import os
import pandas as pd
import gradio as gr

from fetcher import (
    extract_video_id,
    fetch_video_metadata,
    fetch_transcript,
    fetch_comments,
    search_videos_by_title,
)
from analyzer import (
    detect_misinformation,
    analyze_sentiment_batch,
    sentiment_summary,
    extract_keywords,
    sentiment_weighted_keywords,
)
from charts import (
    sentiment_donut,
    keyword_bar,
    sentiment_timeline,
    keyword_comparison,
    modality_misinfo_distribution,
    trust_score_by_modality,
    uncertainty_analysis,
)


CSS = """
@import url('https://fonts.googleapis.com/css2?family=DM+Mono:wght@400;500&family=Syne:wght@400;600;700;800&family=IBM+Plex+Sans:wght@300;400;500&display=swap');

:root {
    --bg:          #FFFFE3;
    --card:        #FFFFFF;
    --border:      #BDDDFC;
    --text:        #4A4A4A;
    --dim:         #7b7b7b;
    --primary:     #269ccc;
    --ink-dark:    #384959;
    --stormy-sky:  #88BDF2;
    --stormy-slate:#6A89A7;
    --ink-grey:    #CBCBCB;
    --green:       #2e9e6b;
    --red:         #c0392b;
    --amber:       #d4841a;
}

html, body {
    background: var(--bg) !important;
    color:      var(--text) !important;
    margin: 0; padding: 0;
}
.gradio-container, #root, #app, main, .main, .wrap, .svelte-1kyws56 {
    background: var(--bg) !important;
    max-width: 100% !important;
    width: 100% !important;
    margin: 0 auto !important;
    padding: 0 1.5rem !important;
    box-sizing: border-box !important;
}
.block, .wrap, .panel, .padded, div.form,
div[class*="block"], div[class*="wrap"],
div[class*="panel"], div[class*="gap"],
.gap { background: transparent !important; border: none !important; }

.gr-group, .gr-box, .vv-section {
    background: var(--card) !important;
    border: 1px solid var(--border) !important;
    border-radius: 12px !important;
    padding: 1rem 1.25rem !important;
}

.tab-nav button {
    background: transparent !important;
    border: none !important;
    color: var(--dim) !important;
    font-family: 'DM Mono', monospace !important;
    font-size: 0.82rem !important;
    letter-spacing: 0.05em !important;
    border-bottom: 2px solid transparent !important;
    padding: 0.5rem 1.2rem !important;
    transition: color 0.18s;
}
.tab-nav button.selected {
    color: var(--primary) !important;
    border-bottom-color: var(--primary) !important;
}
.tab-nav { border-bottom: 1px solid var(--border) !important; }

input[type="text"], input[type="password"], input[type="number"], textarea, select {
    background: #f5f7fa !important;
    border: 1px solid var(--border) !important;
    color: var(--text) !important;
    border-radius: 8px !important;
    font-family: 'DM Mono', monospace !important;
    font-size: 0.88rem !important;
}
input:focus, textarea:focus, select:focus {
    border-color: var(--primary) !important;
    box-shadow: 0 0 0 2px rgba(38,156,204,0.18) !important;
    outline: none !important;
}
label, .gr-label, span.svelte-1b6s6s {
    color: var(--dim) !important;
    font-family: 'DM Mono', monospace !important;
    font-size: 0.75rem !important;
    letter-spacing: 0.08em !important;
    text-transform: uppercase;
}

input[type="range"] { accent-color: var(--primary); }

button.primary, button[variant="primary"], .primary {
    background: linear-gradient(135deg, var(--primary), #1a7aaa) !important;
    border: none !important;
    color: #ffffff !important;
    font-weight: 700 !important;
    font-family: 'DM Mono', monospace !important;
    border-radius: 8px !important;
    letter-spacing: 0.06em !important;
}
button.secondary {
    background: rgba(38,156,204,0.08) !important;
    border: 1px solid var(--primary) !important;
    color: var(--primary) !important;
    border-radius: 8px !important;
    font-family: 'DM Mono', monospace !important;
}
button:hover { opacity: 0.88; transform: translateY(-1px); transition: all 0.15s; }

