737 lines
36 KiB
Python
737 lines
36 KiB
Python
"""
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app.py
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HIFFI Artist Intelligence - Multi-Platform Executive Dashboard
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Clean dark-themed design with multi-platform evidence tiers, Spotify & Instagram telemetry,
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ChartMasters record sales (CSPC/EAS), breakout velocity index, deep-dive scouting dossier,
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and experimental Commercial Power Index (CPI) simulation engine with sensitivity analysis.
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"""
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from pathlib import Path
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import pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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import streamlit as st
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from core.cpi_engine import CommercialPowerEngine
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from exporters.excel_workbook import generate_executive_sales_workbook
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st.set_page_config(
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page_title="HIFFI | Multi-Platform Intelligence",
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page_icon="⚡",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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DATA_PATH = Path("data/hiffi_multiplatform_500.csv")
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FALLBACK_DATA_PATH = Path("enriched_artists.csv")
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# Global CSS Styling
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st.markdown(
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"""
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<style>
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.block-container {
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padding-top: 1.8rem;
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padding-bottom: 2.5rem;
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padding-left: 2.5rem;
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padding-right: 2.5rem;
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max-width: 100% !important;
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}
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/* KPI Cards */
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.kpi-box {
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background: #111827;
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border: 1px solid #1f2937;
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border-radius: 12px;
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padding: 16px 20px;
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margin-bottom: 12px;
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}
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.kpi-title {
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font-size: 0.75rem;
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font-weight: 700;
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color: #9ca3af;
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text-transform: uppercase;
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letter-spacing: 0.05em;
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}
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.kpi-value {
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font-size: 1.9rem;
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font-weight: 800;
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color: #f9fafb;
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margin: 4px 0;
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}
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.kpi-subtitle {
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font-size: 0.75rem;
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color: #6b7280;
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}
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/* Telemetry Coverage Banner */
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.coverage-card {
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background: #0f172a;
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border: 1px solid #1e293b;
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border-radius: 8px;
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padding: 12px 18px;
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margin-bottom: 16px;
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display: flex;
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justify-content: space-between;
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align-items: center;
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flex-wrap: wrap;
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}
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/* Simulation Micro-Cards */
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.sim-kpi-card {
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background: #0f172a;
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border: 1px solid #334155;
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border-radius: 8px;
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padding: 10px 14px;
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margin-bottom: 10px;
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}
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.sim-kpi-title {
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font-size: 0.68rem;
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font-weight: 700;
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color: #94a3b8;
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text-transform: uppercase;
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}
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.sim-kpi-val {
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font-size: 1.25rem;
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font-weight: 800;
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margin-top: 2px;
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}
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/* Podium Cards */
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.podium-tile {
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background: #111827;
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border-radius: 12px;
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padding: 18px 20px;
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border: 1px solid #1f2937;
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height: 100%;
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}
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.podium-gold { border-top: 4px solid #f59e0b; }
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.podium-silver { border-top: 4px solid #94a3b8; }
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.podium-bronze { border-top: 4px solid #f97316; }
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/* Badges */
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.badge {
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display: inline-block;
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padding: 3px 8px;
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border-radius: 6px;
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font-size: 0.70rem;
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font-weight: 700;
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text-transform: uppercase;
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}
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.badge-tier3 { background: rgba(16, 185, 129, 0.2); color: #10b981; }
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.badge-tier2 { background: rgba(56, 189, 248, 0.2); color: #38bdf8; }
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.badge-tier1 { background: rgba(245, 158, 11, 0.2); color: #f59e0b; }
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.badge-dormant { background: rgba(239, 68, 68, 0.2); color: #ef4444; }
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.custom-divider {
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height: 1px;
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background: #1f2937;
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margin: 24px 0;
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}
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</style>
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""",
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unsafe_allow_html=True,
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)
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@st.cache_data(ttl=60)
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def load_data():
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target = DATA_PATH if DATA_PATH.exists() else FALLBACK_DATA_PATH
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if not target.exists():
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return pd.DataFrame()
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df = pd.read_csv(target)
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if "hiffi_xp_published_rank" not in df.columns:
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df["hiffi_xp_published_rank"] = df.get("hiffi_xp_rank", df.get("hiffi_rank", df.index + 1))
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if "hiffi_xp_score" not in df.columns:
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df["hiffi_xp_score"] = df.get("production_hiffi_score", df.get("hiffi_score", 0.0))
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if "platform_evidence_tier" not in df.columns:
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has_yt = df.get("production_hiffi_score", df.get("hiffi_score")).notna().astype(int)
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has_sp = df.get("spotify_monthly_listeners", pd.Series(index=df.index)).notna().astype(int)
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has_ig = df.get("instagram_followers", pd.Series(index=df.index)).notna().astype(int)
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df["platform_evidence_tier"] = has_yt + has_sp + has_ig
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numeric_cols = [
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"hiffi_xp_published_rank",
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"hiffi_xp_rank",
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"hiffi_xp_score",
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"production_hiffi_rank",
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"production_hiffi_score",
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"platform_evidence_tier",
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"breakout_velocity_index",
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"subscriber_count",
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"daily_view_velocity",
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"momentum_30d",
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"spotify_monthly_listeners",
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"spotify_daily_change",
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"instagram_followers",
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"confidence_score",
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"breakout_rank",
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"chartmasters_sales_rank",
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"cohort_sales_rank",
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"pure_album_sales",
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"physical_singles_sales",
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"digital_singles_sales",
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"streaming_eas",
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"total_eas_cspc",
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"riaa_certified_album_units",
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"riaa_certified_singles_units",
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]
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for col in numeric_cols:
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if col in df.columns:
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df[col] = pd.to_numeric(df[col], errors="coerce")
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# Commercial Power Engine
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engine = CommercialPowerEngine()
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df["commercial_power_index"] = engine.calculate_cpi(df)
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if "subgenre" not in df.columns:
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def infer_subgenre(r):
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subs = r.get("subscriber_count", 0) or 0
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bvi = r.get("breakout_velocity_index", 0) or 0
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if bvi >= 70:
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return "Breakout / Emerging"
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elif subs >= 15000000:
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return "Legends"
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else:
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return "Trap & Street"
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df["subgenre"] = df.apply(infer_subgenre, axis=1)
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return df
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df = load_data()
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engine = CommercialPowerEngine()
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def get_provenance_badge(status: str) -> str:
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"""Format status values into readable provenance labels."""
