feat: finalize root api.py with X-Auth-Secret auth security and clean pure sales cohort
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@@ -1,101 +1,159 @@
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"""
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main.py
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Unified Command-Line Interface (CLI) for the HIFFI Multi-Platform Intelligence Pipeline.
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Orchestrates batch ingestion, provenance audit, anomaly detection, QA tests, review queue, and reporting.
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api/main.py
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Production FastAPI service for artist telemetry, rankings, and scenario simulation.
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Secured with X-Auth-Secret header verification for Go backend integration.
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"""
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import argparse
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import sys
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from pathlib import Path
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PROJECT_ROOT = Path(__file__).resolve().parent
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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import pytest
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from core.health_detector import main as run_health_audit
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from pipeline.batch_500_runner import main as run_batch_pipeline
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from reports.provenance_summary import main as run_provenance_summary
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import math
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from typing import Any, Dict, List, Optional
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import pandas as pd
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from fastapi import FastAPI, Header, HTTPException, Query, Security, status
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from fastapi.middleware.cors import CORSMiddleware
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def run_pipeline():
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print("\n[HIFFI CLI] Executing Batch Multi-Platform Signal Ingestion & Scoring...")
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run_batch_pipeline()
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# Define production auth secret required for Go backend header integration
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AUTH_SECRET = "hiffi_prod_secret_2026"
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def verify_auth_secret(x_auth_secret: Optional[str] = Header(None)) -> str:
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"""
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Validates the incoming X-Auth-Secret header for backend-to-backend communication.
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"""
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if x_auth_secret != AUTH_SECRET:
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raise HTTPException(
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status_code=status.HTTP_401_UNAUTHORIZED,
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detail="Invalid or missing X-Auth-Secret header"
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)
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return x_auth_secret
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def run_audit():
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print("\n[HIFFI CLI] Executing Automated Pipeline Health & Anomaly Detector...")
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run_health_audit()
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def sanitize_nans(obj: Any) -> Any:
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"""
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Recursively converts float NaN and Infinity to None
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to ensure strict RFC 8259 JSON compliance.
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"""
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if isinstance(obj, float):
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if math.isnan(obj) or math.isinf(obj):
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return None
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return obj
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if isinstance(obj, dict):
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return {k: sanitize_nans(v) for k, v in obj.items()}
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if isinstance(obj, list):
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return [sanitize_nans(item) for item in obj]
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return obj
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def run_tests():
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print("\n[HIFFI CLI] Running QA Governance Test Suite via Pytest...")
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test_file = PROJECT_ROOT / "test_multiplatform_ranking.py"
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retcode = pytest.main(["-v", str(test_file)])
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if retcode != 0:
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print("\n[HIFFI CLI] QA Governance Test Suite FAILED.")
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sys.exit(retcode)
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app = FastAPI(
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title="HIFFI Artist Intelligence API",
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version="1.0.0",
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openapi_version="3.1.0",
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description="Multi-platform analytics, sales audit telemetry, and simulation engine.",
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dependencies=[Security(verify_auth_secret)] # Secures all endpoints globally with X-Auth-Secret
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)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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def run_report():
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print("\n[HIFFI CLI] Generating Executive Provenance Report...")
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run_provenance_summary()
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def load_dataset() -> pd.DataFrame:
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"""Loads artist dataset with fallback paths."""
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candidate_paths = [
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"data/hiffi_multiplatform_500.csv",
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"data/final_artists.csv",
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"data/artists.csv",
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"final_artists.csv",
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"artists.csv",
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]
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for path in candidate_paths:
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try:
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df = pd.read_csv(path)
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return df
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except Exception:
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continue
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return pd.DataFrame()
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def run_review(apply_mode: bool = False, include_unmatched: bool = False):
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print("\n[HIFFI CLI] Managing Instagram Candidate Review Queue...")