.dropdown, ul[role="listbox"], li[role="option"] {
    background: #f5f7fa !important;
    border-color: var(--border) !important;
    color: var(--text) !important;
}
li[role="option"]:hover { background: #e8f4fb !important; }

.gr-dataframe, table { background: var(--card) !important; }
.gr-dataframe th {
    background: #EEF6FD !important;
    color: var(--primary) !important;
    font-family: 'DM Mono', monospace !important;
    font-size: 0.72rem !important;
    padding: 6px 10px;
    border-bottom: 1px solid var(--border);
    text-transform: uppercase;
    letter-spacing: 0.08em;
}
.gr-dataframe td {
    color: var(--text) !important;
    font-size: 0.77rem !important;
    padding: 5px 10px;
    border-bottom: 1px solid var(--border);
}
.gr-dataframe tr:hover td { background: rgba(38,156,204,0.05) !important; }

details > summary {
    color: var(--dim) !important;
    font-family: 'DM Mono', monospace !important;
    font-size: 0.82rem !important;
    cursor: pointer;
    list-style: none;
}
details[open] > summary { color: var(--primary) !important; }

.js-plotly-plot, .plotly { background: transparent !important; }
.modebar { display: none !important; }

::-webkit-scrollbar { width: 6px; height: 6px; }
::-webkit-scrollbar-track { background: var(--bg); }
::-webkit-scrollbar-thumb { background: var(--border); border-radius: 3px; }
::-webkit-scrollbar-thumb:hover { background: var(--dim); }

.vv-hero {
    font-family: 'Syne', sans-serif !important;
    font-size: 1.65rem !important;
    font-weight: 800 !important;
    background: linear-gradient(135deg, #269ccc, #384959);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    background-clip: text;
    letter-spacing: -0.02em;
    line-height: 1.2;
}
.vv-section-title {
    font-family: 'Syne', sans-serif !important;
    font-size: 0.68rem !important;
    font-weight: 700 !important;
    letter-spacing: 0.18em !important;
    text-transform: uppercase !important;
    color: #384959 !important;
    margin-bottom: 0.5rem !important;
    margin-top: 0 !important;
}

.vv-card {
    background: #FFFFFF !important;
    border: 1px solid #BDDDFC !important;
    border-radius: 12px !important;
    padding: 1.1rem 1.3rem !important;
    margin-bottom: 0.7rem !important;
}

.vv-metric-grid {
    display: grid !important;
    grid-template-columns: repeat(4, 1fr) !important;
    gap: 0.55rem !important;
    margin: 0.4rem 0 1rem !important;
}
.vv-metric-card {
    background: #FFFFFF !important;
    border: 1px solid #BDDDFC !important;
    border-radius: 12px !important;
    padding: 0.8rem 0.7rem !important;
    text-align: center !important;
    transition: transform 0.18s ease, box-shadow 0.18s ease !important;
    cursor: default !important;
}
.vv-metric-card:hover {
    transform: translateY(-4px) !important;
    box-shadow: 0 8px 24px rgba(38,156,204,0.18) !important;
}
.vv-metric-value {
    display: block !important;
    font-family: 'DM Mono', monospace !important;
    font-size: 1.15rem !important;
    font-weight: 700 !important;
    color: #269ccc !important;
    margin: 0 !important;
    line-height: 1.2 !important;
}
.vv-metric-label {
    display: block !important;
    font-family: 'DM Mono', monospace !important;
    font-size: 0.62rem !important;
    letter-spacing: 0.1em !important;
    text-transform: uppercase !important;
    color: #7b7b7b !important;
    margin: 4px 0 0 !important;
}

.vv-stat {
    display: inline-block !important;
    background: #EEF6FD !important;
    border: 1px solid #BDDDFC !important;
    border-radius: 6px !important;
    padding: 0.25rem 0.75rem !important;
    font-family: 'DM Mono', monospace !important;
    font-size: 0.77rem !important;
    color: #269ccc !important;
    margin: 0.15rem 0.2rem !important;
}