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s = str(status).strip()
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if s == "VERIFIED_CSPC":
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return "✅ VERIFIED (CSPC)"
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elif s == "VERIFIED_RIAA":
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return "🏅 VERIFIED (RIAA)"
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elif s == "ESTIMATED_SEA":
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return "📊 ESTIMATED (SEA)"
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return "⏳ PENDING_INGESTION"
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# ----------------- Sidebar Controls -----------------
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with st.sidebar:
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st.markdown("### ⚡ HIFFI Multi-Platform")
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st.caption("YouTube • Spotify • Instagram • Sales Telemetry")
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st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
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if df.empty:
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st.error("No dataset found. Run pipeline to generate master data.")
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st.stop()
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view_mode = st.radio(
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"Leaderboard Perspective:",
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[
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"🏆 Published Multi-Platform Rank",
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"🚀 Breakout Velocity (BVI)",
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"💿 Record Sales (CSPC Methodology)",
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"🧪 Commercial Simulation Engine (CPI)",
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"🔍 Full Cohort Audit Grid",
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],
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index=0,
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)
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st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
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catalog_filter = "All Monitored Artists (500)"
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if view_mode in ("💿 Record Sales (CSPC Methodology)", "🧪 Commercial Simulation Engine (CPI)"):
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st.markdown("##### 🔍 Sales Catalog Scope")
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catalog_filter = st.radio(
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"Filter Rows by Sales Telemetry:",
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["All Monitored Artists (500)", "Verified Only (321)", "Pure CSPC Only (62)"],
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index=0,
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help="Toggle between viewing the full cohort, verified certifications, or only ChartMasters studies.",
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)
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st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
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sim_weight = 0.15
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sim_mode = "matched_only"
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if view_mode == "🧪 Commercial Simulation Engine (CPI)":
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st.markdown("##### 🧪 Simulation Parameters")
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sim_mode_choice = st.radio(
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"Evaluation Scope:",
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["🎯 Matched Cohort Only", "🌐 Global Cohort (N=500, Weight Redistribution)"],
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index=1 if catalog_filter == "All Monitored Artists (500)" else 0,
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help="Matched Cohort evaluates cataloged artists in an isolated leaderboard. Global Cohort keeps all artists with normalized weights.",
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)
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sim_mode = "matched_only" if "Matched" in sim_mode_choice else "global_redistribution"
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sim_weight = st.select_slider(
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"Commercial Signal Weight:",
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options=[0.0, 0.05, 0.10, 0.15, 0.20],
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value=0.15,
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format_func=lambda x: f"{int(x*100)}% Commercial",
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help="Blends Commercial Power Index (CPI) with baseline production score.",
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)
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st.caption(f"Baseline Score Weight: **{int((1.0 - sim_weight)*100)}%**")
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st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
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st.markdown("##### 🛡️ Cohort Filters")
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all_verticals = sorted(df["subgenre"].dropna().unique().tolist())
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selected_verticals = st.multiselect("Vertical", options=all_verticals, default=all_verticals)
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tier_options = sorted(df["platform_evidence_tier"].dropna().unique().astype(int).tolist(), reverse=True)
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selected_tiers = st.multiselect("Evidence Tier", options=tier_options, default=tier_options)
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min_xp = st.slider("Min HIFFI-XP Score", 0.0, 100.0, 0.0, 5.0)
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# Filter logic
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filtered_df = df[
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(df["subgenre"].isin(selected_verticals))
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& (df["platform_evidence_tier"].isin(selected_tiers))
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& (df["hiffi_xp_score"].fillna(0.0) >= min_xp)
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].copy()
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if catalog_filter == "Pure CSPC Only (62)":
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filtered_df = filtered_df[filtered_df["sales_data_status"] == "VERIFIED_CSPC"].copy()
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elif catalog_filter == "Verified Only (321)":
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filtered_df = filtered_df[filtered_df["sales_data_status"].isin(["VERIFIED_CSPC", "VERIFIED_RIAA"])].copy()
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# Execute simulation calculations if chosen
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sim_metrics = {}
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if view_mode == "🧪 Commercial Simulation Engine (CPI)":
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filtered_df = engine.simulate_ranking(filtered_df, commercial_weight=sim_weight, mode=sim_mode)
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sim_metrics = engine.compute_simulation_metrics(filtered_df)
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with st.sidebar:
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st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
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st.markdown("##### 💾 Unified Audit Exports")
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excel_workbook_bytes = generate_executive_sales_workbook(filtered_df, commercial_weight=sim_weight, mode=sim_mode)
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st.download_button(
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"📊 Multi-Tab Audit Excel (.xlsx)",
