feat: finalize root api.py with X-Auth-Secret auth security and clean pure sales cohort

scripts/audit_platform_gaps.py

@@ -0,0 +1,106 @@
+"""
+scripts/audit_platform_gaps.py
+Audits the exact volume and names of artists with unverified or missing
+metrics across YouTube, Spotify, Instagram, and Commercial Sales.
+"""
+
+import sys
+from pathlib import Path
+import pandas as pd
+
+ROOT_DIR = Path(__file__).resolve().parent.parent
+if str(ROOT_DIR) not in sys.path:
+    sys.path.insert(0, str(ROOT_DIR))
+
+from core.database import get_connection
+
+df500 = pd.read_csv(ROOT_DIR / "data" / "hiffi_multiplatform_500.csv")
+cohort_names = [n.strip() for n in df500["name"]]
+
+conn = get_connection()
+cur = conn.cursor()
+
+# 1. Enriched YouTube Check
+enr_file = ROOT_DIR / "enriched_artists.csv"
+if enr_file.exists():
+    enr = pd.read_csv(enr_file)
+    yt_verified = set(
+        enr[enr["subscriber_count"].notna() & (enr["subscriber_count"] > 0)]["name"]
+        .str.strip()
+        .str.lower()
+    )
+else:
+    yt_verified = set()
+
+# 2. Instagram Checkpoint Check
+chk_ig_file = ROOT_DIR / "data" / "checkpoints" / "multiplatform_checkpoints.csv"
+if chk_ig_file.exists():
+    chk_ig = pd.read_csv(chk_ig_file)
+    ig_verified = set(
+        chk_ig[chk_ig["instagram_followers"] > 0]["name"]
+        .str.strip()
+        .str.lower()
+    )
+else:
+    ig_verified = set()
+
+# 3. Spotify Monthly Listeners Check
+sp_col = "spotify_monthly_listeners" if "spotify_monthly_listeners" in df500.columns else None
+if sp_col:
+    sp_verified = set(
+        df500[df500[sp_col] > 0]["name"].str.strip().str.lower()
+    )
+else:
+    sp_verified = set()
+
+# 4. Commercial Sales Check from ChartMasters / SQLite
+# Inspect columns of commercial_sales table
+cur.execute("PRAGMA table_info(commercial_sales)")
+cs_cols = [c[1] for c in cur.fetchall()]
+
+# Sales in df500
+sales_col = None
+for col_candidate in ["audited_sales_units", "sales", "eas", "commercial_sales", "chartmasters_sales"]:
+    if col_candidate in df500.columns:
+        sales_col = col_candidate
+        break
+
+if sales_col:
+    sales_verified = set(df500[df500[sales_col] > 0]["name"].str.strip().str.lower())
+else:
+    cur.execute("""
+        SELECT a.artist_name 
+        FROM artists a 
+        JOIN commercial_sales c ON a.artist_id = c.artist_id 
+        WHERE c.audited_sales_units > 0
+    """)
+    sales_verified = set(row[0].strip().lower() for row in cur.fetchall())
+
+conn.close()
+
+# Compute missing
+yt_missing = [n for n in cohort_names if n.lower() not in yt_verified]
+sp_missing = [n for n in cohort_names if n.lower() not in sp_verified]
+ig_missing = [n for n in cohort_names if n.lower() not in ig_verified]
+sales_missing = [n for n in cohort_names if n.lower() not in sales_verified]
+
+print("=" * 70)
+print("MULTI-PLATFORM LIVE DATA GAP AUDIT (500 ARTISTS)")
+print("=" * 70)
+print(f"YouTube Channels lacking original scrape : {len(yt_missing):>3} / 500")
+print(f"Spotify Listeners lacking direct scrape  : {len(sp_missing):>3} / 500")
+print(f"Instagram Handles lacking direct scrape  : {len(ig_missing):>3} / 500")
+print(f"Commercial Sales lacking audited source  : {len(sales_missing):>3} / 500")
+print("=" * 70)
+
+print(f"\n[SAMPLE] YouTube Missing ({len(yt_missing)} total):")
+print(", ".join(yt_missing[:20]))
+
+print(f"\n[SAMPLE] Spotify Missing ({len(sp_missing)} total):")
+print(", ".join(sp_missing[:20]))
+
+print(f"\n[SAMPLE] Instagram Missing ({len(ig_missing)} total):")
+print(", ".join(ig_missing[:20]))
+
+print(f"\n[SAMPLE] Commercial Sales Missing ({len(sales_missing)} total):")
+print(", ".join(sales_missing[:20]))