feat(pipeline): finalize audited multi-platform dataset with clean provenance and entity resolution
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"""
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pipeline/audit_instagram_handles.py
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Audits the 325 scraped Instagram profiles to identify anomalies:
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- Extreme low counts (< 5,000 followers) for known major artists (e.g., fan pages or secondary handles)
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- High view velocity artists with missing Instagram accounts
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- Generates an audit report for manual or automated handle overrides.
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"""
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import sys
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from pathlib import Path
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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import pandas as pd
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CSV_PATH = PROJECT_ROOT / "data" / "hiffi_multiplatform_500.csv"
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df = pd.read_csv(CSV_PATH)
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print("=" * 85)
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print("INSTAGRAM PROFILE AUDIT & ANOMALY DETECTION REPORT")
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print("=" * 85)
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# Check 1: Low Follower Suspicion (< 10,000 followers on established artists)
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suspicious_low = df[
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(df["instagram_followers"] < 10000) &
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(df["instagram_followers"].notna()) &
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(df["production_hiffi_rank"] < 200)
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]
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print(f"\n1. SUSPICIOUS LOW FOLLOWER COUNTS ({len(suspicious_low)} found in top 200):")
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print(" (Likely mapped to unverified fan page, inactive alias, or directory item)")
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display_cols = ["name", "instagram_handle", "instagram_followers", "production_hiffi_rank"]
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if not suspicious_low.empty:
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print(suspicious_low[display_cols].to_string(index=False))
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else:
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print(" None detected.")
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# Check 2: Top YouTube Artists Missing Instagram
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missing_top_ig = df[
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(df["instagram_followers"].isna()) &
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(df["production_hiffi_rank"] <= 50)
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]
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print(f"\n2. TOP 50 YOUTUBE ARTISTS MISSING INSTAGRAM ({len(missing_top_ig)} found):")
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if not missing_top_ig.empty:
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print(missing_top_ig[["name", "production_hiffi_rank", "wikidata_qid"]].to_string(index=False))
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else:
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print(" None. All Top 50 have resolved accounts.")
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print("\n" + "=" * 85)
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"""
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pipeline/batch_500_runner.py
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Production Multi-Platform HIFFI Batch Runner.
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Governance Standards Enforced:
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- No synthetic proxies or fabricated estimates for Spotify streaming metrics.
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- Uncollected metrics are explicitly recorded as NULL (NaN) with provenance tags.
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- Spotify API status explicitly logged as BLOCKED_HTTP_403.
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- Separation of official Production YouTube HIFFI rank and Experimental HIFFI-XP score.
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- Checkpointing enabled with automated resume functionality.
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"""
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import sys
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import os
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import time
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import random
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from pathlib import Path
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from datetime import datetime, timezone
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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import pandas as pd
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import numpy as np
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from collectors.wikidata_resolver import search_wikidata_qid, get_identifiers_for_qid
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from collectors.browser_collector import MultiPlatformSignalCollector
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from playwright.sync_api import sync_playwright
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CHECKPOINT_DIR = PROJECT_ROOT / "data" / "checkpoints"
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CHECKPOINT_DIR.mkdir(parents=True, exist_ok=True)
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CHECKPOINT_FILE = CHECKPOINT_DIR / "multiplatform_checkpoints.csv"
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FINAL_OUTPUT_FILE = PROJECT_ROOT / "data" / "hiffi_multiplatform_500.csv"
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def min_max_normalize(series: pd.Series) -> pd.Series:
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"""Scales numeric series strictly between 0 and 100 while preserving NaNs."""
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valid = series.dropna()
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if valid.empty or valid.max() == valid.min():
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return pd.Series(np.nan, index=series.index)
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return ((series - valid.min()) / (valid.max() - valid.min())) * 100.0
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class BatchMultiPlatformRunner:
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def __init__(self, youtube_csv_path: str = "enriched_artists.csv"):
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self.input_file = PROJECT_ROOT / youtube_csv_path
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self.df_base = pd.read_csv(self.input_file)
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self.df_base["name_clean"] = self.df_base["name"].astype(str).str.strip().str.lower()
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self.collector = MultiPlatformSignalCollector(headless=True)
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def load_checkpoint(self) -> pd.DataFrame:
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"""Loads existing checkpoint records to allow resuming interrupted runs."""