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data=excel_workbook_bytes,
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file_name="hiffi_executive_sales_audit.xlsx",
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mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
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width="stretch",
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)
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csv_payload = filtered_df.to_csv(index=False).encode("utf-8")
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st.download_button(
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"📥 Filtered Table CSV",
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data=csv_payload,
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file_name="hiffi_multiplatform_filtered.csv",
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mime="text/csv",
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width="stretch",
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)
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st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
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st.caption("Governance: CSPC Methodology • EAS Units • Zero-Imputation Compliant")
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# ----------------- Main Header -----------------
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head_col1, head_col2 = st.columns([3, 1])
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with head_col1:
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st.markdown("<h2 style='margin-bottom: 2px;'>HIFFI Multi-Platform Intelligence Hub</h2>", unsafe_allow_html=True)
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st.markdown("<p style='color: #9ca3af; font-size: 0.92rem; margin-top: 0;'>Multi-platform composite rankings with zero-imputation policy, dynamic weight redistribution, and verified record sales.</p>", unsafe_allow_html=True)
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with head_col2:
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st.markdown(
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f"<div style='text-align: right; color: #6b7280; font-size: 0.8rem; padding-top: 10px;'>Filtered Cohort: <b>{len(filtered_df)} / {len(df)}</b> artists</div>",
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unsafe_allow_html=True,
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)
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# ----------------- Macro KPI Cards -----------------
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k1, k2, k3, k4, k5 = st.columns(5)
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with k1:
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st.markdown(f'<div class="kpi-box"><div class="kpi-title">Tracked Artists</div><div class="kpi-value">{len(df)}</div><div class="kpi-subtitle">Monitored Cohort</div></div>', unsafe_allow_html=True)
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with k2:
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t3_count = len(df[df["platform_evidence_tier"] == 3])
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st.markdown(f'<div class="kpi-box"><div class="kpi-title" style="color: #10b981;">Tier 3 (Complete)</div><div class="kpi-value" style="color: #10b981;">{t3_count}</div><div class="kpi-subtitle">YouTube + Spotify + Instagram</div></div>', unsafe_allow_html=True)
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with k3:
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sp_count = df["spotify_monthly_listeners"].notna().sum()
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st.markdown(f'<div class="kpi-box"><div class="kpi-title" style="color: #38bdf8;">Spotify Telemetry</div><div class="kpi-value" style="color: #38bdf8;">{sp_count}</div><div class="kpi-subtitle">Verified monthly listeners</div></div>', unsafe_allow_html=True)
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with k4:
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ig_count = df["instagram_followers"].notna().sum()
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st.markdown(f'<div class="kpi-box"><div class="kpi-title" style="color: #f59e0b;">Instagram Audience</div><div class="kpi-value" style="color: #f59e0b;">{ig_count}</div><div class="kpi-subtitle">Verified public followings</div></div>', unsafe_allow_html=True)
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with k5:
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cataloged_count = df["total_eas_cspc"].fillna(0).gt(0).sum()
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st.markdown(f'<div class="kpi-box"><div class="kpi-title" style="color: #ec4899;">Catalog Coverage</div><div class="kpi-value" style="color: #ec4899;">{cataloged_count}</div><div class="kpi-subtitle">100% Cohort Cataloged</div></div>', unsafe_allow_html=True)
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st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
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# ----------------- Dynamic Telemetry Coverage Banner -----------------
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if view_mode in ("💿 Record Sales (CSPC Methodology)", "🧪 Commercial Simulation Engine (CPI)"):
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verified_cspc_cnt = int((df["sales_data_status"] == "VERIFIED_CSPC").sum())
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verified_riaa_cnt = int((df["sales_data_status"] == "VERIFIED_RIAA").sum())
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estimated_sea_cnt = int((df["sales_data_status"] == "ESTIMATED_SEA").sum())
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total_active_coverage = verified_cspc_cnt + verified_riaa_cnt + estimated_sea_cnt
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coverage_pct = (total_active_coverage / len(df)) * 100.0
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st.markdown(
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f"""
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<div class="coverage-card">
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<div>
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<span style="font-weight: 800; color: #f9fafb; font-size: 0.92rem;">📡 Commercial Telemetry Coverage Progress:</span>
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<span style="color: #10b981; font-weight: 700; margin-left: 8px;">{verified_cspc_cnt} CSPC Verified</span>
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<span style="color: #64748b; margin: 0 6px;">•</span>
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<span style="color: #38bdf8; font-weight: 700;">{verified_riaa_cnt} RIAA Certified</span>
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<span style="color: #64748b; margin: 0 6px;">•</span>
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<span style="color: #a855f7; font-weight: 600;">{estimated_sea_cnt} Streaming SEA</span>
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</div>
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<div style="font-size: 0.85rem; color: #94a3b8;">
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Active Commercial Coverage: <strong style="color: #10b981;">{coverage_pct:.1f}%</strong> ({total_active_coverage}/{len(df)})
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</div>
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</div>
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""",
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unsafe_allow_html=True,
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)
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# ----------------- Standalone Module Views -----------------
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if view_mode == "💿 Record Sales (CSPC Methodology)":
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st.markdown("### 💿 Global Record Sales & Lifetime Commercial Ranking")
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st.caption("Standardized on ChartMasters CSPC, verified RIAA certifications, and video/audio streaming equivalent units.")
