"""
Blockchain Intelligence Dashboard — 8-Dimension Cross-Chain Analyzer
HuggingFace Space: Interactive exploration of 50K real cryptocurrency transactions
across ETC, BTC, DOGE, BCH, DASH.

Upload your own CSVs or explore the pre-loaded dataset.
"""

import gradio as gr
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
from scipy import stats
from sklearn.ensemble import IsolationForest, RandomForestClassifier, GradientBoostingRegressor
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, mean_absolute_error, mean_squared_error, r2_score
import json
import warnings
import io

warnings.filterwarnings("ignore")
np.random.seed(42)

# ─── Color palette ───
COLORS = {
    "ETC": "#627EEA", "BTC": "#F7931A", "DOGE": "#C2A633",
    "BCH": "#8DC351", "DASH": "#008DE4",
}
CHAIN_ORDER = ["ETC", "BTC", "DOGE", "BCH", "DASH"]
UTXO_CHAINS = ["BTC", "DOGE", "BCH", "DASH"]

# ─── Pre-loaded results from real analysis ───
PRELOADED = json.loads(r'''
{
  "meta": {"dataset": "Omarrran/50k_Cryptocurrency_Transaction_Dataset_by_HNM", "chains": ["BCH","BTC","DASH","DOGE","ETC"], "total_tx": 50000},
  "rd1_fee": {"ETC": {"mean": 6.200637, "median": 1.0, "std": 41.592424, "cv": 6.7078, "skewness": 89.1592, "kurtosis": 8512.9975, "n": 10000}, "BTC": {"mean": 9.4692e-06, "median": 4.4e-06, "cv": 3.1239, "skewness": 22.0863, "kurtosis": 780.3443, "n": 9997}, "DOGE": {"mean": 0.0716, "median": 0.0104, "cv": 7.2477, "skewness": 17.3935, "kurtosis": 389.2284, "n": 9986}, "BCH": {"mean": 0.000307, "median": 3.74e-06, "cv": 15.8896, "skewness": 17.8966, "kurtosis": 320.7481, "n": 9879}, "DASH": {"mean": 6.19e-05, "median": 6e-06, "cv": 13.9415, "skewness": 71.7787, "kurtosis": 5477.6621, "n": 6422}, "levene_etc_btc": {"stat": 51.4278, "p": 0.0, "sig": true}},
  "rd2_whale": {"etc": {"threshold_99": 1075.7688, "whale_count": 100, "whale_vol_pct": 88.43, "gini": 0.9871, "freq_whale_addrs": 11, "ks_stat": 0.0733, "ks_p": 0.635, "mean": 75.7417, "median": 0.3645, "max": 91499.99}, "utxo": {"BTC": {"anomalies": 100, "whale_vol_pct": 58.53, "gini": 0.9656}, "DOGE": {"anomalies": 100, "whale_vol_pct": 98.96, "gini": 0.9984}, "BCH": {"anomalies": 100, "whale_vol_pct": 72.56, "gini": 0.9646}, "DASH": {"anomalies": 100, "whale_vol_pct": 53.37, "gini": 0.9019}}},
  "rd3_reliability": {"failure_rate": 0.0007, "failed": 7, "total": 10000, "auc": 0.4985, "features": {"gas": 0.2903, "zero_val": 0.2382, "value_etc": 0.1679, "log_val": 0.1181, "gas_price_gwei": 0.0775, "log_gp": 0.074, "high_gas": 0.0215, "hour": 0.0126}},
  "rd4_aml": {"etc": {"round_tx": 488, "round_pct": 4.88, "rapid_tx": 9060, "rapid_pct": 90.6, "equal_val_patterns": 5251, "freq_senders": 35}, "utxo": {"BTC": {"peeling": 5018, "round_outputs": 5466, "high_risk_rate": 0.4013}, "DOGE": {"peeling": 383, "round_outputs": 9334, "high_risk_rate": 0.0141}, "BCH": {"peeling": 5022, "round_outputs": 1903, "high_risk_rate": 0.1733}, "DASH": {"peeling": 4907, "round_outputs": 2796, "high_risk_rate": 0.3532}}, "total_peeling": 15330},
  "rd5_velocity": {"BCH": {"velocity": 53.27, "bh_ratio": 0.4695, "health": 44.15}, "BTC": {"velocity": 2.49, "bh_ratio": 0.0, "health": 30.0}, "DASH": {"velocity": 23.40, "bh_ratio": 0.5836, "health": 47.54}, "DOGE": {"velocity": 30977.50, "bh_ratio": 0.0, "health": 70.0}, "ETC": {"velocity": 378.52, "bh_ratio": 0.4746, "health": 44.73}},
  "rd6_mev": {"pred": {"mae": 3.5607, "rmse": 6.7611, "r2": 0.2686, "features": {"ma10": 0.493, "ma30": 0.4696, "std10": 0.0189, "l1": 0.0085, "vr": 0.0055, "min": 0.0022, "l3": 0.0014, "l5": 0.0007, "hour": 0.0003}}, "mev": {"candidates_z3": 4, "front_run": 3, "mev_rate_pct": 0.0401}},
  "rd7_arbitrage": {"coint": {"BTC-BCH": {"adf": -9.3522, "coint": true}, "BTC-DOGE": {"adf": -9.3398, "coint": true}, "ETC-DASH": {"adf": -9.5276, "coint": true}, "BTC-ETC": {"adf": -9.3351, "coint": true}, "DOGE-DASH": {"adf": -8.4527, "coint": true}}, "signals": [{"pair": "BTC-BCH", "count": 518, "avg_div": 4.3728, "max_div": 12.1035}, {"pair": "BTC-DOGE", "count": 339, "avg_div": 5.4999, "max_div": 15.0246}, {"pair": "ETC-DASH", "count": 758, "avg_div": 4.3304, "max_div": 10.2677}], "total_signals": 1615, "coint_pairs": 5},
  "rd8_privacy": {"etc": {"unique": 4232, "reused": 2351, "reuse_rate": 0.5555, "max_reuse": 2487, "entropy": 8.6111, "max_entropy": 12.0471, "norm_entropy": 0.7148}, "utxo": {"BTC": {"risk_score": 0.6272}, "DOGE": {"risk_score": 0.6356}, "BCH": {"risk_score": 0.4591}, "DASH": {"risk_score": 0.4417}}}
}
''')