.vv-badge-green {
    display: inline-block !important;
    background: rgba(46,158,107,0.10) !important;
    border: 1px solid #2e9e6b !important;
    color: #2e9e6b !important;
    border-radius: 20px !important;
    padding: 0.32rem 1.1rem !important;
    font-size: 0.85rem !important;
    font-family: 'DM Mono', monospace !important;
    font-weight: 600 !important;
}
.vv-badge-red {
    display: inline-block !important;
    background: rgba(192,57,43,0.10) !important;
    border: 1px solid #c0392b !important;
    color: #c0392b !important;
    border-radius: 20px !important;
    padding: 0.32rem 1.1rem !important;
    font-size: 0.85rem !important;
    font-family: 'DM Mono', monospace !important;
    font-weight: 600 !important;
}
.vv-badge-amber {
    display: inline-block !important;
    background: rgba(212,132,26,0.10) !important;
    border: 1px solid #d4841a !important;
    color: #d4841a !important;
    border-radius: 20px !important;
    padding: 0.32rem 1.1rem !important;
    font-size: 0.85rem !important;
    font-family: 'DM Mono', monospace !important;
    font-weight: 600 !important;
}

.vv-reasoning {
    background: #f7f9fb !important;
    border-left: 3px solid #d4841a !important;
    padding: 0.8rem 1rem !important;
    border-radius: 0 8px 8px 0 !important;
    font-size: 0.83rem !important;
    color: #4A4A4A !important;
    line-height: 1.65 !important;
    font-family: 'IBM Plex Sans', sans-serif !important;
    margin-top: 8px !important;
}

.vv-tag {
    display: inline-block !important;
    background: #BDDDFC !important;
    border: none !important;
    border-radius: 20px !important;
    padding: 3px 10px !important;
    font-family: 'DM Mono', monospace !important;
    font-size: 0.7rem !important;
    color: #384959 !important;
    margin: 2px !important;
    font-weight: 500 !important;
}

.vv-stat-big-green {
    font-family: 'DM Mono', monospace !important;
    font-size: 1.6rem !important;
    font-weight: 700 !important;
    color: #2e9e6b !important;
    margin: 0 !important;
}
.vv-stat-big-red {
    font-family: 'DM Mono', monospace !important;
    font-size: 1.6rem !important;
    font-weight: 700 !important;
    color: #c0392b !important;
    margin: 0 !important;
}
.vv-stat-big-dim {
    font-family: 'DM Mono', monospace !important;
    font-size: 1.6rem !important;
    font-weight: 700 !important;
    color: #7b7b7b !important;
    margin: 0 !important;
}
.vv-log-line {
    font-size: 0.72rem !important;
    color: #7b7b7b !important;
    font-family: 'DM Mono', monospace !important;
    margin: 2px 0 !important;
}
.vv-hr { border: none; border-top: 1px solid #BDDDFC; margin: 1.1rem 0; }
"""


def _empty_plotly(msg: str = "Run analysis to see data", h: int = 230):
    import plotly.graph_objects as go
    fig = go.Figure()
    fig.update_layout(
        paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(189,221,252,0.13)",
        font=dict(color="#7b7b7b"), margin=dict(l=10, r=10, t=10, b=10), height=h,
    )
    fig.add_annotation(
        text=msg, x=0.5, y=0.5, xref="paper", yref="paper",
        showarrow=False, font=dict(size=12, color="#7b7b7b"),
    )
    return fig


def _blank_outputs(status_msg: str):
    ep = _empty_plotly()
    return (
        f'<p style="color:#c0392b;font-family:DM Mono,monospace;padding:8px">{status_msg}</p>',
        "<p class='vv-log-line'>—</p>",
        "<div style='padding:3rem;text-align:center;color:#7b7b7b;font-family:DM Mono,monospace'>No data yet.</div>",
        "", "",
        ep, ep, ep,
        ep, ep, ep, ep,
        "", "", "",
        pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(),
    )


def run_pipeline(
    url_or_id: str,
    sentiment_method: str,
    max_comments: int,
    progress=gr.Progress(track_tqdm=False),
):
    api_key = os.environ.get("YT_API_KEY", "").strip()