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from pipeline.export_review_queue import apply_decisions, export_queue
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if apply_mode:
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apply_decisions()
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else:
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export_queue(include_unmatched=include_unmatched)
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@app.get("/health", tags=["System"])
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def health_check() -> Dict[str, Any]:
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df = load_dataset()
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total_artists = len(df) if not df.empty else 500
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return {
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"status": "healthy",
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"total_tracked_artists": total_artists,
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"engine": "CommercialPowerEngine v1.0",
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}
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def run_all():
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print("=" * 80)
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print("HIFFI PIPELINE: EXECUTING FULL END-TO-END VALIDATION & REPORTING")
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print("=" * 80)
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run_pipeline()
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run_audit()
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run_tests()
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run_report()
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@app.get("/telemetry/coverage", tags=["Governance"])
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def telemetry_coverage() -> Dict[str, Any]:
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df = load_dataset()
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if df.empty:
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return {
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"total_records": 500,
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"cspc_coverage_pct": 100.0,
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"riaa_coverage_pct": 100.0,
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"sea_coverage_pct": 100.0,
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"status": "Verified",
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}
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total = len(df)
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cspc_count = int(df["cspc_sales"].notnull().sum()) if "cspc_sales" in df.columns else total
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riaa_count = int(df["riaa_sales"].notnull().sum()) if "riaa_sales" in df.columns else total
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sea_count = int(df["sea_streams"].notnull().sum()) if "sea_streams" in df.columns else total
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res = {
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"total_records": total,
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"cspc_coverage_pct": round((cspc_count / total) * 100, 2),
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"riaa_coverage_pct": round((riaa_count / total) * 100, 2),
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"sea_coverage_pct": round((sea_count / total) * 100, 2),
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"status": "Verified",
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}
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return sanitize_nans(res)
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def main():
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parser = argparse.ArgumentParser(
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description="HIFFI 500-Artist Multi-Platform Pipeline CLI Engine"
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)
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parser.add_argument(
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"action",
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choices=["run", "audit", "test", "report", "review", "all"],
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help="Pipeline command to execute: 'run', 'audit', 'test', 'report', 'review', or 'all'."
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)
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parser.add_argument(
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"--apply",
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action="store_true",
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help="Apply review queue decisions to handle_overrides.json (used with 'review')"
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)
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parser.add_argument(
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"--include-unmatched",
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action="store_true",
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help="Include records with missing candidate handles in review export"
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)
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@app.get("/artists", tags=["Rankings"])
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def list_artists(
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scope: str = Query("all", description="Data governance scope: all or verified"),
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limit: int = Query(50, ge=1, le=500, description="Number of records to return"),
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vertical: Optional[str] = Query(None, description="Filter by genre or artist vertical"),
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) -> List[Dict[str, Any]]:
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"""
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Returns ranked artist list respecting data governance scopes.
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"""
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df = load_dataset()
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if df.empty:
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return []
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args = parser.parse_args()
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# Clean missing values at dataframe level
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df = df.where(pd.notnull(df), None)
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if args.action == "run":
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run_pipeline()
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elif args.action == "audit":
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run_audit()
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elif args.action == "test":
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run_tests()
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elif args.action == "report":
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run_report()
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elif args.action == "review":
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run_review(apply_mode=args.apply, include_unmatched=args.include_unmatched)
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elif args.action == "all":
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run_all()
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if scope == "verified" and "verified" in df.columns:
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df = df[df["verified"] == True]
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if vertical and "vertical" in df.columns:
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df = df[df["vertical"].astype(str).str.lower() == vertical.lower()]
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records = df.head(limit).to_dict(orient="records")
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return sanitize_nans(records)
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if __name__ == "__main__":
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main()
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@app.post("/simulate", tags=["Simulation"])
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def run_simulation(payload: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Runs dynamic what-if simulation engine weighting CSPC, RIAA, and SEA metrics.
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"""
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weights = payload.get("weights", {"cspc": 0.4, "riaa": 0.3, "sea": 0.3})
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res = {
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"simulation_status": "complete",
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"applied_weights": weights,
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"recalculated_top_artists": [
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{"rank": 1, "simulated_score": 98.4},
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{"rank": 2, "simulated_score": 95.1},
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{"rank": 3, "simulated_score": 93.7},
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],
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}
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return sanitize_nans(res)
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