# ═══════════════════════════════════════════════════════════
# VISUALIZATION BUILDERS
# ═══════════════════════════════════════════════════════════

def build_overview():
    """Build the overview summary dashboard."""
    fig = make_subplots(
        rows=2, cols=3,
        subplot_titles=("Fee CV", "Whale Vol %", "Gini Coefficient",
                        "Velocity (log)", "AML Risk Rate %", "Privacy Risk"),
        specs=[[{"type": "bar"}, {"type": "bar"}, {"type": "bar"}],
               [{"type": "bar"}, {"type": "bar"}, {"type": "bar"}]],
        horizontal_spacing=0.08, vertical_spacing=0.15,
    )

    rd1 = PRELOADED["rd1_fee"]
    chains = ["ETC", "BTC", "DOGE", "BCH", "DASH"]
    cvs = [rd1.get(c, {}).get("cv", 0) for c in chains]
    colors = [COLORS[c] for c in chains]

    fig.add_trace(go.Bar(x=chains, y=cvs, marker_color=colors, text=[f"{v:.1f}" for v in cvs],
                         textposition="outside", showlegend=False), row=1, col=1)

    rd2 = PRELOADED["rd2_whale"]
    wvols = [rd2["etc"]["whale_vol_pct"]] + [rd2["utxo"][c]["whale_vol_pct"] for c in UTXO_CHAINS]
    fig.add_trace(go.Bar(x=chains, y=wvols, marker_color=colors, text=[f"{v:.0f}%" for v in wvols],
                         textposition="outside", showlegend=False), row=1, col=2)

    ginis = [rd2["etc"]["gini"]] + [rd2["utxo"][c]["gini"] for c in UTXO_CHAINS]
    fig.add_trace(go.Bar(x=chains, y=ginis, marker_color=colors, text=[f"{v:.3f}" for v in ginis],
                         textposition="outside", showlegend=False), row=1, col=3)

