580 lines
19 KiB
Python
580 lines
19 KiB
Python
"""
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app.py
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HIFFI Artist Intelligence - Executive Dashboard
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Clean dark-themed design with aligned dataframes, responsive cards, and scouting dossier.
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"""
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import io
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import os
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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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st.set_page_config(
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page_title="HIFFI | Artist 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 = "enriched_artists.csv"
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# Global CSS Polish
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st.markdown(
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"""
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<style>
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/* Global Container Spacing */
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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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/* 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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/* Health 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-active { background: rgba(16, 185, 129, 0.15); color: #10b981; }
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.badge-evergreen { background: rgba(56, 189, 248, 0.15); color: #38bdf8; }
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.badge-dormant { background: rgba(239, 68, 68, 0.15); color: #ef4444; }
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/* Clean Section Dividers */
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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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if not os.path.exists(DATA_PATH):
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return pd.DataFrame()
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df = pd.read_csv(DATA_PATH)
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numeric_cols = [
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"hiffi_score",
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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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"confidence_score",
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"hiffi_rank",
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"breakout_rank",
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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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# Fallback assignment for subgenre if not yet computed in pipeline
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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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def to_excel_bytes(dataframe: pd.DataFrame) -> bytes:
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"""Converts dataframe to downloadable in-memory Excel spreadsheet."""
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output = io.BytesIO()
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with pd.ExcelWriter(output, engine="openpyxl") as writer:
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dataframe.to_excel(writer, index=False, sheet_name="HIFFI_Cohort")
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return output.getvalue()
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df = load_data()
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# ----------------- Sidebar Controls -----------------
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with st.sidebar:
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st.markdown("### ⚡ HIFFI Intelligence")
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st.caption("Hip-Hop Market Scoring & Breakout Detection")
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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(f"'{DATA_PATH}' not found. Please run 'python main.py' first!")
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st.stop()
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view_mode = st.radio(
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"Leaderboard Perspective:",
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["🏆 Overall HIFFI Rank", "🚀 Breakout Velocity (BVI)", "🔍 Master Audit Grid"],
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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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st.markdown("##### 🛡️ Filters")
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# Subgenre / Vertical Filter
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all_verticals = sorted(df["subgenre"].dropna().unique().tolist())
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target_presets = ["Legends", "Trap & Street", "UK Drill", "Lyricists", "Breakout / Emerging"]
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vertical_choices = [v for v in target_presets if v in all_verticals] + [v for v in all_verticals if v not in target_presets]
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selected_verticals = st.multiselect(
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"Hip-Hop Vertical",
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options=vertical_choices,
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default=vertical_choices,
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)
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available_statuses = df["health_status"].dropna().unique().tolist()
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selected_statuses = st.multiselect(
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"Channel Status",
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options=available_statuses,
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default=available_statuses,
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)
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min_conf = st.slider(
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"Min Confidence",
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min_value=0.0,
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max_value=1.0,
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value=0.50,
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step=0.05,
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)
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# Filter logic
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filtered_df = df[
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(df["health_status"].isin(selected_statuses))
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& (df["subgenre"].isin(selected_verticals))
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& (df["confidence_score"] >= min_conf)
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].copy()
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# Sidebar Data Export Buttons (Filters applied)
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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("##### 💾 Export Filtered Cohort")
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col_csv, col_xlsx = st.columns(2)
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csv_payload = filtered_df.to_csv(index=False).encode("utf-8")
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col_csv.download_button(