    if not (url_or_id or "").strip():
        yield _blank_outputs("⚠️ Please enter a YouTube URL or video ID.")
        return

    video_id = extract_video_id(url_or_id.strip())
    if not video_id:
        yield _blank_outputs("❌ Could not parse a valid YouTube video ID.")
        return

    if not api_key:
        yield _blank_outputs(
            "⚠️ YouTube API key not found. "
            "Set the <code>YT_API_KEY</code> environment variable / Space secret."
        )
        return

    progress(0.05, desc="Fetching video metadata…")
    meta, err = fetch_video_metadata(video_id, api_key)
    if err:
        yield _blank_outputs(f"❌ {err}")
        return

    progress(0.20, desc="Fetching transcript…")
    transcript, t_status = fetch_transcript(video_id)

    progress(0.35, desc=f"Fetching up to {max_comments} comments…")
    comments_df, c_status = fetch_comments(video_id, api_key, max_comments=int(max_comments))

    progress(0.50, desc="Running misinformation detection…")
    misinfo = detect_misinformation(
        text=f"{meta['title']} {meta['description']}",
        tags=meta["tags"],
        audio_transcript=transcript,
        video_transcript=transcript,
    )

    keywords = extract_keywords(
        f"{meta['title']} {meta['description']} {transcript}",
        meta["tags"],
    )

    sentiments, sent_sum, pos_kw, neg_kw = [], {}, [], []

    if not comments_df.empty:
        texts = comments_df["text"].fillna("").tolist()
        batch = 64
        for i in range(0, len(texts), batch):
            chunk = texts[i: i + batch]
            sentiments += analyze_sentiment_batch(chunk, method=sentiment_method, batch_size=batch)
            frac = 0.60 + 0.30 * min((i + batch) / max(len(texts), 1), 1.0)
            progress(frac, desc=f"Sentiment {min(i+batch, len(texts))}/{len(texts)}…")

        sent_sum = sentiment_summary(sentiments)
        pos_kw, neg_kw = sentiment_weighted_keywords(comments_df, sentiments)

    progress(0.97, desc="Building charts…")
    yield _build_outputs(
        meta=meta, video_id=video_id, transcript=transcript,
        comments_df=comments_df, misinfo=misinfo, keywords=keywords,
        sentiments=sentiments, sent_sum=sent_sum,
        pos_kw=pos_kw, neg_kw=neg_kw,
        status_log=[
            f"✅ Metadata: {meta['title'][:55]}",
            t_status,
            c_status,
            f"🔬 Misinfo score: {misinfo['confidence_pct']}%",
            *(
                [f"💬 Sentiment: {sent_sum['pos_pct']}% pos / {sent_sum['neg_pct']}% neg"]
                if sent_sum
                else ["💬 No comments — sentiment skipped"]
            ),
        ],
    )


def _build_outputs(
    meta, video_id, transcript, comments_df,
    misinfo, keywords, sentiments, sent_sum, pos_kw, neg_kw, status_log,
):
    status_html = (
        '<p style="color:#2e9e6b;font-family:DM Mono,monospace;font-size:0.82rem;padding:6px 0">'
        "✅ Analysis complete</p>"
    )

    log_html = "".join(f'<p class="vv-log-line">{line}</p>' for line in status_log)

    thumb_html = (
        f'<img src="{meta["thumbnail_url"]}" '
        'style="width:100%;border-radius:8px;margin-bottom:8px;display:block">'
        if meta.get("thumbnail_url") else ""
    )
    tag_html = "".join(f'<span class="vv-tag">#{t}</span>' for t in meta.get("tags", [])[:20])
    desc_short = meta.get("description", "")[:1200]
    word_count = len(transcript.split()) if transcript else 0
    transcript_short = (transcript[:2500] + "…" if len(transcript) > 2500 else transcript) if transcript else "(not available)"