    rd5 = PRELOADED["rd5_velocity"]
    vels = [rd5[c]["velocity"] for c in chains]
    fig.add_trace(go.Bar(x=chains, y=vels, marker_color=colors, text=[f"{v:.1f}" for v in vels],
                         textposition="outside", showlegend=False), row=2, col=1)
    fig.update_yaxes(type="log", row=2, col=1)

    rd4 = PRELOADED["rd4_aml"]
    risks = [0] + [rd4["utxo"][c]["high_risk_rate"] * 100 for c in UTXO_CHAINS]
    fig.add_trace(go.Bar(x=chains, y=risks, marker_color=colors, text=[f"{v:.1f}" for v in risks],
                         textposition="outside", showlegend=False), row=2, col=2)

    rd8 = PRELOADED["rd8_privacy"]
    priv = [1 - rd8["etc"]["norm_entropy"]] + [rd8["utxo"][c]["risk_score"] for c in UTXO_CHAINS]
    fig.add_trace(go.Bar(x=chains, y=priv, marker_color=colors, text=[f"{v:.3f}" for v in priv],
                         textposition="outside", showlegend=False), row=2, col=3)

    fig.update_layout(height=600, title_text="Cross-Chain Intelligence Overview — 50K Real Transactions",
                      template="plotly_white", margin=dict(t=80))
    return fig


def build_rd1_fee():
    rd1 = PRELOADED["rd1_fee"]
    chains = ["ETC", "BTC", "DOGE", "BCH", "DASH"]

    fig = make_subplots(rows=1, cols=3, subplot_titles=("CV (σ/μ)", "Skewness", "Kurtosis"),
                        horizontal_spacing=0.08)
    colors = [COLORS[c] for c in chains]

    for i, (metric, fmt) in enumerate([(lambda c: rd1[c]["cv"], ".1f"),
                                        (lambda c: rd1[c]["skewness"], ".1f"),
                                        (lambda c: rd1[c]["kurtosis"], ",.0f")], 1):
        vals = [metric(c) for c in chains]
        fig.add_trace(go.Bar(x=chains, y=vals, marker_color=colors,
                             text=[f"{v:{fmt}}" for v in vals], textposition="outside",
                             showlegend=False), row=1, col=i)

    lev = rd1["levene_etc_btc"]
    fig.update_layout(height=400, template="plotly_white",
                      title_text=f"RD1: Fee Market Efficiency — Levene W={lev['stat']:.1f}, p<0.001")
    return fig


def build_rd2_whale():
    rd2 = PRELOADED["rd2_whale"]
    chains = ["ETC", "BTC", "DOGE", "BCH", "DASH"]
    colors = [COLORS[c] for c in chains]

    fig = make_subplots(rows=1, cols=2, subplot_titles=("Whale Volume %", "Gini Coefficient"),
                        horizontal_spacing=0.1)

    wvols = [rd2["etc"]["whale_vol_pct"]] + [rd2["utxo"][c]["whale_vol_pct"] for c in UTXO_CHAINS]
    fig.add_trace(go.Bar(x=chains, y=wvols, marker_color=colors,
                         text=[f"{v:.1f}%" for v in wvols], textposition="outside",
                         showlegend=False), row=1, col=1)

    ginis = [rd2["etc"]["gini"]] + [rd2["utxo"][c]["gini"] for c in UTXO_CHAINS]
    fig.add_trace(go.Bar(x=chains, y=ginis, marker_color=colors,
                         text=[f"{v:.4f}" for v in ginis], textposition="outside",
                         showlegend=False), row=1, col=2)

    fig.update_layout(height=400, template="plotly_white",
                      title_text="RD2: Whale Concentration — Top 1% controls 53-99% of volume")
    return fig


def build_rd3_reliability():
    rd3 = PRELOADED["rd3_reliability"]
    feats = rd3["features"]
    names = list(feats.keys())
    vals = list(feats.values())

    fig = go.Figure(go.Bar(y=names, x=vals, orientation="h",
                           marker_color="#627EEA",
                           text=[f"{v:.3f}" for v in vals], textposition="outside"))
    fig.update_layout(height=400, template="plotly_white",
                      title_text=f"RD3: Reliability — {rd3['failed']}/{rd3['total']} failures (AUC={rd3['auc']:.3f})",
                      xaxis_title="Feature Importance")
    return fig


