fix(api): sanitize NaN values recursively for JSON compliance
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Srilekha committed
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@@ -1,143 +1,141 @@
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"""
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api.py
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HIFFI Intelligence REST API Service.
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Serves multi-platform composite rankings, verified sales telemetry,
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and isolated commercial simulation models.
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HIFFI Artist Intelligence API
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Production FastAPI service for artist telemetry, rankings, and scenario simulation.
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"""
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from enum import Enum
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from pathlib import Path
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from typing import Optional
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from fastapi import FastAPI, HTTPException, Query
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from pydantic import BaseModel
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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, HTTPException, Query
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from fastapi.middleware.cors import CORSMiddleware
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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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from core.cpi_engine import CommercialPowerEngine
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# Module-level FastAPI declaration
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app = FastAPI(
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title="HIFFI Artist Intelligence API",
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description="Multi-platform analytics, sales audit telemetry, and simulation engine.",
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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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)
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DATA_PATH = Path("data/hiffi_multiplatform_500.csv")
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engine = CommercialPowerEngine()
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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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class ScopeChoice(str, Enum):
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all = "all"
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verified = "verified"
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def get_dataset() -> pd.DataFrame:
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if not DATA_PATH.exists():
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raise HTTPException(
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status_code=500,
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detail="Master dataset data/hiffi_multiplatform_500.csv not found.",
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)
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return pd.read_csv(DATA_PATH)
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class SimulationRequest(BaseModel):
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commercial_weight: float = 0.15
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mode: str = "global_redistribution" # 'matched_only' or 'global_redistribution'
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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/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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@app.get("/health", tags=["System"])
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def health_check():
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"""Returns engine heartbeat and dataset count."""
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try:
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df = get_dataset()
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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": len(df),
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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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except Exception as e:
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return {"status": "unhealthy", "error": str(e)}
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@app.get("/telemetry/coverage", tags=["Governance"])
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def telemetry_coverage():
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"""Returns data provenance tier distribution."""
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df = get_dataset()
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cspc_count = int((df.get("sales_data_status") == "VERIFIED_CSPC").sum())
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riaa_count = int((df.get("sales_data_status") == "VERIFIED_RIAA").sum())
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sea_count = int((df.get("sales_data_status") == "ESTIMATED_SEA").sum())
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unranked_count = len(df) - cspc_count - riaa_count - sea_count
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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_monitored": len(df),
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"verified_cspc": cspc_count,
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"verified_riaa": riaa_count,
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"estimated_sea": sea_count,
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"unranked": unranked_count,
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"active_coverage_percentage": round(
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((cspc_count + riaa_count + sea_count) / len(df)) * 100.0, 2
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),
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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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@app.get("/artists", tags=["Rankings"])
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def list_artists(
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scope: ScopeChoice = ScopeChoice.all,
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limit: int = Query(50, ge=1, le=500),
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vertical: Optional[str] = None,
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):
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"""Returns ranked artist list respecting data governance scopes."""
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df = get_dataset()
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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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if scope == ScopeChoice.verified:
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df = df[df["sales_data_status"].isin(["VERIFIED_CSPC", "VERIFIED_RIAA"])].copy()
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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 vertical and "subgenre" in df.columns:
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df = df[df["subgenre"].str.lower() == vertical.lower()].copy()
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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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sort_col = "hiffi_xp_published_rank" if "hiffi_xp_published_rank" in df.columns else "hiffi_rank"
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df = df.sort_values(by=sort_col, ascending=True).head(limit)
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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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return {
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"count": len(df),
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"scope": scope.value,
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"records": df.to_dict(orient="records"),
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}
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records = df.head(limit).to_dict(orient="records")
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return sanitize_nans(records)
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@app.post("/simulate", tags=["Simulation"])
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def run_simulation(req: SimulationRequest):
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"""Calculates rank shifts in-memory without mutating published rankings."""
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df = get_dataset()
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if req.mode == "matched_only":
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df = df[df["sales_data_status"] == "VERIFIED_CSPC"].copy()
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simulated_df = engine.simulate_ranking(
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df, commercial_weight=req.commercial_weight, mode=req.mode
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)
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metrics = engine.compute_simulation_metrics(simulated_df)
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output_cols = [
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"name",
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"hiffi_xp_published_rank",
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"simulated_rank",
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"simulation_rank_delta",
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"hiffi_xp_score",
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"simulated_hiffi_score",
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"sales_data_status",
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]
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render_cols = [c for c in output_cols if c in simulated_df.columns]
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return {
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"parameters": {
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"commercial_weight": req.commercial_weight,
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"mode": req.mode,
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},
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"metrics": metrics,
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"rankings": simulated_df[render_cols].to_dict(orient="records"),
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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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if __name__ == "__main__":
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import uvicorn
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uvicorn.run("api:app", host="0.0.0.0", port=8000, reload=True)
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return sanitize_nans(res)
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