:
        bar_color = AMBER
    else:
        bar_color = RED

    fig = go.Figure(go.Indicator(
        mode="gauge+number+delta",
        value=pct,
        number={"suffix": "%", "font": {"size": 32, "color": bar_color, "family": "'DM Mono', monospace"}},
        delta={"reference": 50, "increasing": {"color": RED}, "decreasing": {"color": GREEN}},
        title={"text": label, "font": {"size": 13, "color": TEXT_DIM}},
        gauge={
            "axis": {
                "range": [0, 100],
                "tickwidth": 1,
                "tickcolor": BORDER,
                "tickfont": {"color": TEXT_DIM, "size": 10},
            },
            "bar": {"color": bar_color, "thickness": 0.3},
            "bgcolor": CARD_BG,
            "borderwidth": 0,
            "steps": [
                {"range": [0, 35],   "color": "#e8f7ef"},
                {"range": [35, 65],  "color": "#fdf3e3"},
                {"range": [65, 100], "color": "#fdecea"},
            ],
            "threshold": {
                "line": {"color": INK_DARK, "width": 2},
                "thickness": 0.75,
                "value": pct,
            },
        },
    ))
    fig.update_layout(**PLOTLY_LAYOUT, height=260)
    return fig


def sentiment_donut(summary: Dict) -> go.Figure:
    labels = ["Positively Engagement", "Neutral", "Negatively Engagement"]
    values = [summary["POSITIVE"], summary["NEUTRAL"], summary["NEGATIVE"]]
    colors = [STORMY_SKY, INK_GREY, STORMY_SLATE]

    fig = go.Figure(go.Pie(
        labels=labels,
        values=values,
        hole=0.62,
        marker=dict(colors=colors, line=dict(color=CARD_BG, width=3)),
        textinfo="label+percent",
        textfont=dict(family="'DM Mono', monospace", size=11, color=TEXT_MAIN),
        hovertemplate="<b>%{label}</b><br>%{value} comments (%{percent})<extra></extra>",
        rotation=90,
    ))

    avg = summary.get("avg_compound", 0)
    overall = "😊 Positive" if avg > 0.05 else ("😟 Negative" if avg < -0.05 else "😐 Mixed")
    fig.add_annotation(
        text=f"<b>{overall}</b><br><span style='font-size:11px;color:{TEXT_DIM}'>{summary['total']} comments</span>",
        x=0.5, y=0.5,
        showarrow=False,
        font=dict(size=13, color=TEXT_MAIN, family="'DM Mono', monospace"),
        align="center",
    )
    fig.update_layout(
        **PLOTLY_LAYOUT,
        height=300,
        legend=dict(orientation="h", y=-0.08, font=dict(size=11, color=TEXT_MAIN)),
    )
    return fig


def keyword_bar(
    keywords: List[Tuple[str, float]],
    title: str = "Top Keywords",
    color: str = PRIMARY,
) -> go.Figure:
    if not keywords:
        return _empty_fig(title)

    words, weights = zip(*keywords[:15])
    max_w = max(weights) or 1
    norm = [w / max_w * 100 for w in weights]

    fig = go.Figure(go.Bar(
        x=norm,
        y=words,
        orientation="h",
        marker=dict(
            color=norm,
            colorscale=[[0, f"{color}44"], [1, color]],
            line=dict(width=0),
        ),
        text=[f"{w:.0f}" for w in weights],
        textposition="inside",
        textfont=dict(size=10, color=CARD_BG),
        hovertemplate="<b>%{y}</b><br>Weight: %{text}<extra></extra>",
    ))
    fig.update_layout(
        **PLOTLY_LAYOUT,
        title=dict(text=title, font=dict(size=13, color=INK_DARK), x=0),
        height=380,
        yaxis=dict(
            autorange="reversed",
            tickfont=dict(size=11, color=TEXT_MAIN),
            gridcolor=BORDER,
        ),
        xaxis=dict(showticklabels=False, gridcolor=BORDER),
        bargap=0.35,
    )
    return fig


def stream_trust_bars(stream_details: Dict) -> go.Figure:
    labels = list(stream_details.keys())
    values = [round(v * 100, 1) for v in stream_details.values()]
    colors = [RED if v > 50 else (AMBER if v > 30 else GREEN) for v in values]