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sc1, sc2, sc3, sc4 = st.columns(4)
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total_sales_volume = filtered_df["total_eas_cspc"].fillna(0).sum()
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cspc_count = (filtered_df["sales_data_status"] == "VERIFIED_CSPC").sum()
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riaa_count = (filtered_df["sales_data_status"] == "VERIFIED_RIAA").sum()
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sea_count = (filtered_df["sales_data_status"] == "ESTIMATED_SEA").sum()
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with sc1:
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st.markdown(f'<div class="kpi-box"><div class="kpi-title">Tracked Catalog Volume</div><div class="kpi-value">{total_sales_volume/1e6:.1f}M</div><div class="kpi-subtitle">Total Equivalent Album Sales (EAS)</div></div>', unsafe_allow_html=True)
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with sc2:
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st.markdown(f'<div class="kpi-box"><div class="kpi-title" style="color: #10b981;">CSPC Verified</div><div class="kpi-value" style="color: #10b981;">{cspc_count}</div><div class="kpi-subtitle">ChartMasters Full Dossiers</div></div>', unsafe_allow_html=True)
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with sc3:
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st.markdown(f'<div class="kpi-box"><div class="kpi-title" style="color: #38bdf8;">RIAA Certified</div><div class="kpi-value" style="color: #38bdf8;">{riaa_count}</div><div class="kpi-subtitle">Domestic Certifications</div></div>', unsafe_allow_html=True)
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with sc4:
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st.markdown(f'<div class="kpi-box"><div class="kpi-title" style="color: #a855f7;">Streaming SEA</div><div class="kpi-value" style="color: #a855f7;">{sea_count}</div><div class="kpi-subtitle">Luminate/Billboard Conversion</div></div>', unsafe_allow_html=True)
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st.markdown("#### 🏆 All-Time Global Sales Leaderboard")
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sales_table_df = filtered_df.sort_values(by="total_eas_cspc", ascending=False).copy()
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sales_table_df["provenance_badge"] = sales_table_df["sales_data_status"].apply(get_provenance_badge)
|
|
|
|
sales_cols = [
|
|
"cohort_sales_rank",
|
|
"chartmasters_sales_rank",
|
|
"name",
|
|
"provenance_badge",
|
|
"total_eas_cspc",
|
|
"pure_album_sales",
|
|
"streaming_eas",
|
|
"physical_singles_sales",
|
|
"digital_singles_sales",
|
|
"commercial_power_index",
|
|
"hiffi_xp_published_rank",
|
|
]
|
|
render_s_cols = [c for c in sales_cols if c in sales_table_df.columns]
|
|
|
|
st.dataframe(
|
|
sales_table_df[render_s_cols].reset_index(drop=True),
|
|
width="stretch",
|
|
height=380,
|
|
column_config={
|
|
"cohort_sales_rank": st.column_config.NumberColumn("Cohort Sales Rank", format="#%d", width="small"),
|
|
"chartmasters_sales_rank": st.column_config.NumberColumn("All-Time Global Rank", format="#%d", width="small"),
|
|
"name": st.column_config.TextColumn("Artist", width="medium"),
|
|
"provenance_badge": st.column_config.TextColumn("Audit Provenance", width="medium"),
|
|
"total_eas_cspc": st.column_config.NumberColumn("Total EAS Units", format="%,d", width="medium"),
|
|
"pure_album_sales": st.column_config.NumberColumn("Pure Studio Albums", format="%,d", width="medium"),
|
|
"streaming_eas": st.column_config.NumberColumn("Streaming EAS", format="%,d", width="medium"),
|
|
"physical_singles_sales": st.column_config.NumberColumn("Physical Singles", format="%,d", width="small"),
|
|
"digital_singles_sales": st.column_config.NumberColumn("Digital Downloads", format="%,d", width="small"),
|
|