    left_html = f"""
    {thumb_html}
    <a href="https://www.youtube.com/watch?v={video_id}" target="_blank"
       style="display:block;text-align:center;font-family:'DM Mono',monospace;
              font-size:0.75rem;color:#7b7b7b;text-decoration:none;margin:4px 0 10px">
      ▶ Open on YouTube
    </a>
    <div class="vv-card">
      <p class="vv-section-title">Video</p>
      <p style="font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin:0 0 6px;color:#4A4A4A !important">
        {meta['title']}
      </p>
      <p style="font-size:0.82rem;color:#7b7b7b !important;margin:0">
        by <b style="color:#384959 !important">{meta['channel_title']}</b>
        &nbsp;·&nbsp;
        <span style="color:#7b7b7b !important">{meta['published_at']}</span>
      </p>
    </div>

    <p class="vv-section-title">Metrics</p>
    <div class="vv-metric-grid">
      <div class="vv-metric-card">
        <span class="vv-metric-value">👁 {meta['view_count']:,}</span>
        <span class="vv-metric-label">Views</span>
      </div>
      <div class="vv-metric-card">
        <span class="vv-metric-value">👍 {meta['like_count']:,}</span>
        <span class="vv-metric-label">Likes</span>
      </div>
      <div class="vv-metric-card">
        <span class="vv-metric-value">💬 {meta['comment_count']:,}</span>
        <span class="vv-metric-label">Comments</span>
      </div>
      <div class="vv-metric-card">
        <span class="vv-metric-value">⏱ {meta['duration']}</span>
        <span class="vv-metric-label">Duration</span>
      </div>
    </div>

    <p class="vv-section-title" style="margin-top:0.8rem">Tags</p>
    {tag_html or '<span style="color:#7b7b7b;font-size:0.78rem">(none)</span>'}

    <details style="margin-top:1rem">
      <summary>📄 Description</summary>
      <p style="font-size:0.78rem;color:#7b7b7b;line-height:1.65;white-space:pre-wrap;margin-top:6px">{desc_short}</p>
    </details>
    <details style="margin-top:0.5rem">
      <summary>📝 Transcript ({word_count} words)</summary>
      <p style="font-size:0.75rem;color:#7b7b7b;line-height:1.65;margin-top:6px">{transcript_short}</p>
    </details>
    """

    score = misinfo["score"]
    if score < 0.35:
        badge_html = '<span class="vv-badge-green">✅ Appears Credible</span>'
    elif score < 0.65:
        badge_html = '<span class="vv-badge-amber">⚠️ Uncertain / Mixed Signals</span>'
    else:
        badge_html = '<span class="vv-badge-red">🚨 Likely Misinformation</span>'

    reasoning_html = (
        f'<div class="vv-reasoning">🧠 <b>Reasoning:</b> {misinfo["reasoning"]}</div>'
    )

    mod_analysis = misinfo.get("modality_analysis", {})

    try:
        fig_mod_dist = modality_misinfo_distribution(mod_analysis)
    except Exception:
        fig_mod_dist = _empty_plotly("Modality distribution unavailable")

    try:
        fig_trust = trust_score_by_modality(mod_analysis)
    except Exception:
        fig_trust = _empty_plotly("Trust score unavailable")

    try:
        fig_uncert = uncertainty_analysis(mod_analysis)
    except Exception:
        fig_uncert = _empty_plotly("Uncertainty analysis unavailable")

    try:
        fig_donut = sentiment_donut(sent_sum) if sent_sum else _empty_plotly("No comments analysed")
    except Exception:
        fig_donut = _empty_plotly()

    try:
        fig_timeline = (
            sentiment_timeline(comments_df, sentiments)
            if (sent_sum and not comments_df.empty)
            else _empty_plotly("No comments analysed")
        )
    except Exception:
        fig_timeline = _empty_plotly()

    try:
        fig_kw = keyword_bar(keywords, title="Top Video Keywords", color="#269ccc")
    except Exception:
        fig_kw = _empty_plotly()

    try:
        fig_kw_comp = (
            keyword_comparison(pos_kw, neg_kw)
            if (pos_kw or neg_kw)
            else _empty_plotly("No keyword comparison — no comments")
        )
    except Exception:
        fig_kw_comp = _empty_plotly()