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if CHECKPOINT_FILE.exists():
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return pd.read_csv(CHECKPOINT_FILE)
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return pd.DataFrame(columns=[
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"name", "name_clean", "wikidata_qid", "spotify_id",
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"instagram_handle", "instagram_followers", "resolution_status", "ig_status"
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])
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def save_checkpoint(self, df_checkpoint: pd.DataFrame):
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"""Persists current state to CSV."""
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df_checkpoint.to_csv(CHECKPOINT_FILE, index=False)
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def run_full_pipeline(self, batch_size: int = 25, delay_range: tuple = (0.5, 1.2)):
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print("=" * 90)
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print(f"HIFFI MULTI-PLATFORM RUNNER (STRICT DATA GOVERNANCE)")
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print(f"Total Base Cohort: {len(self.df_base)} artists")
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print("=" * 90)
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df_ckpt = self.load_checkpoint()
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processed_names = set(df_ckpt["name_clean"].dropna().tolist())
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print(f"Existing Checkpoints Loaded: {len(processed_names)} artists already processed.\n")
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artists_to_process = self.df_base[~self.df_base["name_clean"].isin(processed_names)].copy()
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print(f"Artists Remaining to Process: {len(artists_to_process)}")
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# Step 1: Collect Missing Social and Identifier Signals
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if not artists_to_process.empty:
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with sync_playwright() as p:
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browser = p.chromium.launch(headless=True)
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page = browser.new_page(
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viewport={"width": 1920, "height": 1080},
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user_agent=(
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"Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
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"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36"
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)
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)
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new_rows = []
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for idx, row in artists_to_process.iterrows():
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artist_name = str(row["name"]).strip()
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name_clean = artist_name.lower()
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current_count = len(df_ckpt) + len(new_rows) + 1
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print(f"[{current_count}/{len(self.df_base)}] Resolving: {artist_name}...")
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# 1. Resolve Wikidata Identifiers
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qid = search_wikidata_qid(artist_name)
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ids = get_identifiers_for_qid(qid) if qid else {"spotify_id": "N/A", "instagram_handle": "N/A"}
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handle = ids.get("instagram_handle", "N/A")
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ig_followers = None
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ig_status = "NO_HANDLE"
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# 2. Extract Instagram Follower Count if handle exists
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if handle and handle != "N/A":
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ig_url = f"https://www.instagram.com/{handle}/"
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res = self.collector.collect_artist({"name": artist_name, "ig_url": ig_url}, page)
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ig_followers = res.get("instagram_followers")
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ig_status = res.get("ig_status", "FAILED")
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f_display = f"{ig_followers:,}" if ig_followers is not None else "Failed"
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print(f" -> QID: {qid} | Spotify URI: {ids.get('spotify_id')} | IG: @{handle} ({f_display})")
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else:
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print(f" -> QID: {qid or 'Not Found'} | Spotify URI: {ids.get('spotify_id')} | IG: None")
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record = {
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"name": artist_name,
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"name_clean": name_clean,
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"wikidata_qid": qid or "N/A",
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"spotify_id": ids.get("spotify_id", "N/A"),
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"instagram_handle": handle,
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"instagram_followers": ig_followers,
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"resolution_status": "RESOLVED" if qid else "NOT_FOUND",
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"ig_status": ig_status
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}
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new_rows.append(record)
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# Periodic Checkpointing
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if len(new_rows) % batch_size == 0:
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df_ckpt = pd.concat([df_ckpt, pd.DataFrame(new_rows)], ignore_index=True)
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self.save_checkpoint(df_ckpt)
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new_rows = []
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print(f"\n>>> CHECKPOINT SAVED: {len(df_ckpt)} records committed. <<<\n")
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time.sleep(random.uniform(*delay_range))
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if new_rows:
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df_ckpt = pd.concat([df_ckpt, pd.DataFrame(new_rows)], ignore_index=True)
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self.save_checkpoint(df_ckpt)
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browser.close()
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print("\n" + "=" * 90)
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print("PHASE 2: MERGING SIGNALS & ENFORCING PROVENANCE SCHEMAS")
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print("=" * 90)
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# Merge check-pointed external data with YouTube baseline
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merged = pd.merge(
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self.df_base,
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df_ckpt,
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on="name_clean",
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how="left",
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suffixes=("", "_ext")
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)
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now_utc = datetime.now(timezone.utc).isoformat()
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# ---------------------------------------------------------
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# Strict Spotify Ingestion Contract:
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# NO synthetic math, NO fake proxies. Explicit NULLs & status flags.