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label="📥 CSV",
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data=csv_payload,
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file_name="hiffi_filtered_cohort.csv",
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mime="text/csv",
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use_container_width=True,
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)
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excel_payload = to_excel_bytes(filtered_df)
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col_xlsx.download_button(
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label="📊 Excel",
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data=excel_payload,
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file_name="hiffi_filtered_cohort.xlsx",
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mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
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use_container_width=True,
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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("Momentum (35%) • Velocity (30%) • Reach (20%) • Cadence (15%)")
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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 Artist Intelligence Hub</h2>", unsafe_allow_html=True)
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st.markdown("<p style='color: #9ca3af; font-size: 0.92rem; margin-top: 0;'>Cross-platform tracking of catalog sustainability and audience breakout velocity.</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;'>Active 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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# ----------------- KPI Cards Row -----------------
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k1, k2, k3, k4 = st.columns(4)
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with k1:
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st.markdown(
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f"""
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<div class="kpi-box">
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<div class="kpi-title">Tracked Artists</div>
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<div class="kpi-value">{len(df)}</div>
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<div class="kpi-subtitle">Monitored pipeline size</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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with k2:
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active_count = len(df[df["health_status"] == "ACTIVE_HEALTHY"])
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st.markdown(
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f"""
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<div class="kpi-box">
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<div class="kpi-title" style="color: #10b981;">Active Healthy</div>
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<div class="kpi-value" style="color: #10b981;">{active_count}</div>
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<div class="kpi-subtitle">Recent uploads & steady traction</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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with k3:
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evergreen_count = len(df[df["health_status"] == "CATALOG_EVERGREEN"])
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st.markdown(
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f"""
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<div class="kpi-box">
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<div class="kpi-title" style="color: #38bdf8;">Catalog Evergreen</div>
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<div class="kpi-value" style="color: #38bdf8;">{evergreen_count}</div>
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<div class="kpi-subtitle">Strong passive catalog streams</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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with k4:
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zombies = len(df[df["health_status"] == "DORMANT_CATALOG"])
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st.markdown(
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f"""
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<div class="kpi-box">
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<div class="kpi-title" style="color: #ef4444;">Dormant / Excluded</div>
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<div class="kpi-value" style="color: #ef4444;">{zombies}</div>
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<div class="kpi-subtitle">Disqualified (<NA> Rank)</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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st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
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# ----------------- Top 3 Leaders Podium -----------------
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top3 = (
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filtered_df[filtered_df["is_rank_eligible"] == True]
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.sort_values(by="hiffi_rank", ascending=True)
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.head(3)
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)
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if len(top3) >= 3:
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st.markdown("#### 👑 Market Leaders")
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p1, p2, p3 = st.columns(3)
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podium_data = [
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(p1, top3.iloc[0], "🥇 RANK #1", "podium-gold", "#f59e0b"),
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(p2, top3.iloc[1], "🥈 RANK #2", "podium-silver", "#94a3b8"),
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(p3, top3.iloc[2], "🥉 RANK #3", "podium-bronze", "#f97316"),
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]
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for col, row_data, rank_label, css_class, accent_color in podium_data:
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badge_cls = (
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"badge-active"
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if row_data["health_status"] == "ACTIVE_HEALTHY"
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else "badge-evergreen"
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)
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bvi_str = f"{row_data['breakout_velocity_index']:.1f}" if pd.notna(row_data.get("breakout_velocity_index")) else "N/A"
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vel_str = f"{int(row_data['daily_view_velocity']):,}" if pd.notna(row_data.get("daily_view_velocity")) else "N/A"
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with col:
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st.markdown(
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f"""
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<div class="podium-tile {css_class}">
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<div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 6px;">
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<span style="font-weight: 800; color: {accent_color}; font-size: 0.85rem;">{rank_label}</span>
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<span class="badge {badge_cls}">{row_data['health_status'].replace('_', ' ')}</span>
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</div>