def build_rd4_aml():
    rd4 = PRELOADED["rd4_aml"]

    fig = make_subplots(rows=1, cols=2, subplot_titles=("Peeling Chains", "High-Risk Rate %"),
                        horizontal_spacing=0.12)

    utxo = UTXO_CHAINS
    peeling = [rd4["utxo"][c]["peeling"] for c in utxo]
    risk = [rd4["utxo"][c]["high_risk_rate"] * 100 for c in utxo]
    colors = [COLORS[c] for c in utxo]

    fig.add_trace(go.Bar(x=utxo, y=peeling, marker_color=colors,
                         text=peeling, textposition="outside", showlegend=False), row=1, col=1)
    fig.add_trace(go.Bar(x=utxo, y=risk, marker_color=colors,
                         text=[f"{v:.1f}%" for v in risk], textposition="outside",
                         showlegend=False), row=1, col=2)

    etc = rd4["etc"]
    fig.update_layout(height=400, template="plotly_white",
                      title_text=f"RD4: AML Detection — {rd4['total_peeling']:,} peeling chains | ETC: {etc['round_pct']}% round, {etc['freq_senders']} freq senders")
    return fig


def build_rd5_velocity():
    rd5 = PRELOADED["rd5_velocity"]
    chains = CHAIN_ORDER

    fig = make_subplots(rows=1, cols=2, subplot_titles=("Velocity (log scale)", "Health Index"),
                        horizontal_spacing=0.1)
    colors = [COLORS[c] for c in chains]

    vels = [rd5[c]["velocity"] for c in chains]
    health = [rd5[c]["health"] for c in chains]

    fig.add_trace(go.Bar(x=chains, y=vels, marker_color=colors,
                         text=[f"{v:,.1f}" for v in vels], textposition="outside",
                         showlegend=False), row=1, col=1)
    fig.update_yaxes(type="log", row=1, col=1)

    fig.add_trace(go.Bar(x=chains, y=health, marker_color=colors,
                         text=[f"{v:.1f}" for v in health], textposition="outside",
                         showlegend=False), row=1, col=2)

    fig.update_layout(height=400, template="plotly_white",
                      title_text="RD5: Payment Velocity — 12,400× gap between DOGE and BTC")
    return fig


def build_rd6_mev():
    rd6 = PRELOADED["rd6_mev"]
    pred = rd6["pred"]

    feats = pred["features"]
    names = list(feats.keys())
    vals = list(feats.values())

    fig = go.Figure(go.Bar(y=names, x=vals, orientation="h",
                           marker_color="#627EEA",
                           text=[f"{v:.4f}" for v in vals], textposition="outside"))

    mev = rd6["mev"]
    fig.update_layout(height=400, template="plotly_white",
                      title_text=f"RD6: Gas Prediction — R²={pred['r2']:.3f}, MAE={pred['mae']:.2f} Gwei | MEV candidates: {mev['candidates_z3']}",
                      xaxis_title="Feature Importance")
    return fig


def build_rd7_arbitrage():
    rd7 = PRELOADED["rd7_arbitrage"]

    fig = make_subplots(rows=1, cols=2, subplot_titles=("ADF Statistics (all < -2.86)", "Divergence Signals"),
                        horizontal_spacing=0.12)

    pairs = list(rd7["coint"].keys())
    adfs = [rd7["coint"][p]["adf"] for p in pairs]
    fig.add_trace(go.Bar(x=pairs, y=adfs, marker_color="#2ECC71",
                         text=[f"{v:.2f}" for v in adfs], textposition="outside",
                         showlegend=False), row=1, col=1)
    fig.add_hline(y=-2.86, line_dash="dash", line_color="red",
                  annotation_text="5% critical", row=1, col=1)

    sigs = rd7["signals"]
    fig.add_trace(go.Bar(x=[s["pair"] for s in sigs], y=[s["count"] for s in sigs],
                         marker_color="#3498DB",
                         text=[s["count"] for s in sigs], textposition="outside",
                         showlegend=False), row=1, col=2)

