import re
import pandas as pd
from urllib.parse import urlparse

def clean_url(url: str) -> str:
    """Normalize URLs by stripping query params, trailing slashes, and spaces."""
    if not isinstance(url, str) or not url.strip() or url.strip().lower() in ["nan", "none", ""]:
        return ""
    url = url.strip().rstrip("/")
    if not re.match(r"^https?://", url, re.I):
        url = "https://" + url
    parsed = urlparse(url)
    return f"{parsed.scheme}://{parsed.netloc}{parsed.path}".rstrip("/")

def extract_channel_key(yt_url: str) -> str:
    """Extract unique channel ID or handle to catch cross-name channel duplicates."""
    clean = clean_url(yt_url).lower()
    if not clean:
        return ""
    parts = [p for p in urlparse(clean).path.split("/") if p]
    if not parts:
        return ""
    return parts[-1]

def deduplicate_inventory(input_path: str = "artist_inventory_artists.csv") -> pd.DataFrame:
    print(f"Loading {input_path}...")
    df = pd.read_csv(input_path)
    initial_count = len(df)
    print(f"Initial raw rows: {initial_count}")

    # 1. Clean strings and URLs
    for col in ["Name", "Location"]:
        df[col] = df[col].fillna("").astype(str).str.strip()

    for col in ["Instagram", "Youtube", "TikTok", "Facebook"]:
        df[col] = df[col].apply(clean_url)

    # 2. Generate normalized match keys
    df["clean_name"] = df["Name"].str.lower().str.replace(r"[^\w\s]", "", regex=True).str.strip()
    df["yt_key"] = df["Youtube"].apply(extract_channel_key)

    # 3. Drop exact duplicates on Name + YouTube
    df_dedup = df.drop_duplicates(subset=["clean_name", "yt_key"], keep="first").copy()
    
    # 4. Check for duplicate YouTube channels across different names
    yt_conflicts = df_dedup[df_dedup["yt_key"] != ""].duplicated(subset=["yt_key"], keep=False).sum()
    print(f"Rows with shared YouTube channels across different names: {yt_conflicts}")

    dropped = initial_count - len(df_dedup)
    print(f"Deduplicated count: {len(df_dedup)} (Dropped {dropped} duplicate rows)")

    # Clean temporary helper columns before returning
    df_dedup.drop(columns=["clean_name", "yt_key"], inplace=True)
    return df_dedup

if __name__ == "__main__":
    cleaned_df = deduplicate_inventory()
    cleaned_df.to_csv("cleaned_artists_inventory.csv", index=False)
    print("Saved cleaned data to cleaned_artists_inventory.csv")