"commercial_power_index": st.column_config.ProgressColumn("CPI Score", min_value=0, max_value=100, format="%.1f", width="small"),
|
|
"hiffi_xp_published_rank": st.column_config.NumberColumn("HIFFI Base Rank", format="#%d", width="small"),
|
|
},
|
|
)
|
|
|
|
st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
|
|
|
|
c_s1, c_s2 = st.columns(2)
|
|
with c_s1:
|
|
top_sellers = sales_table_df[sales_table_df["total_eas_cspc"] > 0].head(12)
|
|
fig_format = go.Figure()
|
|
fig_format.add_trace(go.Bar(name="Traditional Albums", x=top_sellers["name"], y=top_sellers["pure_album_sales"], marker_color="#8b5cf6"))
|
|
fig_format.add_trace(go.Bar(name="Digital Singles", x=top_sellers["name"], y=top_sellers["digital_singles_sales"], marker_color="#38bdf8"))
|
|
fig_format.add_trace(go.Bar(name="Streaming EAS", x=top_sellers["name"], y=top_sellers["streaming_eas"], marker_color="#10b981"))
|
|
fig_format.update_layout(
|
|
barmode="stack",
|
|
title="Format Contribution: Pure Albums vs Downloads vs Streaming EAS",
|
|
template="plotly_dark",
|
|
paper_bgcolor="#111827",
|
|
plot_bgcolor="#111827",
|
|
font=dict(color="#9ca3af"),
|
|
height=360,
|
|
margin=dict(l=20, r=20, t=50, b=20),
|
|
legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1.0),
|
|
)
|
|
st.plotly_chart(fig_format, width="stretch")
|
|
|
|
with c_s2:
|
|
scatter_data = sales_table_df.dropna(subset=["total_eas_cspc", "hiffi_xp_score"]).copy()
|
|
fig_quad = px.scatter(
|
|
scatter_data,
|
|
x="hiffi_xp_score",
|
|
y="total_eas_cspc",
|
|
size="total_eas_cspc",
|
|
color="provenance_badge",
|
|
hover_name="name",
|
|
log_y=True,
|
|
title="Legacy vs. Velocity Quadrant (All-Time Sales vs HIFFI-XP)",
|
|
labels={"hiffi_xp_score": "Current Multi-Platform Velocity (HIFFI-XP)", "total_eas_cspc": "Total Lifetime EAS Units (Log Scale)"},
|
|
template="plotly_dark",
|
|
)
|
|
fig_quad.update_layout(
|
|
paper_bgcolor="#111827",
|
|
plot_bgcolor="#111827",
|
|
font=dict(color="#9ca3af"),
|
|
height=360,
|
|
margin=dict(l=20, r=20, t=50, b=20),
|
|
)
|
|
st.plotly_chart(fig_quad, width="stretch")
|
|
|
|
else:
|
|
top3 = filtered_df.sort_values(by="hiffi_xp_published_rank", ascending=True).head(3)
|
|
if len(top3) >= 3 and view_mode != "🧪 Commercial Simulation Engine (CPI)":
|
|
st.markdown("#### 👑 Market Leaders (Published Baseline Rankings)")
|
|
p1, p2, p3 = st.columns(3)
|
|
podium_data = [
|
|
(p1, top3.iloc[0], "🥇 PUBLISHED #1", "podium-gold", "#f59e0b"),
|
|
(p2, top3.iloc[1], "🥈 PUBLISHED #2", "podium-silver", "#94a3b8"),
|
|
(p3, top3.iloc[2], "🥉 PUBLISHED #3", "podium-bronze", "#f97316"),
|
|
]
|
|
for col, row_data, rank_label, css_class, accent_color in podium_data:
|
|
tier_val = int(row_data.get("platform_evidence_tier", 1))
|
|
sp_str = f"{int(row_data['spotify_monthly_listeners']):,}" if pd.notna(row_data.get("spotify_monthly_listeners")) else "NaN"
|
|
ig_str = f"{int(row_data['instagram_followers']):,}" if pd.notna(row_data.get("instagram_followers")) else "NaN"
|
|
with col:
|
|
st.markdown(
|
|
f"""
|
|
<div class="podium-tile {css_class}">
|
|
<div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 6px;">
|
|
<span style="font-weight: 800; color: {accent_color}; font-size: 0.85rem;">{rank_label}</span>
|
|
<span class="badge badge-tier{tier_val}">Tier {tier_val} Evidence</span>
|
|
</div>
|
|
<div style="font-size: 1.35rem; font-weight: 800; color: #ffffff; margin-bottom: 2px;">{row_data['name']}</div>
|
|
<div style="font-size: 0.8rem; color: #6b7280; margin-bottom: 12px;">{row_data.get('channel_title', '')}</div>
|
|
<div style="display: flex; justify-content: space-between; background: #0b0f17; padding: 10px 14px; border-radius: 8px;">
|
|
<div><div style="font-size: 0.68rem; color: #9ca3af;">HIFFI-XP</div><div style="font-size: 1.25rem; font-weight: 800; color: {accent_color};">{row_data['hiffi_xp_score']:.2f}</div></div>
|
|