    fig.update_layout(height=400, template="plotly_white",
                      title_text=f"RD7: Cross-Chain Arbitrage — {rd7['coint_pairs']}/5 cointegrated, {rd7['total_signals']:,} signals")
    return fig


def build_rd8_privacy():
    rd8 = PRELOADED["rd8_privacy"]

    fig = make_subplots(rows=1, cols=2, subplot_titles=("ETC Address Entropy", "UTXO Privacy Risk"),
                        horizontal_spacing=0.12)

    etc = rd8["etc"]
    fig.add_trace(go.Bar(x=["Shannon H", "Max H", "Norm H"],
                         y=[etc["entropy"], etc["max_entropy"], etc["norm_entropy"]],
                         marker_color=["#627EEA", "#95A5A6", "#E74C3C"],
                         text=[f"{etc['entropy']:.2f}", f"{etc['max_entropy']:.2f}", f"{etc['norm_entropy']:.3f}"],
                         textposition="outside", showlegend=False), row=1, col=1)

    utxo = UTXO_CHAINS
    risks = [rd8["utxo"][c]["risk_score"] for c in utxo]
    fig.add_trace(go.Bar(x=utxo, y=risks, marker_color=[COLORS[c] for c in utxo],
                         text=[f"{v:.3f}" for v in risks], textposition="outside",
                         showlegend=False), row=1, col=2)

    fig.update_layout(height=400, template="plotly_white",
                      title_text=f"RD8: Privacy — ETC {etc['reuse_rate']:.1%} address reuse, max reuse {etc['max_reuse']:,}×")
    return fig


def build_radar():
    """Radar chart comparing all chains across normalized dimensions."""
    categories = ["Fee Stability", "Whale Equality", "Reliability",
                  "AML Safety", "Velocity", "Privacy"]

    rd1 = PRELOADED["rd1_fee"]
    rd2 = PRELOADED["rd2_whale"]
    rd5 = PRELOADED["rd5_velocity"]
    rd4 = PRELOADED["rd4_aml"]
    rd8 = PRELOADED["rd8_privacy"]

    fig = go.Figure()
    for chain in CHAIN_ORDER:
        # Normalize each metric to 0-1 (higher = better)
        max_cv = max(rd1[c]["cv"] for c in CHAIN_ORDER)
        fee_stab = 1 - rd1[chain]["cv"] / max_cv

        if chain == "ETC":
            whale_eq = 1 - PRELOADED["rd2_whale"]["etc"]["gini"]
            aml_safe = 1.0  # No peeling chain metric for ETC
            privacy = PRELOADED["rd8_privacy"]["etc"]["norm_entropy"]
        else:
            whale_eq = 1 - rd2["utxo"][chain]["gini"]
            aml_safe = 1 - rd4["utxo"][chain]["high_risk_rate"]
            privacy = 1 - rd8["utxo"][chain]["risk_score"]

        reliability = 1.0 if chain == "ETC" else 0.9  # ETC has receipt_status

        max_vel = max(rd5[c]["velocity"] for c in CHAIN_ORDER)
        velocity = np.log1p(rd5[chain]["velocity"]) / np.log1p(max_vel)

        vals = [fee_stab, whale_eq, reliability, aml_safe, velocity, privacy]
        vals.append(vals[0])  # Close the radar

        fig.add_trace(go.Scatterpolar(
            r=vals, theta=categories + [categories[0]],
            fill="toself", name=chain,
            line_color=COLORS[chain], opacity=0.6,
        ))

    fig.update_layout(
        polar=dict(radialaxis=dict(visible=True, range=[0, 1])),
        height=500, template="plotly_white",
        title_text="Cross-Chain Radar — Normalized Scores (higher = better)",
    )
    return fig


# ═══════════════════════════════════════════════════════════
# CUSTOM ANALYSIS ENGINE
# ═══════════════════════════════════════════════════════════

def analyze_custom_csv(file):
    """Analyze an uploaded CSV file and return results + visualization."""
    if file is None:
        return "Please upload a CSV file.", None

    try:
        df = pd.read_csv(file.name)
    except Exception as e:
        return f"Error reading CSV: {e}", None

    cols = [c.lower() for c in df.columns]
    n = len(df)
    report = []
    report.append(f"## Dataset: {n:,} rows × {len(df.columns)} columns")
    report.append(f"**Columns:** {', '.join(df.columns)}")

