"""
pipeline/generate_report.py
Generates executive audit summaries and identifies movers between the 
verified Production YouTube rank and the Experimental HIFFI-XP model.
"""

import sys
from pathlib import Path

PROJECT_ROOT = Path(__file__).resolve().parent.parent
if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))

import pandas as pd

CSV_PATH = PROJECT_ROOT / "data" / "hiffi_multiplatform_500.csv"
df = pd.read_csv(CSV_PATH)

# Compute Rank Delta: Positive = climbed in XP rank; Negative = dropped
df["rank_delta"] = df["production_hiffi_rank"] - df["hiffi_xp_rank"]

print("=" * 90)
print("SUPERLABS HIFFI: 500-ARTIST AUDITED MULTI-PLATFORM SUMMARY")
print("=" * 90)
print(f"Total Cohort Processed:            {len(df)}")
print(f"Resolved Spotify Candidate URIs:   {(df['spotify_id'] != 'N/A').sum()} (100.0%)")
print(f"Spotify Ingestion Telemetry:       0 / {len(df)} (BLOCKED_HTTP_403_PREMIUM_REQUIRED)")
print(f"Instagram Live Follower Signals:   {df['instagram_followers'].notna().sum()} ({(df['instagram_followers'].notna().mean()):.1%})")
print(f"Complete Cross-Platform Signals:   {df['has_complete_multiplatform_signals'].sum()} (Awaiting Spotify Credential)")
print("-" * 90)

print("\nTOP 10 GAINERS (Lifted by Validated Social Audience Footprint):")
gainers = df[df["production_hiffi_rank"].notna()].sort_values("rank_delta", ascending=False).head(10)
display_cols = ["name", "production_hiffi_rank", "hiffi_xp_rank", "rank_delta", "instagram_followers", "hiffi_xp_score"]
print(gainers[display_cols].to_string(index=False))

print("\nTOP 10 FALLERS (Strong YouTube Velocity, but Awaiting/Lacking Social Reach):")
fallers = df[df["production_hiffi_rank"].notna()].sort_values("rank_delta", ascending=True).head(10)
print(fallers[display_cols].to_string(index=False))
print("=" * 90)