<div><div style="font-size: 0.68rem; color: #9ca3af;">Spotify Monthly</div><div style="font-size: 1.05rem; font-weight: 700; color: #38bdf8;">{sp_str}</div></div>
|
|
<div><div style="font-size: 0.68rem; color: #9ca3af;">IG Followers</div><div style="font-size: 1.05rem; font-weight: 700; color: #e5e7eb;">{ig_str}</div></div>
|
|
</div>
|
|
</div>
|
|
""",
|
|
unsafe_allow_html=True,
|
|
)
|
|
st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
|
|
|
|
if view_mode == "🧪 Commercial Simulation Engine (CPI)":
|
|
st.markdown("#### 🧪 Simulation Impact & Decision Summary")
|
|
sm1, sm2, sm3, sm4 = st.columns(4)
|
|
|
|
g_delta = f"+{sim_metrics.get('top_gainer_delta', 0)}" if sim_metrics.get('top_gainer_delta', 0) > 0 else f"{sim_metrics.get('top_gainer_delta', 0)}"
|
|
l_delta = f"{sim_metrics.get('top_loser_delta', 0)}"
|
|
|
|
with sm1:
|
|
st.markdown(f'<div class="sim-kpi-card"><div class="sim-kpi-title">Top Gainer</div><div class="sim-kpi-val" style="color: #10b981;">{sim_metrics.get("top_gainer_name", "None")} <span style="font-size: 0.85rem;">(▲ {g_delta})</span></div></div>', unsafe_allow_html=True)
|
|
with sm2:
|
|
st.markdown(f'<div class="sim-kpi-card"><div class="sim-kpi-title">Top Loser</div><div class="sim-kpi-val" style="color: #ef4444;">{sim_metrics.get("top_loser_name", "None")} <span style="font-size: 0.85rem;">(▼ {l_delta})</span></div></div>', unsafe_allow_html=True)
|
|
with sm3:
|
|
st.markdown(f'<div class="sim-kpi-card"><div class="sim-kpi-title">Mean Absolute Shift</div><div class="sim-kpi-val" style="color: #38bdf8;">± {sim_metrics.get("mean_abs_delta", 0.0)} <span style="font-size: 0.85rem; color: #94a3b8;">positions</span></div></div>', unsafe_allow_html=True)
|
|
with sm4:
|
|
st.markdown(f'<div class="sim-kpi-card"><div class="sim-kpi-title">Simulation Scope</div><div class="sim-kpi-val" style="color: #f59e0b;">{sim_metrics.get("matched_coverage", 0)} / {len(filtered_df)} <span style="font-size: 0.85rem; color: #94a3b8;">({sim_mode})</span></div></div>', unsafe_allow_html=True)
|
|
|
|
st.caption(f"💡 **Executive Takeaway:** At **{int(sim_weight*100)}% commercial weight**, the average cataloged artist moves **±{sim_metrics.get('mean_abs_delta', 0.0)} positions**.")
|
|
st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
|
|
|
|
st.markdown(f"#### 📋 {view_mode}")
|
|
|
|
if view_mode == "🏆 Published Multi-Platform Rank":
|
|
table_df = filtered_df.sort_values(by="hiffi_xp_published_rank", ascending=True).copy()
|
|
cols = ["hiffi_xp_published_rank", "hiffi_xp_rank", "name", "platform_evidence_tier", "hiffi_xp_score", "spotify_monthly_listeners", "instagram_followers", "effective_weights", "anomaly_flags"]
|
|
elif view_mode == "🚀 Breakout Velocity (BVI)":
|
|
table_df = filtered_df[filtered_df["breakout_velocity_index"].notna()].sort_values(by="breakout_velocity_index", ascending=False).copy()
|
|
cols = ["name", "breakout_velocity_index", "hiffi_xp_published_rank", "daily_view_velocity", "momentum_30d", "subscriber_count", "subgenre"]
|
|
elif view_mode == "🧪 Commercial Simulation Engine (CPI)":
|
|
table_df = filtered_df.sort_values(by="simulated_rank", ascending=True).copy()
|
|
|
|
def format_rank_delta(val):
|
|
if pd.isna(val):
|
|
return "N/A"
|
|
ival = int(val)
|
|
if ival > 0:
|
|
return f"▲ +{ival} (Up)"
|
|
elif ival < 0:
|
|
return f"▼ {ival} (Down)"
|
|
else:
|
|
return "— 0 (Equal)"
|
|
|
|
table_df["rank_shift_indicator"] = table_df["simulation_rank_delta"].apply(format_rank_delta)
|
|
comp_rank = "baseline_matched_rank" if sim_mode == "matched_only" else "hiffi_xp_published_rank"
|
|
|
|
cols = [
|
|
"simulated_rank",
|
|
comp_rank,
|
|
"rank_shift_indicator",
|
|
"name",
|
|
"simulated_hiffi_score",
|
|
"hiffi_xp_score",
|
|
"commercial_power_index",
|
|
"total_eas_cspc",
|
|
"sales_data_status",
|
|
]
|
|
else:
|
|
table_df = filtered_df.sort_values(by="hiffi_xp_published_rank", ascending=True).copy()
|
|