    # Auto-detect chain type
    is_etc = any("gas" in c for c in cols) or any("from" in c for c in cols)
    report.append(f"**Detected type:** {'Account-based (ETC-like)' if is_etc else 'UTXO-based'}")

    fig = make_subplots(rows=2, cols=2,
                        subplot_titles=("Value Distribution", "Fee Distribution",
                                        "Temporal Activity", "Concentration"),
                        horizontal_spacing=0.1, vertical_spacing=0.15)

    # Find value column
    val_col = None
    for c in df.columns:
        cl = c.lower()
        if "value" in cl or "input_btc" in cl or "input_doge" in cl or "input_bch" in cl or "input_dash" in cl:
            val_col = c
            break
    if val_col is None:
        for c in df.columns:
            if df[c].dtype in [np.float64, np.int64] and c.lower() not in ["block_number"]:
                val_col = c
                break

    if val_col:
        vals = df[val_col].dropna()
        vals_pos = vals[vals > 0]
        report.append(f"\n### Value Analysis (`{val_col}`)")
        report.append(f"- Mean: {vals.mean():.6f}")
        report.append(f"- Median: {vals.median():.6f}")
        report.append(f"- Std: {vals.std():.6f}")
        report.append(f"- CV: {vals.std()/vals.mean():.4f}" if vals.mean() != 0 else "- CV: N/A")
        report.append(f"- Skewness: {vals.skew():.4f}")
        report.append(f"- Kurtosis: {vals.kurtosis():.4f}")

        if len(vals_pos) > 10:
            sorted_v = np.sort(vals_pos.values)
            nn = len(sorted_v)
            idx = np.arange(1, nn + 1)
            gini = float((2 * np.sum(idx * sorted_v)) / (nn * np.sum(sorted_v)) - (nn + 1) / nn)
            t99 = vals_pos.quantile(0.99)
            whale_vol = vals_pos[vals_pos >= t99].sum() / vals_pos.sum() * 100
            report.append(f"- **Gini coefficient: {gini:.4f}**")
            report.append(f"- **Top 1% volume share: {whale_vol:.1f}%**")

            fig.add_trace(go.Histogram(x=np.log1p(vals_pos), nbinsx=50,
                                       marker_color="#627EEA", name="log(1+value)"), row=1, col=1)

    # Find fee column
    fee_col = None
    for c in df.columns:
        cl = c.lower()
        if "fee" in cl or "gas_price" in cl:
            fee_col = c
            break

    if fee_col:
        fees = df[fee_col].dropna()
        fees_pos = fees[fees > 0]
        report.append(f"\n### Fee Analysis (`{fee_col}`)")
        report.append(f"- Mean: {fees.mean():.8f}")
        report.append(f"- Median: {fees.median():.8f}")
        report.append(f"- CV: {fees.std()/fees.mean():.4f}" if fees.mean() != 0 else "- CV: N/A")

        if len(fees_pos) > 10:
            fig.add_trace(go.Histogram(x=np.log1p(fees_pos), nbinsx=50,
                                       marker_color="#F7931A", name="log(1+fee)"), row=1, col=2)

    # Temporal analysis
    ts_col = None
    for c in df.columns:
        if "timestamp" in c.lower():
            ts_col = c
            break

    if ts_col:
        try:
            ts = pd.to_datetime(df[ts_col], format="mixed", utc=True)
            hours = ts.dt.hour
            bh_ratio = ((hours >= 9) & (hours <= 17)).mean()
            report.append(f"\n### Temporal Analysis")
            report.append(f"- Business hours (9-17 UTC): {bh_ratio:.1%}")
            report.append(f"- Time span: {ts.min()} to {ts.max()}")

            hour_counts = hours.value_counts().sort_index()
            fig.add_trace(go.Bar(x=hour_counts.index, y=hour_counts.values,
                                 marker_color="#C2A633", name="Hourly activity"), row=2, col=1)
        except Exception:
            pass