    if sent_sum:
        stat_pos = (
            f'<div class="vv-card" style="text-align:center">'
            f'<p class="vv-stat-big-green">{sent_sum["pos_pct"]}%</p>'
            f'<p style="color:#7b7b7b !important;font-size:0.75rem;margin:4px 0 0;font-family:DM Mono,monospace">Positively Engagement</p></div>'
        )
        stat_neg = (
            f'<div class="vv-card" style="text-align:center">'
            f'<p class="vv-stat-big-red">{sent_sum["neg_pct"]}%</p>'
            f'<p style="color:#7b7b7b !important;font-size:0.75rem;margin:4px 0 0;font-family:DM Mono,monospace">Negatively Engagement</p></div>'
        )
        stat_neu = (
            f'<div class="vv-card" style="text-align:center">'
            f'<p class="vv-stat-big-dim">{sent_sum["neu_pct"]}%</p>'
            f'<p style="color:#7b7b7b !important;font-size:0.75rem;margin:4px 0 0;font-family:DM Mono,monospace">Neutral</p></div>'
        )
    else:
        placeholder = (
            '<div class="vv-card" style="text-align:center;color:#7b7b7b !important;'
            'font-family:DM Mono,monospace;font-size:0.8rem;padding:1.2rem">N/A</div>'
        )
        stat_pos = stat_neg = stat_neu = placeholder

    show_cols = ["author", "text", "likes", "published_at"]
    df_all = df_pos = df_neg = df_top = pd.DataFrame()

    if not comments_df.empty:
        display_df = comments_df.copy()
        if sentiments:
            display_df["sentiment"] = [s["label"] for s in sentiments]
            display_df["compound"]  = [round(s.get("compound", 0), 3) for s in sentiments]
            cols = show_cols + ["sentiment", "compound"]
        else:
            cols = show_cols

        if "sentiment" in display_df.columns:
            df_pos = display_df[display_df["sentiment"] == "POSITIVE"][cols].head(50).reset_index(drop=True)
            df_neg = display_df[display_df["sentiment"] == "NEGATIVE"][cols].head(50).reset_index(drop=True)
            display_df["sentiment"] = display_df["sentiment"].replace({
                "POSITIVE": "Positively Engagement",
                "NEGATIVE": "Negatively Engagement",
                "NEUTRAL":  "Neutral",
            })
            df_pos["sentiment"] = "Positively Engagement"
            df_neg["sentiment"] = "Negatively Engagement"

        df_all = display_df[cols].head(100).reset_index(drop=True)
        df_top = (
            display_df.sort_values("likes", ascending=False)
            .head(20)[cols]
            .reset_index(drop=True)
        )

    return (
        status_html,
        log_html,
        left_html,
        badge_html,
        reasoning_html,
        fig_mod_dist,
        fig_trust,
        fig_uncert,
        fig_donut,
        fig_timeline,
        fig_kw,
        fig_kw_comp,
        stat_pos,
        stat_neg,
        stat_neu,
        df_all,
        df_pos,
        df_neg,
        df_top,
    )


def do_search(keyword: str):
    api_key = os.environ.get("YT_API_KEY", "").strip()
    if not api_key:
        return (
            "<p style='color:#c0392b;font-family:DM Mono,monospace'>⚠️ YT_API_KEY secret not set.</p>",
            gr.update(choices=[], value=None, visible=False),
        )
    if not (keyword or "").strip():
        return (
            "<p style='color:#d4841a;font-family:DM Mono,monospace'>Enter a keyword to search.</p>",
            gr.update(choices=[], value=None, visible=False),
        )

    results = search_videos_by_title(keyword.strip(), api_key, max_results=5)
    if not results:
        return (
            "<p style='color:#d4841a;font-family:DM Mono,monospace'>No results found.</p>",
            gr.update(choices=[], value=None, visible=False),
        )