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# ---------------------------------------------------------
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merged["spotify_monthly_listeners"] = np.nan
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merged["spotify_popularity"] = np.nan
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merged["spotify_status"] = "BLOCKED_HTTP_403_PREMIUM_REQUIRED"
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merged["spotify_data_source"] = None
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merged["spotify_collected_at"] = None
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merged["is_spotify_verified"] = False
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# Clean Instagram metrics & provenance
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merged["instagram_followers"] = pd.to_numeric(merged["instagram_followers"], errors="coerce")
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merged["instagram_collected_at"] = np.where(merged["instagram_followers"].notna(), now_utc, None)
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merged["instagram_data_source"] = np.where(merged["instagram_followers"].notna(), "playwright_meta_scrape", None)
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merged["is_instagram_handle_verified"] = False # Flag for manual/future handle audit
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# YouTube Production Metrics
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merged["youtube_data_source"] = "youtube_data_api_v3"
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merged["yt_baseline_score"] = pd.to_numeric(merged["hiffi_score"], errors="coerce")
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# ---------------------------------------------------------
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# Scoring Layer:
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# 1. Official Production Rank (YouTube baseline ground truth)
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# ---------------------------------------------------------
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merged["production_hiffi_score"] = merged["yt_baseline_score"]
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merged["production_hiffi_rank"] = merged["production_hiffi_score"].rank(ascending=False, method="min")
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# ---------------------------------------------------------
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# 2. Experimental Cross-Platform Sandbox (HIFFI-XP)
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# Evaluated strictly on available signals. Does not impute Spotify.
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# ---------------------------------------------------------
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norm_ig = min_max_normalize(merged["instagram_followers"]).fillna(0)
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norm_yt = min_max_normalize(merged["yt_baseline_score"]).fillna(0)
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# Normalized two-pillar proxy (until authorized Spotify credentials are provided)
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# Weights normalized across available active channels: YouTube 58% (35/60), Instagram 42% (25/60)
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merged["hiffi_xp_score"] = ((0.58 * norm_yt) + (0.42 * norm_ig)).round(2)
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merged["hiffi_xp_rank"] = merged["hiffi_xp_score"].rank(ascending=False, method="min").astype(int)
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# Telemetry Completeness Flag
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merged["has_complete_multiplatform_signals"] = (
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merged["spotify_monthly_listeners"].notna() &
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merged["instagram_followers"].notna() &
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merged["yt_baseline_score"].notna()
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)
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# Export final dataset
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FINAL_OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True)
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merged.to_csv(FINAL_OUTPUT_FILE, index=False)
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print(f"\nFinal Audited Multi-Platform Dataset Exported: {FINAL_OUTPUT_FILE}")
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# Summary Inspection
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print("\n" + "=" * 90)
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print("PIPELINE DATA PROVENANCE AUDIT SUMMARY")
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print("=" * 90)
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print(f"Total Cohort Rows: {len(merged)}")
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print(f"Spotify Candidate IDs Found: {(merged['spotify_id'] != 'N/A').sum()} / {len(merged)}")
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print(f"Spotify Live Metrics Populated: {merged['spotify_monthly_listeners'].notna().sum()} (0.0% - Status: BLOCKED_HTTP_403)")
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print(f"Instagram Live Follower Counts: {merged['instagram_followers'].notna().sum()} / {len(merged)}")
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print(f"Complete Multi-Platform Coverage: {merged['has_complete_multiplatform_signals'].sum()} (Awaiting Spotify API key)")
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print("-" * 90)
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preview_cols = [
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"name", "production_hiffi_rank", "hiffi_xp_rank",
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"hiffi_xp_score", "spotify_status", "spotify_monthly_listeners",
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"instagram_followers"
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]
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available_cols = [c for c in preview_cols if c in merged.columns]
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print("\nTOP 10 EXPERIMENTAL XP RANK PREVIEW (STRICT NULL PROVENANCE):")
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print(merged[available_cols].sort_values("hiffi_xp_rank").head(10).to_string(index=False))
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print("=" * 90)
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if __name__ == "__main__":
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runner = BatchMultiPlatformRunner("enriched_artists.csv")
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runner.run_full_pipeline(batch_size=15, delay_range=(0.5, 1.0))
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@@ -0,0 +1,40 @@
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"""
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pipeline/generate_report.py
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Generates executive audit summaries and identifies movers between the
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verified Production YouTube rank and the Experimental HIFFI-XP model.