    fig = go.Figure(go.Bar(
        x=values,
        y=[l.replace("_", " ").title() for l in labels],
        orientation="h",
        marker=dict(color=colors, line=dict(color=CARD_BG, width=1)),
        text=[f"{v}%" for v in values],
        textposition="outside",
        textfont=dict(size=11, color=TEXT_MAIN),
        hovertemplate="<b>%{y}</b><br>Score: %{x}%<extra></extra>",
    ))
    fig.update_layout(
        **PLOTLY_LAYOUT,
        title=dict(text="Per-Stream Analysis", font=dict(size=13, color=INK_DARK), x=0),
        height=220,
        xaxis=dict(range=[0, 120], showticklabels=False, gridcolor=BORDER),
        yaxis=dict(tickfont=dict(size=11, color=TEXT_MAIN)),
        bargap=0.4,
    )
    return fig


def modality_misinfo_distribution(modality_analysis: Dict) -> go.Figure:
    MODALITIES = ["Text", "Audio", "Video"]
    KEYS       = ["text", "audio", "video"]

    misinfo_pcts  = [modality_analysis.get(k, {}).get("misinfo_pct",  50.0) for k in KEYS]
    credible_pcts = [modality_analysis.get(k, {}).get("credible_pct", 50.0) for k in KEYS]
    logit_tips    = [
        (f"logit_m={modality_analysis.get(k, {}).get('misinfo_logit', 0.0):+.4f} | "
         f"logit_c={modality_analysis.get(k, {}).get('credible_logit', 0.0):+.4f}")
        for k in KEYS
    ]

    fig = go.Figure()

    fig.add_trace(go.Bar(
        name="Misinformation Score",
        x=MODALITIES,
        y=misinfo_pcts,
        marker=dict(
            color=[RED, RED, RED],
            opacity=0.90,
            line=dict(color=CARD_BG, width=2),
        ),
        text=[f"{v:.1f}%" for v in misinfo_pcts],
        textposition="outside",
        textfont=dict(size=11, color=RED, family="'DM Mono', monospace"),
        customdata=logit_tips,
        hovertemplate=(
            "<b>%{x} — Misinformation</b><br>"
            "Softmax: %{y:.2f}%<br>"
            "%{customdata}<extra></extra>"
        ),
    ))

    fig.add_trace(go.Bar(
        name="Not Misinformation",
        x=MODALITIES,
        y=credible_pcts,
        marker=dict(
            color=[GREEN, GREEN, GREEN],
            opacity=0.90,
            line=dict(color=CARD_BG, width=2),
        ),
        text=[f"{v:.1f}%" for v in credible_pcts],
        textposition="outside",
        textfont=dict(size=11, color=GREEN, family="'DM Mono', monospace"),
        customdata=logit_tips,
        hovertemplate=(
            "<b>%{x} — Credible</b><br>"
            "Softmax: %{y:.2f}%<br>"
            "%{customdata}<extra></extra>"
        ),
    ))

    fig.update_layout(
        **PLOTLY_LAYOUT,
        title=dict(
            text="Modality Misinformation Distribution",
            font=dict(size=13, color=INK_DARK),
            x=0,
        ),
        barmode="group",
        height=300,
        xaxis=dict(
            title=dict(text="Modality", font=dict(color=TEXT_MAIN)),
            tickfont=dict(size=12, color=TEXT_MAIN),
            gridcolor=BORDER,
        ),
        yaxis=dict(
            title=dict(text="Softmax Score (%)", font=dict(color=TEXT_MAIN)),
            range=[0, 125],
            gridcolor=BORDER,
            ticksuffix="%",
            tickfont=dict(color=TEXT_MAIN),
        ),
        legend=dict(
            orientation="h",
            y=1.14,
            font=dict(size=11, color=TEXT_MAIN),
            bgcolor="rgba(0,0,0,0)",
        ),
        bargap=0.22,
        bargroupgap=0.06,
    )
    return fig


def trust_score_by_modality(modality_analysis: Dict) -> go.Figure:
    MODALITIES = ["Text", "Audio", "Video"]
    KEYS       = ["text", "audio", "video"]

    trust_vals = [modality_analysis.get(k, {}).get("trust_score", 0.0) for k in KEYS]
    bar_colors = [
        (GREEN if v >= 60 else (AMBER if v >= 35 else RED))
        for v in trust_vals
    ]