cols = ["hiffi_xp_published_rank", "name", "production_hiffi_rank", "hiffi_xp_rank", "rank_delta", "platform_evidence_tier", "hiffi_xp_score", "spotify_monthly_listeners", "instagram_followers", "total_eas_cspc", "sales_data_status", "evidence_exclusions", "anomaly_flags"]
|
|
|
|
display_table = table_df.copy()
|
|
if "sales_data_status" in display_table.columns:
|
|
display_table["sales_data_status"] = display_table["sales_data_status"].apply(get_provenance_badge)
|
|
|
|
render_cols = [c for c in cols if c in display_table.columns]
|
|
|
|
st.dataframe(
|
|
display_table[render_cols].reset_index(drop=True),
|
|
width="stretch",
|
|
height=340,
|
|
column_config={
|
|
"hiffi_xp_published_rank": st.column_config.NumberColumn("Published Rank", format="%d", width="small"),
|
|
"baseline_matched_rank": st.column_config.NumberColumn("Baseline Match Rank", format="%d", width="small"),
|
|
"simulated_rank": st.column_config.NumberColumn("Simulated Rank", format="%d", width="small"),
|
|
"rank_shift_indicator": st.column_config.TextColumn("Simulated Δ (Movement)", width="medium"),
|
|
"simulated_hiffi_score": st.column_config.ProgressColumn("Simulated Score", format="%.2f", min_value=0, max_value=100, width="medium"),
|
|
"hiffi_xp_score": st.column_config.ProgressColumn("HIFFI-XP Score", format="%.2f", min_value=0, max_value=100, width="medium"),
|
|
"commercial_power_index": st.column_config.ProgressColumn("Commercial Power Index", format="%.1f", min_value=0, max_value=100, width="medium"),
|
|
"total_eas_cspc": st.column_config.NumberColumn("Total Equivalent Album Sales", format="%,d", width="medium"),
|
|
"pure_album_sales": st.column_config.NumberColumn("Traditional Album Sales", format="%,d", width="medium"),
|
|
"streaming_eas": st.column_config.NumberColumn("Streaming-Equivalent Album Sales", format="%,d", width="medium"),
|
|
"sales_data_status": st.column_config.TextColumn("Sales Audit Status", width="medium"),
|
|
},
|
|
)
|
|
|
|
st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
|
|
|
|
if view_mode == "🧪 Commercial Simulation Engine (CPI)":
|
|
c_sim1, c_sim2 = st.columns(2)
|
|
with c_sim1:
|
|
sim_delta_df = filtered_df.dropna(subset=["simulation_rank_delta"]).sort_values(by="total_eas_cspc", ascending=False).head(15).copy()
|
|
if not sim_delta_df.empty:
|
|
fig_delta = px.bar(
|
|
sim_delta_df,
|
|
x="name",
|
|
y="simulation_rank_delta",
|
|
color="simulation_rank_delta",
|
|
color_continuous_scale="RdYlGn",
|
|
title=f"Simulated Rank Movement at {int(sim_weight*100)}% Commercial Weight (Top 15 Sellers)",
|
|
labels={"simulation_rank_delta": "Rank Movement (▲ Positive / ▼ Negative)", "name": "Artist"},
|
|
template="plotly_dark",
|
|
)
|
|
fig_delta.update_layout(
|
|
paper_bgcolor="#111827",
|
|
plot_bgcolor="#111827",
|
|
font=dict(color="#9ca3af"),
|
|
margin=dict(l=20, r=20, t=50, b=20),
|
|
height=350,
|
|
)
|
|
st.plotly_chart(fig_delta, width="stretch")
|
|
|
|
with c_sim2:
|
|
sensitivity_df = engine.run_sensitivity_matrix(filtered_df, mode=sim_mode).head(15)
|
|
fig_sens = go.Figure()
|
|
for idx, r in sensitivity_df.head(6).iterrows():
|
|
fig_sens.add_trace(go.Scatter(
|
|
x=["0%", "5%", "10%", "15%", "20%"],
|
|
y=[r["rank_0pct"], r["rank_5pct"], r["rank_10pct"], r["rank_15pct"], r["rank_20pct"]],
|
|
mode="lines+markers",
|
|
name=r["name"],
|
|
))
|
|
fig_sens.update_layout(
|
|
title="Weight Sensitivity: Rank Trajectories Across Weights (Top Volatile Acts)",
|
|
yaxis=dict(autorange="reversed", title="Simulated Rank (Lower is Better)"),
|
|
xaxis=dict(title="Commercial Signal Weight (%)"),
|
|
template="plotly_dark",
|
|
paper_bgcolor="#111827",
|
|
plot_bgcolor="#111827",
|
|
font=dict(color="#9ca3af"),
|
|
margin=dict(l=20, r=20, t=50, b=20),
|
|
height=350,
|
|
)
|
|
st.plotly_chart(fig_sens, width="stretch")
|
|
else:
|
|
c_left, c_right = st.columns(2)
|
|
with c_left:
|
|
chart_df = filtered_df.dropna(subset=["spotify_monthly_listeners", "instagram_followers", "hiffi_xp_score"]).copy()