    # Address analysis (if ETC-like)
    addr_col = None
    for c in df.columns:
        if "from" in c.lower() and "addr" in c.lower():
            addr_col = c
            break
    if addr_col is None:
        for c in df.columns:
            if c.lower().startswith("from"):
                addr_col = c
                break

    if addr_col:
        addr_counts = df[addr_col].value_counts()
        unique = len(addr_counts)
        reused = (addr_counts > 1).sum()
        report.append(f"\n### Address Analysis (`{addr_col}`)")
        report.append(f"- Unique addresses: {unique:,}")
        report.append(f"- Reuse rate: {reused/unique:.1%}")

        probs = addr_counts.values / addr_counts.values.sum()
        H = -np.sum(probs * np.log2(probs + 1e-15))
        Hmax = np.log2(unique) if unique > 1 else 1
        report.append(f"- **Shannon entropy: {H:.2f} / {Hmax:.2f} (norm: {H/Hmax:.3f})**")

        top20 = addr_counts.head(20)
        fig.add_trace(go.Bar(x=[f"Addr{i}" for i in range(len(top20))],
                             y=top20.values, marker_color="#8DC351", name="Top addresses"), row=2, col=2)

    # Receipt status (if present)
    status_col = None
    for c in df.columns:
        if "status" in c.lower() or "receipt" in c.lower():
            status_col = c
            break
    if status_col:
        sr = df[status_col].mean()
        report.append(f"\n### Reliability (`{status_col}`)")
        report.append(f"- Success rate: {sr:.4%}")
        report.append(f"- Failures: {(df[status_col]==0).sum()}")

    fig.update_layout(height=550, template="plotly_white",
                      title_text=f"Custom Analysis: {n:,} transactions",
                      showlegend=False)

    return "\n".join(report), fig


# ═══════════════════════════════════════════════════════════
# GRADIO APP
# ═══════════════════════════════════════════════════════════

SUMMARY_MD = """
# 🔗 Blockchain Intelligence Dashboard
### 8-Dimension Cross-Chain Analysis of 50,000 Real Transactions

| Dimension | Key Finding |
|-----------|-------------|
| **RD1** Fee Markets | BCH highest CV (15.89), BTC most stable (3.12). Levene p<0.001 |
| **RD2** Whales | DOGE Gini = 0.998. Top 1% controls 53-99% of volume |
| **RD3** Reliability | ETC 99.93% success. Failures unpredictable (AUC=0.499) |
| **RD4** AML | 15,330 peeling chains. BTC risk rate 40.1% |
| **RD5** Velocity | 12,400× gap: DOGE (30,978) vs BTC (2.49) |
| **RD6** Gas/MEV | R²=0.269. Moving averages = 96% importance. Only 4 MEV |
| **RD7** Arbitrage | All 5 pairs cointegrated. 1,615 divergence signals |
| **RD8** Privacy | ETC 55.6% address reuse. Norm entropy 0.715 |

**Dataset:** [Omarrran/50k_Cryptocurrency_Transaction_Dataset_by_HNM](https://huggingface.co/datasets/Omarrran/50k_Cryptocurrency_Transaction_Dataset_by_HNM)
**Chains:** ETC (account) · BTC · DOGE · BCH · DASH (UTXO)
"""

with gr.Blocks(title="Blockchain Intelligence", theme=gr.themes.Soft()) as demo:
    gr.Markdown(SUMMARY_MD)

    with gr.Tabs():
        with gr.TabItem("📊 Overview"):
            gr.Plot(value=build_overview)
            gr.Plot(value=build_radar)

        with gr.TabItem("💰 RD1: Fee Markets"):
            gr.Plot(value=build_rd1_fee)
            gr.Markdown("""
**Insight:** All chains exhibit extreme heavy-tailed fee distributions (CV 3.1–15.9).
BCH's CV of 15.89 reflects sporadic high-fee events on low base volume.
ETC's kurtosis of 8,513 means extreme outliers dominate — median is 1.0 Gwei but mean is 6.2 Gwei.
Levene's test (W=51.4, p<0.001) confirms account vs UTXO fee mechanisms produce fundamentally different profiles.
            """)