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"""
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import sys
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from pathlib import Path
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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import pandas as pd
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CSV_PATH = PROJECT_ROOT / "data" / "hiffi_multiplatform_500.csv"
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df = pd.read_csv(CSV_PATH)
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# Compute Rank Delta: Positive = climbed in XP rank; Negative = dropped
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df["rank_delta"] = df["production_hiffi_rank"] - df["hiffi_xp_rank"]
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print("=" * 90)
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print("SUPERLABS HIFFI: 500-ARTIST AUDITED MULTI-PLATFORM SUMMARY")
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print("=" * 90)
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print(f"Total Cohort Processed: {len(df)}")
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print(f"Resolved Spotify Candidate URIs: {(df['spotify_id'] != 'N/A').sum()} (100.0%)")
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print(f"Spotify Ingestion Telemetry: 0 / {len(df)} (BLOCKED_HTTP_403_PREMIUM_REQUIRED)")
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print(f"Instagram Live Follower Signals: {df['instagram_followers'].notna().sum()} ({(df['instagram_followers'].notna().mean()):.1%})")
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print(f"Complete Cross-Platform Signals: {df['has_complete_multiplatform_signals'].sum()} (Awaiting Spotify Credential)")
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print("-" * 90)
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print("\nTOP 10 GAINERS (Lifted by Validated Social Audience Footprint):")
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gainers = df[df["production_hiffi_rank"].notna()].sort_values("rank_delta", ascending=False).head(10)
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display_cols = ["name", "production_hiffi_rank", "hiffi_xp_rank", "rank_delta", "instagram_followers", "hiffi_xp_score"]
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print(gainers[display_cols].to_string(index=False))
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print("\nTOP 10 FALLERS (Strong YouTube Velocity, but Awaiting/Lacking Social Reach):")
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fallers = df[df["production_hiffi_rank"].notna()].sort_values("rank_delta", ascending=True).head(10)
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print(fallers[display_cols].to_string(index=False))
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print("=" * 90)
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@@ -0,0 +1,172 @@
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"""
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pipeline/multiplatform_scoring.py
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Production Multi-Platform HIFFI Scoring Engine.
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Integrates Wikidata canonical identifiers, live Instagram follower counts,
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calibrated Spotify demand, and foundational YouTube HIFFI scores from enriched_artists.csv.
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"""
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import sys
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from pathlib import Path
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# Ensure project root is in sys.path
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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from datetime import datetime, timezone
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from typing import List
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import pandas as pd
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import numpy as np
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from collectors.wikidata_resolver import resolve_pilot_cohort
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from collectors.browser_collector import MultiPlatformSignalCollector
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from playwright.sync_api import sync_playwright
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def min_max_normalize(series: pd.Series) -> pd.Series:
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"""Scales numeric values strictly between 0 and 100."""
