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hiffi-artist-intelligence/scripts/scrape_youtube_remaining_180.py
Srilekha 78a46a8dbe
HIFFI Multi-Platform CI / test (3.12) (push) Successful in 50s
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feat: finalize root api.py with X-Auth-Secret auth security and clean pure sales cohort
2026-11-04 16:49:39 +05:30

195 lines
6.1 KiB
Python

"""
scripts/scrape_youtube_remaining_180.py
Extracts verified live YouTube subscriber counts for the remaining unverified artists
by cross-referencing canonical channel identifiers from cleaned_artists_inventory.csv.
"""
import json
import re
import sys
import time
from pathlib import Path
import pandas as pd
import requests
ROOT_DIR = Path(__file__).resolve().parent.parent
if str(ROOT_DIR) not in sys.path:
sys.path.insert(0, str(ROOT_DIR))
from core.database import get_connection
HEADERS = {
"User-Agent": (
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
"AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/124.0.0.0 Safari/537.36"
),
"Accept-Language": "en-US,en;q=0.9",
}
def parse_metric(raw_text):
if not raw_text:
return 0
t = str(raw_text).upper().replace(",", "").strip()
match = re.search(r"([\d\.]+)\s*([KMB])?", t)
if not match:
return 0
val = float(match.group(1))
suffix = match.group(2)
if suffix == "K":
val *= 1e3
elif suffix == "M":
val *= 1e6
elif suffix == "B":
val *= 1e9
return int(val)
def extract_subscribers_from_html(html_text):
# Match ytInitialData embedded JSON
match = re.search(r"var ytInitialData\s*=\s*({.+?});</script>", html_text)
if not match:
match = re.search(r"window\[\"ytInitialData\"\]\s*=\s*({.+?});", html_text)
if match:
try:
data = json.loads(match.group(1))
header = data.get("header", {})
# Modern page header renderer
c4 = header.get("c4TabbedHeaderRenderer", {})
sub_text = c4.get("subscriberCountText", {}).get("simpleText", "")
if not sub_text:
runs = c4.get("subscriberCountText", {}).get("runs", [])
sub_text = "".join(r.get("text", "") for r in runs)
if sub_text:
return parse_metric(sub_text)
# Modern pageHeaderViewModel
vm = (
header.get("pageHeaderRenderer", {})
.get("content", {})
.get("pageHeaderViewModel", {})
.get("metadata", {})
.get("contentMetadataViewModel", {})
.get("metadataRows", [])
)
for row in vm:
for part in row.get("metadataParts", []):
text = part.get("text", {}).get("content", "")
if "subscribers" in text.lower():
return parse_metric(text)
except Exception:
pass
# Direct fallback regex on raw HTML body
meta_match = re.search(r'([\d\.]+[KMB]?)\s+subscribers', html_text, re.IGNORECASE)
if meta_match:
return parse_metric(meta_match.group(1))
return 0
def fetch_channel_stats(url_or_handle):
if not url_or_handle or pd.isna(url_or_handle):
return 0
url_str = str(url_or_handle).strip()
if not url_str.startswith("http"):
if url_str.startswith("UC"):
url_str = f"https://www.youtube.com/channel/{url_str}"
elif url_str.startswith("@"):
url_str = f"https://www.youtube.com/{url_str}"
else:
url_str = f"https://www.youtube.com/@{url_str}"
try:
r = requests.get(url_str, headers=HEADERS, timeout=12)
if r.status_code == 200:
return extract_subscribers_from_html(r.text)
except Exception:
pass
return 0
def main():
print("=" * 65)
print("HIFFI INTELLIGENCE: YOUTUBE 180 CANONICAL EXTRACTION")
print("=" * 65)
# 1. Load canonical mapping from inventory
inv_file = ROOT_DIR / "cleaned_artists_inventory.csv"
inv_map = {}
if inv_file.exists():
inv_df = pd.read_csv(inv_file)
# Find column candidates for artist name and youtube url/channel
name_col = "name" if "name" in inv_df.columns else "artist_name"
yt_col = None
for col in ["youtube_url", "youtube", "channel_id", "youtube_channel", "social_youtube"]:
if col in inv_df.columns:
yt_col = col
break
if yt_col:
for _, row in inv_df.iterrows():
if pd.notna(row.get(name_col)) and pd.notna(row.get(yt_col)):
inv_map[str(row[name_col]).strip().lower()] = str(row[yt_col]).strip()
# 2. Get list of artists needing verified scrape
enr_file = ROOT_DIR / "enriched_artists.csv"
enr_verified = set()
if enr_file.exists():
enr = pd.read_csv(enr_file)
enr_verified = set(
enr[enr["subscriber_count"].notna() & (enr["subscriber_count"] > 0)]["name"]
.str.strip()
.str.lower()
)
df500 = pd.read_csv(ROOT_DIR / "data" / "hiffi_multiplatform_500.csv")
cohort_names = [n.strip() for n in df500["name"]]
targets = [n for n in cohort_names if n.lower() not in enr_verified]
print(f"Targeting {len(targets)} artists for canonical YouTube resolution...")
conn = get_connection()
cur = conn.cursor()
updated = 0
for idx, name in enumerate(targets, 1):
target_url = inv_map.get(name.lower())
subs = 0
if target_url:
subs = fetch_channel_stats(target_url)
# Fallback to direct handle if inventory didn't have URL or returned 0
if subs == 0:
clean_handle = re.sub(r"[^a-zA-Z0-9]", "", name)
subs = fetch_channel_stats(f"https://www.youtube.com/@{clean_handle}")
if subs > 0:
updated += 1
cur.execute("""
UPDATE telemetry_metrics
SET subscribers_count = ?
WHERE artist_id IN (
SELECT artist_id FROM artists WHERE LOWER(artist_name) = LOWER(?)
)
""", (subs, name))
print(f"[{idx:>3}/{len(targets)}] {name:<25} -> {subs:,} subscribers")
else:
print(f"[{idx:>3}/{len(targets)}] {name:<25} -> Unresolved (Status: pending manual handle)")
time.sleep(0.4)
conn.commit()
conn.close()
print("=" * 65)
print(f"Finished. Successfully extracted and updated {updated} / {len(targets)} channels.")
if __name__ == "__main__":
main()