        with gr.TabItem("🐋 RD2: Whales"):
            gr.Plot(value=build_rd2_whale)
            gr.Markdown("""
**Insight:** Wealth concentration is universal and extreme. DOGE's Gini of 0.998 means virtually all
economic activity flows through whale accounts. ETC: mean 75.7 vs median 0.36 (207× ratio).
KS test shows whales DON'T transact at different times (p=0.635) — surprising for institutional actors.
Cross-chain correlations are negligible (|r|<0.1) — each chain has independent whale populations.
            """)

        with gr.TabItem("✅ RD3: Reliability"):
            gr.Plot(value=build_rd3_reliability)
            gr.Markdown("""
**Insight:** Only 7/10,000 ETC transactions failed (0.07%). Random Forest AUC of 0.499 means
failures are genuinely unpredictable from transaction features — they're essentially random events.
Gas limit and zero-value indicator dominate importance but provide no actionable signal.
            """)

        with gr.TabItem("🚨 RD4: AML"):
            gr.Plot(value=build_rd4_aml)
            gr.Markdown("""
**Insight:** BTC's 40.1% high-risk rate reflects documented use in layering operations.
DOGE has only 383 peeling chains but 93.3% round outputs — that's micro-payment culture, not laundering.
ETC's 90.6% rapid-sequence rate reflects 13-second block time, not suspicious activity.
DBSCAN found 9 clusters on DOGE vs 3 on other chains — more diverse transaction patterns.
            """)

        with gr.TabItem("⚡ RD5: Velocity"):
            gr.Plot(value=build_rd5_velocity)
            gr.Markdown("""
**Insight:** DOGE velocity of 30,978 vs BTC's 2.49 empirically confirms payment token vs store-of-value.
BTC and DOGE show 0% business-hours activity (automated/non-UTC users).
DASH has highest business-hours ratio (58.4%) consistent with merchant payment use case.
            """)

        with gr.TabItem("⛽ RD6: Gas & MEV"):
            gr.Plot(value=build_rd6_mev)
            gr.Markdown("""
**Insight:** R²=0.269 — modest but meaningful. Moving averages (ma10 + ma30) account for 96.3% of
prediction power, revealing strong mean-reversion behavior in ETC gas prices.
Only 4 MEV candidates (0.04%) — ETC's minimal DeFi activity precludes meaningful extraction.
            """)

        with gr.TabItem("📈 RD7: Arbitrage"):
            gr.Plot(value=build_rd7_arbitrage)
            gr.Markdown("""
**Insight:** All 5 pairs cointegrated despite near-zero contemporaneous correlation (|r|<0.1).
This reveals shared long-run equilibrium driven by latent factors (market sentiment).
1,615 divergence signals (16.2% of observations) exceed random-walk expectations.
BTC-DOGE maximum divergence of 15.02σ reflects the 1,300× nominal value difference.
            """)

        with gr.TabItem("🔒 RD8: Privacy"):
            gr.Plot(value=build_rd8_privacy)
            gr.Markdown("""
**Insight:** ETC privacy is severely compromised — one address appears 2,487 times.
55.6% reuse rate and normalized entropy of 0.715 mean 28.5% of address diversity is lost.
DOGE has highest UTXO risk (0.636) due to 93.3% round outputs + 97.8% single-input transactions.
DASH achieves lowest risk (0.442) despite limited PrivateSend adoption in this sample.
            """)

        with gr.TabItem("🔬 Analyze Your Data"):
            gr.Markdown("""
### Upload a CSV to analyze
Supports any blockchain transaction CSV. The tool auto-detects columns for:
values, fees, timestamps, addresses, and receipt status.
            """)
            file_input = gr.File(label="Upload CSV", file_types=[".csv"])
            analyze_btn = gr.Button("🔍 Analyze", variant="primary")
            result_md = gr.Markdown()
            result_plot = gr.Plot()
            analyze_btn.click(fn=analyze_custom_csv, inputs=[file_input],
                              outputs=[result_md, result_plot])

    gr.Markdown("""
---
*Built from real blockchain data (Nov 2024). Paper: "Comprehensive Cross-Chain Cryptocurrency Analysis:
Eight Dimensions of Blockchain Intelligence" •
[Dataset](https://huggingface.co/datasets/Omarrran/50k_Cryptocurrency_Transaction_Dataset_by_HNM)*
    """)

if __name__ == "__main__":
    demo.launch()