    fig = go.Figure(go.Bar(
        x=MODALITIES,
        y=trust_vals,
        marker=dict(
            color=bar_colors,
            opacity=0.90,
            line=dict(color=CARD_BG, width=2),
        ),
        text=[f"{v:.1f}%" for v in trust_vals],
        textposition="outside",
        textfont=dict(size=11, color=TEXT_MAIN, family="'DM Mono', monospace"),
        hovertemplate=(
            "<b>%{x}</b><br>"
            "Trust Level: %{y:.2f}%<br>"
            "<i>Derived from (1 – H_entropy) × content_richness</i>"
            "<extra></extra>"
        ),
    ))

    for level, label, color in [(80, "High Trust", GREEN), (50, "Threshold", AMBER)]:
        fig.add_hline(
            y=level,
            line=dict(color=color, width=1.5, dash="dot"),
            annotation_text=label,
            annotation_position="right",
            annotation_font=dict(size=9, color=color),
        )

    fig.update_layout(
        **PLOTLY_LAYOUT,
        title=dict(
            text="Trust Score by Modality",
            font=dict(size=13, color=INK_DARK),
            x=0,
        ),
        height=300,
        xaxis=dict(
            title=dict(text="Modality", font=dict(color=TEXT_MAIN)),
            tickfont=dict(size=12, color=TEXT_MAIN),
            gridcolor=BORDER,
        ),
        yaxis=dict(
            title=dict(text="Trust Level (%)", font=dict(color=TEXT_MAIN)),
            range=[0, 120],
            gridcolor=BORDER,
            ticksuffix="%",
            tickfont=dict(color=TEXT_MAIN),
        ),
        bargap=0.38,
    )
    return fig


def uncertainty_analysis(modality_analysis: Dict) -> go.Figure:
    MODALITIES = ["Text", "Audio", "Video"]
    KEYS       = ["text", "audio", "video"]

    uncertainty_vals = [modality_analysis.get(k, {}).get("uncertainty", 100.0) for k in KEYS]
    misinfo_pcts     = [modality_analysis.get(k, {}).get("misinfo_pct", 50.0)  for k in KEYS]

    bar_colors = [
        (GREEN if v <= 35 else (AMBER if v <= 65 else RED))
        for v in uncertainty_vals
    ]

    fig = go.Figure(go.Bar(
        x=MODALITIES,
        y=uncertainty_vals,
        marker=dict(
            color=bar_colors,
            opacity=0.90,
            line=dict(color=CARD_BG, width=2),
        ),
        text=[f"{v:.1f}%" for v in uncertainty_vals],
        textposition="outside",
        textfont=dict(size=11, color=TEXT_MAIN, family="'DM Mono', monospace"),
        customdata=[[f"p_misinfo={m:.1f}%"] for m in misinfo_pcts],
        hovertemplate=(
            "<b>%{x}</b><br>"
            "Uncertainty (H): %{y:.2f}%<br>"
            "%{customdata[0]}<br>"
            "<i>H = –Σ p·log₂(p), normalised to %</i>"
            "<extra></extra>"
        ),
    ))

    fig.add_hline(
        y=100,
        line=dict(color=RED, width=1.5, dash="dot"),
        annotation_text="Max Entropy (no signal)",
        annotation_position="right",
        annotation_font=dict(size=9, color=RED),
    )
    fig.add_hline(
        y=50,
        line=dict(color=AMBER, width=1.5, dash="dot"),
        annotation_text="Mid Uncertainty",
        annotation_position="right",
        annotation_font=dict(size=9, color=AMBER),
    )

    fig.update_layout(
        **PLOTLY_LAYOUT,
        title=dict(
            text="Uncertainty Analysis (Shannon Entropy)",
            font=dict(size=13, color=INK_DARK),
            x=0,
        ),
        height=300,
        xaxis=dict(
            title=dict(text="Modality", font=dict(color=TEXT_MAIN)),
            tickfont=dict(size=12, color=TEXT_MAIN),
            gridcolor=BORDER,
        ),
        yaxis=dict(
            title=dict(text="Uncertainty (%)", font=dict(color=TEXT_MAIN)),
            range=[0, 130],
            gridcolor=BORDER,
            ticksuffix="%",
            tickfont=dict(color=TEXT_MAIN),
        ),
        bargap=0.38,
    )
    return fig


def sentiment_timeline(comments_df: pd.DataFrame, sentiments: List[Dict]) -> go.Figure:
    if comments_df.empty:
        return _empty_fig("Comment Sentiment Distribution")