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<div style="font-size: 1.35rem; font-weight: 800; color: #ffffff; margin-bottom: 2px;">{row_data['name']}</div>
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<div style="font-size: 0.8rem; color: #6b7280; margin-bottom: 12px;">{row_data.get('channel_title', '')}</div>
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<div style="display: flex; justify-content: space-between; background: #0b0f17; padding: 10px 14px; border-radius: 8px;">
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<div>
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<div style="font-size: 0.68rem; color: #9ca3af; text-transform: uppercase;">HIFFI Score</div>
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<div style="font-size: 1.25rem; font-weight: 800; color: {accent_color};">{row_data['hiffi_score']:.1f}</div>
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</div>
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<div>
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<div style="font-size: 0.68rem; color: #9ca3af; text-transform: uppercase;">Breakout Index</div>
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<div style="font-size: 1.25rem; font-weight: 800; color: #a855f7;">{bvi_str}</div>
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</div>
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<div>
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<div style="font-size: 0.68rem; color: #9ca3af; text-transform: uppercase;">Daily Views</div>
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<div style="font-size: 1.05rem; font-weight: 700; color: #e5e7eb;">{vel_str}</div>
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</div>
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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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st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
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# ----------------- Leaderboard Grid -----------------
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st.markdown(f"#### 📋 {view_mode}")
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if view_mode == "🏆 Overall HIFFI Rank":
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table_df = (
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filtered_df[filtered_df["is_rank_eligible"] == True]
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.sort_values(by="hiffi_rank", ascending=True)
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.copy()
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)
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cols = [
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"hiffi_rank",
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"name",
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"subgenre",
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"hiffi_score",
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"breakout_rank",
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"breakout_velocity_index",
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"daily_view_velocity",
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"momentum_30d",
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"health_status",
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]
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elif view_mode == "🚀 Breakout Velocity (BVI)":
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table_df = (
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filtered_df[
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(filtered_df["is_rank_eligible"] == True)
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& (filtered_df["breakout_rank"].notna())
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]
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.sort_values(by="breakout_rank", ascending=True)
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.copy()
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)
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cols = [
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"breakout_rank",
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"name",
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"subgenre",
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"breakout_velocity_index",
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"hiffi_rank",
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"hiffi_score",
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"daily_view_velocity",
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"subscriber_count",
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"health_status",
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]
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else:
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table_df = filtered_df.sort_values(by=["is_rank_eligible", "hiffi_score"], ascending=[False, False]).copy()
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cols = [
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"name",
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"subgenre",
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"hiffi_score",
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"breakout_velocity_index",
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"daily_view_velocity",
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"subscriber_count",
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"health_status",
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"confidence_score",
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"is_rank_eligible",
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]
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# Keep only columns that exist in DataFrame to prevent KeyError
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render_cols = [c for c in cols if c in table_df.columns]
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st.dataframe(
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table_df[render_cols].reset_index(drop=True),
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use_container_width=True,
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height=290,
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column_config={
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"hiffi_rank": st.column_config.NumberColumn("Rank", format="%d", width="small"),
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"breakout_rank": st.column_config.NumberColumn("BVI Rank", format="%d", width="small"),
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"name": st.column_config.TextColumn("Artist", width="medium"),
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"subgenre": st.column_config.TextColumn("Vertical", width="medium"),
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"hiffi_score": st.column_config.ProgressColumn(
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"HIFFI Score",
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format="%.1f",
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min_value=0,
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max_value=100,
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width="medium",
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),
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"breakout_velocity_index": st.column_config.ProgressColumn(
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"Breakout (BVI)",
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format="%.1f",
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min_value=0,
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max_value=100,
