"""
scripts/audit_real_faces.py
Scans all 500 cohort images in data/artist_avatars/ and flags cartoons,
drawings, text signatures, and album covers using image entropy and edge density.
Outputs the total count and saves data/face_audit_report.csv.
"""

import sqlite3
import csv
import re
import math
from pathlib import Path
from PIL import Image, ImageStat, ImageFilter

BASE_DIR = Path(__file__).resolve().parent.parent
DB_PATH = BASE_DIR / "data" / "hiffi_catalog.db"
AVATAR_DIR = BASE_DIR / "data" / "artist_avatars"
COHORT_CSV = BASE_DIR / "data" / "hiffi_multiplatform_500.csv"
FALLBACK_CSV = BASE_DIR / "cleaned_artists_inventory.csv"
REPORT_CSV = BASE_DIR / "data" / "face_audit_report.csv"

def clean_file_slug(name: str) -> str:
    cleaned = re.sub(r'[^a-zA-Z0-9_-]', '_', name.strip().lower())
    return re.sub(r'_+', '_', cleaned).strip('_')

def load_cohort_names() -> list:
    target_csv = COHORT_CSV if COHORT_CSV.exists() else FALLBACK_CSV
    names = []
    if target_csv.exists():
        with open(target_csv, "r", encoding="utf-8-sig") as f:
            reader = csv.DictReader(f)
            for row in reader:
                name = row.get("artist_name") or row.get("Artist") or row.get("name")
                if name and name.strip():
                    names.append(name.strip())
    return names

def calculate_entropy(img: Image.Image) -> float:
    """Calculates image entropy (information richness). Drawings and logos have very low entropy."""
    histogram = img.histogram()
    total = sum(histogram)
    if total == 0:
        return 0.0
    entropy = 0.0
    for count in histogram:
        if count > 0:
            p = count / total
            entropy -= p * math.log2(p)
    return entropy

def inspect_image_type(image_path: Path):
    """
    Analyzes whether an image is a real human photo or a cartoon/drawing/writing.
    Returns: (is_real_face, reason)
    """
    if not image_path.exists():
        return False, "Image file missing"

    if image_path.stat().st_size < 3000:
        return False, "File too small / corrupt"

    try:
        with Image.open(image_path) as img:
            img_rgb = img.convert("RGB")
            width, height = img_rgb.size

            if width < 100 or height < 100:
                return False, f"Low resolution ({width}x{height})"

            stat = ImageStat.Stat(img_rgb)
            avg_stddev = sum(stat.stddev) / len(stat.stddev)
            
            # 1. Flat text signatures / black backgrounds (e.g. 100k Track)
            if avg_stddev < 18.0:
                return False, "Flat graphic or signature writing"

            # 2. Check image entropy (cartoons & clip-art have low color entropy)
            entropy = calculate_entropy(img_rgb)
            if entropy < 5.8:
                return False, "Cartoon / vector drawing / quote art"

            # 3. Check extreme edge contrast typical of comic art / posters
            edges = img_rgb.filter(ImageFilter.FIND_EDGES)
            edge_stat = ImageStat.Stat(edges)
            mean_edge = sum(edge_stat.mean) / len(edge_stat.mean)
            
            if mean_edge > 75.0 and avg_stddev < 40.0:
                return False, "High-contrast poster / graphic illustration"

            return True, "Verified Real Photo"
    except Exception as e:
        return False, f"Error inspecting image: {e}"

def main():
    print("\n" + "="*60)
    print("      HIFFI 500 ARTIST FACE AUDIT REPORT")
    print("="*60 + "\n")

    cohort_names = load_cohort_names()
    conn = sqlite3.connect(DB_PATH)
    conn.row_factory = sqlite3.Row
    cursor = conn.cursor()

    if cohort_names:
        placeholders = ",".join("?" for _ in cohort_names)
        cursor.execute(f"SELECT artist_id, artist_name, image_url FROM artists WHERE artist_name IN ({placeholders})", cohort_names)
    else:
        cursor.execute("SELECT artist_id, artist_name, image_url FROM artists LIMIT 500")

    records = cursor.fetchall()
    conn.close()

    total_artists = len(records)
    real_faces = 0
    non_faces = []

    for r in records:
        artist_id = str(r["artist_id"])
        artist_name = r["artist_name"]
        slug = clean_file_slug(artist_name)

        # Locate local image file
        local_file = None
        for ext in [".jpg", ".png", ".webp", ".jpeg"]:
            cand = AVATAR_DIR / f"{slug}_{artist_id}{ext}"
            if cand.exists():
                local_file = cand
                break

        is_face, reason = inspect_image_type(local_file) if local_file else (False, "No file downloaded")

        if is_face:
            real_faces += 1
        else:
            non_faces.append({
                "artist_id": artist_id,
                "artist_name": artist_name,
                "issue_reason": reason,
                "file_path": str(local_file.name) if local_file else "None"
            })

    # Save CSV report
    with open(REPORT_CSV, "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=["artist_id", "artist_name", "issue_reason", "file_path"])
        writer.writeheader()
        writer.writerows(non_faces)

    print(f"Total Artists Analyzed:      {total_artists}")
    print(f"Confirmed Real Human Faces:  {real_faces}  ({(real_faces/total_artists)*100:.1f}%)")
    print(f"Non-Face / Cartoons / Art:   {len(non_faces)}  ({(len(non_faces)/total_artists)*100:.1f}%)")
    print(f"\nDetailed CSV report saved to: {REPORT_CSV}")
    print("-" * 60)
    print("First 15 Artists Flagged Without Real Faces:")
    for item in non_faces[:15]:
        print(f"  - {item['artist_name']}: {item['issue_reason']}")
    print("=" * 60)

if __name__ == "__main__":
    main()