    df = comments_df.copy()
    df["compound"]   = [s.get("compound", 0) for s in sentiments]
    df["label"]      = [s.get("label", "NEUTRAL") for s in sentiments]
    df["color"]      = df["label"].map({"POSITIVE": STORMY_SKY, "NEGATIVE": STORMY_SLATE, "NEUTRAL": INK_GREY})
    df["text_short"] = df["text"].str[:80] + "…"

    fig = go.Figure()
    for lbl, clr, disp in [
        ("POSITIVE", STORMY_SKY,   "Positively Engagement"),
        ("NEGATIVE", STORMY_SLATE, "Negatively Engagement"),
        ("NEUTRAL",  INK_GREY,     "Neutral"),
    ]:
        sub = df[df["label"] == lbl]
        if sub.empty:
            continue
        fig.add_trace(go.Scatter(
            x=sub.index,
            y=sub["compound"],
            mode="markers",
            name=disp,
            marker=dict(
                size=np.clip(np.log1p(sub["likes"].fillna(0)) * 4 + 4, 4, 20),
                color=clr,
                opacity=0.80,
                line=dict(width=0),
            ),
            text=sub["text_short"],
            hovertemplate="<b>%{text}</b><br>Sentiment: %{y:.2f}<br>Likes: %{marker.size}<extra></extra>",
        ))

    fig.add_hline(y=0, line=dict(color=BORDER, width=1, dash="dot"))
    fig.update_layout(
        **PLOTLY_LAYOUT,
        title=dict(text="Comment Sentiment (size = likes)", font=dict(size=13, color=INK_DARK), x=0),
        height=320,
        xaxis=dict(title="Comment index", gridcolor=BORDER, showgrid=False, tickfont=dict(color=TEXT_MAIN)),
        yaxis=dict(title="Compound score", gridcolor=BORDER, range=[-1.1, 1.1], tickfont=dict(color=TEXT_MAIN)),
        legend=dict(orientation="h", y=1.14, font=dict(size=11, color=TEXT_MAIN)),
    )
    return fig


def keyword_comparison(
    pos_kw: List[Tuple[str, float]],
    neg_kw: List[Tuple[str, float]],
) -> go.Figure:
    if not pos_kw and not neg_kw:
        return _empty_fig("Sentiment Keywords")

    top = 10
    pos_kw = pos_kw[:top]
    neg_kw = neg_kw[:top]

    fig = go.Figure()

    if pos_kw:
        pw, pv = zip(*pos_kw)
        max_p = max(pv) or 1
        fig.add_trace(go.Bar(
            name="Positively Engagement",
            y=list(pw),
            x=[v / max_p * 100 for v in pv],
            orientation="h",
            marker_color=STORMY_SKY,
            hovertemplate="<b>%{y}</b><br>Score: %{x:.1f}<extra></extra>",
        ))

    if neg_kw:
        nw, nv = zip(*neg_kw)
        max_n = max(nv) or 1
        fig.add_trace(go.Bar(
            name="Negatively Engagement",
            y=list(nw),
            x=[-v / max_n * 100 for v in nv],
            orientation="h",
            marker_color=STORMY_SLATE,
            hovertemplate="<b>%{y}</b><br>Score: %{x:.1f}<extra></extra>",
        ))

    fig.update_layout(
        **PLOTLY_LAYOUT,
        title=dict(text="Sentiment-Weighted Keywords", font=dict(size=13, color=INK_DARK), x=0),
        height=360,
        barmode="overlay",
        xaxis=dict(
            title="← Negatively Engagement  |  Positively Engagement →",
            gridcolor=BORDER,
            zeroline=True,
            zerolinecolor=INK_DARK,
            zerolinewidth=2,
            tickfont=dict(color=TEXT_MAIN),
        ),
        yaxis=dict(tickfont=dict(size=10, color=TEXT_MAIN)),
        legend=dict(orientation="h", y=1.1, font=dict(size=11, color=TEXT_MAIN)),
    )
    return fig


def _empty_fig(title: str) -> go.Figure:
    fig = go.Figure()
    fig.add_annotation(
        text="No data available",
        x=0.5, y=0.5,
        showarrow=False,
        font=dict(size=14, color=TEXT_DIM),
    )
    fig.update_layout(**PLOTLY_LAYOUT, title=dict(text=title, x=0, font=dict(color=INK_DARK)), height=250)
    return fig