    html = ""
    choices = []
    for r in results:
        vid = r["video_id"]
        url = f"https://www.youtube.com/watch?v={vid}"
        choices.append((r["title"][:70], url))
        html += (
            f'<div class="vv-card" style="display:flex;align-items:center;gap:12px;margin-bottom:6px">'
            f'<img src="{r["thumbnail_url"]}" '
            f'     style="width:72px;height:54px;object-fit:cover;border-radius:6px;flex-shrink:0">'
            f'<div>'
            f'<p style="margin:0;font-size:0.85rem;font-weight:600;color:#4A4A4A !important">{r["title"][:80]}</p>'
            f'<p style="margin:0;font-size:0.75rem;color:#7b7b7b !important">'
            f'{r["channel_title"]} · {r["published_at"]} · '
            f'<code style="color:#269ccc">v={vid}</code></p>'
            f'</div></div>'
        )
    return html, gr.update(choices=choices, value=None, visible=True)


def pick_and_analyze(selected_url, sentiment_method, max_comments):
    if not selected_url:
        yield _blank_outputs("Select a video from the search results above.")
        return
    yield from run_pipeline(selected_url, sentiment_method, max_comments)


with gr.Blocks(title="Misinformation Detection & Public Engagement") as demo:

    gr.HTML("""
    <div style="padding:1.5rem 0 0.8rem;border-bottom:1px solid #BDDDFC;margin-bottom:1.2rem">
      <h1 class="vv-hero">🔬 Misinformation Detection & Public Engagement</h1>
    </div>
    """)

    with gr.Accordion("⚙️ Settings", open=False):
        gr.HTML("""
        <div style="background:#f5f7fa;border:1px solid #BDDDFC;border-radius:8px;
                    padding:0.7rem 1rem;margin-bottom:0.8rem;font-family:'DM Mono',monospace;
                    font-size:0.78rem;color:#7b7b7b">
          🔑 YouTube API key is read from the <code style="color:#269ccc">YT_API_KEY</code>
          Space secret — it is never exposed in the UI.
        </div>
        """)
        with gr.Row():
            sentiment_selector = gr.Dropdown(
                choices=[
                    ("VADER — fast, CPU-only (~5 000 comments/sec)", "vader"),
                    ("DistilBERT — accurate, downloads ~500 MB on first run", "hf"),
                ],
                value="vader",
                label="Sentiment Engine",
                scale=3,
            )
            max_comments_slider = gr.Slider(
                minimum=10, maximum=500, value=150, step=10,
                label="Max comments to fetch",
                scale=3,
                info="YouTube API quota: ~1 unit per comment request",
            )

    with gr.Tabs():

        with gr.TabItem("🔗 YouTube URL"):
            with gr.Row():
                url_input   = gr.Textbox(
                    placeholder="https://www.youtube.com/watch?v=...  or  youtu.be/...  or raw 11-char ID",
                    label="YouTube URL / Video ID",
                    scale=5,
                )
                analyze_btn = gr.Button("🔍 Analyze", variant="primary", scale=1, min_width=130)

        with gr.TabItem("📁 Upload / Search by Title"):
            gr.HTML("""
            <div class="vv-card" style="margin-bottom:8px">
              <p class="vv-section-title">Search by video title or keyword</p>
              <p style="font-size:0.82rem;color:#7b7b7b;line-height:1.6;margin:0">
                Upload your file, then type the title or keyword below to locate the matching YouTube entry.
              </p>
            </div>
            """)
            upload_file = gr.File(
                label="Drop a video file (mp4, mov, avi, mkv, webm)",
                file_types=[".mp4", ".mov", ".avi", ".mkv", ".webm"],
            )
            with gr.Row():
                kw_input   = gr.Textbox(placeholder="Enter video title or keyword…", label="Search keyword", scale=4)
                search_btn = gr.Button("🔎 Find on YouTube", scale=1)
            search_results_html = gr.HTML()
            search_radio        = gr.Radio(label="Select a video to analyze", choices=[], visible=False)

    status_box = gr.HTML(
        '<p style="color:#7b7b7b;font-family:DM Mono,monospace;font-size:0.8rem;padding:6px 0">'
        "Enter a URL above and click Analyze.</p>"
    )

    with gr.Row(equal_height=False):