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width="medium",
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),
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"daily_view_velocity": st.column_config.NumberColumn(
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"Daily Views / Day",
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format="%,d",
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width="medium",
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),
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"momentum_30d": st.column_config.NumberColumn(
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"30d Momentum",
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format="%.1f%%",
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width="small",
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),
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"subscriber_count": st.column_config.NumberColumn(
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"Subscribers",
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format="%,d",
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width="medium",
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),
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"health_status": st.column_config.TextColumn("Status", width="medium"),
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"confidence_score": st.column_config.NumberColumn("Confidence", format="%.2f", width="small"),
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"is_rank_eligible": st.column_config.CheckboxColumn("Eligible", width="small"),
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},
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)
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st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
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# ----------------- Visual Insights Charts -----------------
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st.markdown("#### 📊 Market Landscape Visualizer")
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c_left, c_right = st.columns(2)
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with c_left:
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scatter_df = filtered_df[filtered_df["is_rank_eligible"] == True].copy()
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fig_scatter = px.scatter(
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scatter_df,
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x="daily_view_velocity",
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y="momentum_30d",
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size="subscriber_count",
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color="health_status",
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color_discrete_map={
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"ACTIVE_HEALTHY": "#10b981",
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"CATALOG_EVERGREEN": "#38bdf8",
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"DORMANT_CATALOG": "#ef4444",
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},
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hover_name="name",
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hover_data={"daily_view_velocity": ":,d", "momentum_30d": ":.1f%"},
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log_x=True,
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title="View Velocity vs. 30-Day Momentum",
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labels={
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"daily_view_velocity": "Daily Views / Day (Log Scale)",
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"momentum_30d": "30-Day Growth (%)",
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"health_status": "Vitality",
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},
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template="plotly_dark",
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)
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fig_scatter.update_layout(
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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, use_container_width=True)
|
|
|
|
with c_right:
|
|
bvi_df = (
|
|
filtered_df[filtered_df["breakout_velocity_index"].notna()]
|
|
.sort_values(by="breakout_velocity_index", ascending=True)
|
|
)
|
|
fig_bar = px.bar(
|
|
bvi_df,
|
|
x="breakout_velocity_index",
|
|
y="name",
|
|
orientation="h",
|
|
color="breakout_velocity_index",
|
|
color_continuous_scale="Purples",
|
|
title="Breakout Velocity Index (BVI)",
|
|
labels={"breakout_velocity_index": "BVI Score", "name": "Artist"},
|
|
template="plotly_dark",
|
|
)
|
|
fig_bar.update_layout(
|
|
paper_bgcolor="#111827",
|
|
plot_bgcolor="#111827",
|
|
font=dict(color="#9ca3af"),
|
|
coloraxis_showscale=False,
|
|
margin=dict(l=20, r=20, t=40, b=20),
|
|
height=320,
|
|
)
|
|
st.plotly_chart(fig_bar, use_container_width=True)
|
|
|
|
st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
|
|
|
|
# ----------------- Deep Dive Scouting Dossier -----------------
|
|
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]
|
|
|
|
# Resolve YouTube channel direct link
|
|
yt_url = str(row.get("youtube_url", "")).strip()
|
|
yt_id = str(row.get("channel_id", row.get("youtube_channel_id", ""))).strip()
|
|
|
|
if (not yt_url or yt_url == "nan") and yt_id and yt_id != "nan":
|
|
if yt_id.startswith("UC"):
|
|
yt_url = f"https://www.youtube.com/channel/{yt_id}"
|
|
elif yt_id.startswith("@"):
|
|
yt_url = f"https://www.youtube.com/{yt_id}"
|
|
else:
|
|
yt_url = f"https://www.youtube.com/@{yt_id}"
|
|
|
|
d1, d2, d3, d4 = st.columns(4)
|
|
|
|
with d1:
|
|
h_val = row.get("hiffi_score")
|
|
st.metric("HIFFI Score", f"{h_val:.1f}" if pd.notna(h_val) else "N/A")
|
|
with d2:
|
|
b_val = row.get("breakout_velocity_index")
|
|
st.metric("Breakout Index (BVI)", f"{b_val:.1f}" if pd.notna(b_val) else "N/A")
|
|
with d3:
|
|
s_val = row.get("subscriber_count")
|
|
st.metric("Subscribers", f"{int(s_val):,}" if pd.notna(s_val) else "N/A")
|
|
with d4:
|
|
conf = row.get("confidence_score", 0.0)
|
|
tier = row.get("confidence_tier", "Standard")
|
|
st.metric("Entity Confidence", f"{conf * 100:.0f}%", delta=tier, delta_color="normal")
|
|
|
|
badge_cls = (
|
|
"badge-active"
|
|
if row.get("health_status") == "ACTIVE_HEALTHY"
|
|
else "badge-evergreen"
|
|
if row.get("health_status") == "CATALOG_EVERGREEN"
|
|
else "badge-dormant"
|
|
)
|
|
|
|
# Dossier info box with integrated Official YouTube Button
|
|
st.markdown(
|
|
f"""
|
|
<div style="background: #111827; padding: 14px 18px; border-radius: 8px; border: 1px solid #1f2937; margin-top: 10px; display: flex; justify-content: space-between; align-items: center; font-size: 0.85rem;">
|
|
<div style="display: flex; gap: 24px; align-items: center;">
|
|
<div><b>Artist:</b> {row['name']}</div>
|
|
<div><b>Vertical:</b> <span style="color: #a855f7;">{row.get('subgenre', 'N/A')}</span></div>
|
|
<div><b>Channel:</b> {row.get('channel_title', 'Unknown')}</div>
|
|
<div><b>Status:</b> <span class="badge {badge_cls}">{row.get('health_status', 'UNKNOWN')}</span></div>
|
|
<div><b>Zombie Flag:</b> {'⚠️ Yes' if row.get('is_zombie') else '✅ Clean'}</div>
|
|
<div><b>Eligible for Rank:</b> {'✅ Yes' if row.get('is_rank_eligible') else '❌ Disqualified'}</div>
|
|
</div>
|
|
</div>
|
|
""",
|
|
unsafe_allow_html=True,
|
|
)
|
|
|
|
st.write("")
|
|
if yt_url and yt_url != "nan":
|
|
st.link_button("▶ Open Official YouTube Channel", yt_url, use_container_width=False)
|
|
else:
|
|
st.caption("⚠️ No verified YouTube channel link available for this artist.") |