|
|
if not chart_df.empty:
|
|
fig_scatter = px.scatter(
|
|
chart_df,
|
|
x="spotify_monthly_listeners",
|
|
y="instagram_followers",
|
|
size="hiffi_xp_score",
|
|
color="platform_evidence_tier",
|
|
hover_name="name",
|
|
log_x=True,
|
|
log_y=True,
|
|
title="Spotify Monthly Listeners vs. Instagram Followers",
|
|
labels={"spotify_monthly_listeners": "Spotify Monthly Listeners (Log Scale)", "instagram_followers": "Instagram Followers (Log Scale)", "platform_evidence_tier": "Tier"},
|
|
template="plotly_dark",
|
|
)
|
|
fig_scatter.update_layout(
|
|
paper_bgcolor="#111827",
|
|
plot_bgcolor="#111827",
|
|
font=dict(color="#9ca3af"),
|
|
margin=dict(l=20, r=20, t=40, b=20),
|
|
height=320,
|
|
)
|
|
st.plotly_chart(fig_scatter, width="stretch")
|
|
with c_right:
|
|
tier_counts = filtered_df["platform_evidence_tier"].value_counts().reset_index()
|
|
tier_counts.columns = ["Tier", "Count"]
|
|
fig_pie = px.pie(tier_counts, values="Count", names="Tier", title="Evidence Tier Cohort Distribution", template="plotly_dark", color="Tier", color_discrete_map={3: "#10b981", 2: "#38bdf8", 1: "#f59e0b", 0: "#ef4444"})
|
|
fig_pie.update_layout(
|
|
paper_bgcolor="#111827",
|
|
plot_bgcolor="#111827",
|
|
font=dict(color="#9ca3af"),
|
|
margin=dict(l=20, r=20, t=40, b=20),
|
|
height=320,
|
|
)
|
|
st.plotly_chart(fig_pie, width="stretch")
|
|
|
|
# ----------------- Deep Dive Scouting Dossier -----------------
|
|
st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
|
|
st.markdown("#### 🔎 Artist Dossier")
|
|
|
|
selected_name = st.selectbox("Inspect Artist Details:", df["name"].unique(), label_visibility="collapsed")
|
|
row = df[df["name"] == selected_name].iloc[0]
|
|
|
|
d1, d2, d3, d4, d5 = st.columns(5)
|
|
with d1:
|
|
h_val = row.get("hiffi_xp_score")
|
|
st.metric("HIFFI-XP Score", f"{h_val:.2f}" if pd.notna(h_val) else "N/A")
|
|
with d2:
|
|
pub_rank = row.get("hiffi_xp_published_rank")
|
|
st.metric("Published Rank", f"#{int(pub_rank)}" if pd.notna(pub_rank) else "Unranked")
|
|
with d3:
|
|
sp_l = row.get("spotify_monthly_listeners")
|
|
st.metric("Spotify Listeners", f"{int(sp_l):,}" if pd.notna(sp_l) else "Unindexed (NaN)")
|
|
with d4:
|
|
ig_f = row.get("instagram_followers")
|
|
st.metric("Instagram Followers", f"{int(ig_f):,}" if pd.notna(ig_f) else "Unindexed (NaN)")
|
|
with d5:
|
|
sales_val = row.get("total_eas_cspc")
|
|
st.metric("Total EAS Sales", f"{int(sales_val):,}" if pd.notna(sales_val) else "Awaiting Ingestion")
|
|
|
|
st.markdown(
|
|
f"""
|
|
<div style="background: #111827; padding: 14px 18px; border-radius: 8px; border: 1px solid #1f2937; margin-top: 10px; font-size: 0.85rem;">
|
|
<div style="display: flex; gap: 20px; flex-wrap: wrap;">
|
|
<div><b>Artist:</b> {row['name']}</div>
|
|
<div><b>Evidence Tier:</b> Tier {int(row.get('platform_evidence_tier', 1))}</div>
|
|
<div><b>Global Sales Rank:</b> {f"#{int(row.get('chartmasters_sales_rank'))}" if pd.notna(row.get('chartmasters_sales_rank')) else "Unranked"}</div>
|
|
<div><b>Cohort Sales Rank:</b> {f"#{int(row.get('cohort_sales_rank'))}" if pd.notna(row.get('cohort_sales_rank')) else "Uncataloged"}</div>
|
|
<div><b>Commercial Power Index:</b> {f"{row.get('commercial_power_index'):.1f}" if pd.notna(row.get('commercial_power_index')) else "Awaiting Audit"}</div>
|
|
<div><b>Traditional Albums:</b> {f"{int(row.get('pure_album_sales')):,}" if pd.notna(row.get('pure_album_sales')) else "N/A"}</div>
|
|
<div><b>Streaming-Equivalent EAS:</b> {f"{int(row.get('streaming_eas')):,}" if pd.notna(row.get('streaming_eas')) else "N/A"}</div>
|
|
<div><b>Sales Status:</b> {get_provenance_badge(row.get('sales_data_status', ''))}</div>
|
|
<div><b>Effective Weights:</b> {row.get('effective_weights', 'N/A')}</div>
|
|
<div><b>Anomaly Flags:</b> {row.get('anomaly_flags', 'CLEAN')}</div>
|
|
</div>
|
|
</div>
|
|
""",
|
|
unsafe_allow_html=True,
|
|
) |