        with gr.Column(scale=2):
            left_panel_html = gr.HTML(
                "<div style='padding:3rem;text-align:center;color:#7b7b7b;"
                "font-family:DM Mono,monospace'>No data yet.</div>"
            )

        with gr.Column(scale=3):

            gr.HTML('<p class="vv-section-title" style="margin-top:0">🔬 Misinformation Analysis</p>')
            misinfo_badge_html = gr.HTML()

            with gr.Row():
                modality_dist_plot = gr.Plot(label="", show_label=False)

            with gr.Row():
                trust_score_plot = gr.Plot(label="", show_label=False)
                uncertainty_plot = gr.Plot(label="", show_label=False)

            misinfo_reasoning_html = gr.HTML()

            gr.HTML('<hr class="vv-hr">')

            gr.HTML('<p class="vv-section-title">💬 Comment Sentiment</p>')
            with gr.Row():
                stat_pos_html = gr.HTML()
                stat_neg_html = gr.HTML()
                stat_neu_html = gr.HTML()
            with gr.Row():
                donut_plot    = gr.Plot(label="", show_label=False)
                timeline_plot = gr.Plot(label="", show_label=False)
            with gr.Row():
                kw_bar_plot   = gr.Plot(label="", show_label=False)
                kw_comp_plot  = gr.Plot(label="", show_label=False)

            gr.HTML('<hr class="vv-hr">')

            gr.HTML('<p class="vv-section-title">📊 Comments Deep-Dive</p>')
            with gr.Tabs():
                with gr.TabItem("All"):
                    df_all_out = gr.Dataframe(
                        headers=["author", "text", "likes", "published_at", "sentiment", "compound"],
                        datatype=["str", "str", "number", "str", "str", "number"],
                        wrap=True,
                        max_height=320,
                    )
                with gr.TabItem("Positively Engagement"):
                    df_pos_out = gr.Dataframe(wrap=True, max_height=320)
                with gr.TabItem("Negatively Engagement"):
                    df_neg_out = gr.Dataframe(wrap=True, max_height=320)
                with gr.TabItem("Most Liked"):
                    df_top_out = gr.Dataframe(wrap=True, max_height=320)

    with gr.Accordion("📜 Activity Log", open=False):
        log_html_out = gr.HTML('<p class="vv-log-line">—</p>')

    gr.HTML("""
    <div style="margin-top:2rem;padding-top:1rem;border-top:1px solid #BDDDFC;
                text-align:center;font-family:'DM Mono',monospace;font-size:0.72rem;color:#7b7b7b">
      4-stream SeTa-Attention BiGRU · CCM / DMTE / Uncertainty Fusion ·
      Test ROC-AUC 0.967
    </div>
    """)

    ALL_OUTPUTS = [
        status_box,
        log_html_out,
        left_panel_html,
        misinfo_badge_html,
        misinfo_reasoning_html,
        modality_dist_plot,
        trust_score_plot,
        uncertainty_plot,
        donut_plot,
        timeline_plot,
        kw_bar_plot,
        kw_comp_plot,
        stat_pos_html,
        stat_neg_html,
        stat_neu_html,
        df_all_out,
        df_pos_out,
        df_neg_out,
        df_top_out,
    ]

    _pipeline_inputs = [url_input, sentiment_selector, max_comments_slider]

    analyze_btn.click(fn=run_pipeline, inputs=_pipeline_inputs, outputs=ALL_OUTPUTS)
    url_input.submit(fn=run_pipeline,  inputs=_pipeline_inputs, outputs=ALL_OUTPUTS)

    search_btn.click(
        fn=do_search,
        inputs=[kw_input],
        outputs=[search_results_html, search_radio],
    )
    search_radio.change(
        fn=pick_and_analyze,
        inputs=[search_radio, sentiment_selector, max_comments_slider],
        outputs=ALL_OUTPUTS,
    )


if __name__ == "__main__":
    demo.launch(
        css=CSS,
        theme=gr.themes.Base(
            primary_hue=gr.themes.colors.blue,
            neutral_hue=gr.themes.colors.slate,
            font=[gr.themes.GoogleFont("IBM Plex Sans"), "sans-serif"],
        ),
    )