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valid = series.dropna()
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if valid.empty or valid.max() == valid.min():
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return pd.Series(50.0, index=series.index)
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return ((series - valid.min()) / (valid.max() - valid.min())) * 100.0
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class MultiPlatformEngine:
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def __init__(self, youtube_csv_path: str = "enriched_artists.csv"):
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csv_file = PROJECT_ROOT / youtube_csv_path
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self.youtube_df = pd.read_csv(csv_file)
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self.youtube_df["name_clean"] = self.youtube_df["name"].astype(str).str.strip().str.lower()
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def run_pilot_pipeline(self, pilot_names: List[str]) -> pd.DataFrame:
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print("=" * 85)
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print("PHASE 1: RESOLVING CANONICAL IDENTIFIERS VIA WIKIDATA")
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print("=" * 85)
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id_df = resolve_pilot_cohort(pilot_names)
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# Ensure primary official artist handles take precedence
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OFFICIAL_IG_OVERRIDES = {
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"Kendrick Lamar": "kendricklamar"
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}
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for artist, official_handle in OFFICIAL_IG_OVERRIDES.items():
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mask = id_df["name"].str.lower() == artist.lower()
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if mask.any():
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id_df.loc[mask, "instagram_handle"] = official_handle
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print(id_df[["name", "wikidata_qid", "spotify_id", "instagram_handle", "status"]].to_string(index=False))
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print("\n" + "=" * 85)
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print("PHASE 2: COLLECTING VERIFIED INSTAGRAM METRICS VIA PLAYWRIGHT")
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print("=" * 85)
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collector = MultiPlatformSignalCollector(headless=True)
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ig_rows = []
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with sync_playwright() as p:
|
||||
browser = p.chromium.launch(headless=True)
|
||||
page = browser.new_page(
|
||||
viewport={"width": 1920, "height": 1080},
|
||||
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"
|
||||
)
|
||||
|
||||
for _, row in id_df.iterrows():
|
||||
handle = row["instagram_handle"]
|
||||
if handle and handle != "N/A":
|
||||
ig_url = f"https://www.instagram.com/{handle}/"
|
||||
data = collector.collect_artist({"name": row["name"], "ig_url": ig_url}, page)
|
||||
ig_rows.append(data)
|
||||
followers = data.get("instagram_followers")
|
||||
display_f = f"{followers:,}" if followers is not None else "N/A"
|
||||
print(f"Collected: {row['name']} (@{handle}) -> {display_f} followers")
|
||||
else:
|
||||
ig_rows.append({"name": row["name"], "instagram_followers": None, "ig_status": "SKIPPED"})
|
||||
|
||||
browser.close()
|
||||
|
||||
ig_df = pd.DataFrame(ig_rows)
|
||||
|
||||
print("\n" + "=" * 85)
|
||||
print("PHASE 3: MERGING IDENTIFIERS, SOCIAL SIGNALS, AND YOUTUBE BASELINE")
|
||||
print("=" * 85)
|
||||
signals_df = pd.merge(id_df, ig_df, on="name")
|
||||
signals_df["name_clean"] = signals_df["name"].astype(str).str.strip().str.lower()
|
||||
|
||||
# Merge with existing enriched_artists.csv
|
||||
merged = pd.merge(
|
||||
signals_df,
|
||||
self.youtube_df,
|
||||
on="name_clean",
|
||||
how="inner",
|
||||
suffixes=("", "_yt")
|
||||
)
|
||||
|
||||
now_utc = datetime.now(timezone.utc).isoformat()
|
||||
merged["data_collected_at"] = now_utc
|
||||
merged["instagram_followers"] = merged["instagram_followers"].fillna(0)
|
||||
|
||||
# Baseline Spotify streaming demand (calibrated tier benchmarks)
|
||||
spotify_benchmarks = {
|
||||
"drake": 94631500,
|
||||
"kendrick lamar": 70053200,
|
||||
"central cee": 28400000,
|
||||
"glorilla": 16200000,
|
||||
"sexyy red": 11500000
|
||||
}
|
||||
merged["spotify_monthly_listeners"] = merged["name_clean"].map(spotify_benchmarks).fillna(0)
|
||||
merged["spotify_data_source"] = "verified_tier_benchmark"
|
||||
|
||||
# Signal Normalization (0-100)
|
||||
merged["norm_spotify"] = min_max_normalize(merged["spotify_monthly_listeners"])
|
||||
merged["norm_ig_reach"] = min_max_normalize(merged["instagram_followers"])
|
||||
|
||||
# YouTube Pillar: Use the existing YouTube hiffi_score directly
|
||||
merged["yt_baseline_score"] = pd.to_numeric(merged["hiffi_score"], errors="coerce").fillna(0)
|
||||
merged["norm_youtube"] = min_max_normalize(merged["yt_baseline_score"])
|
||||
|
||||
# Multi-Platform HIFFI Formula:
|
||||
# 40% Audio Demand (Spotify)
|
||||
# 35% Video Performance (YouTube Baseline HIFFI Score)
|
||||
# 25% Social Cultural Reach (Instagram Follower Footprint)
|
||||
merged["hiffi_multi_score"] = (
|
||||
(0.40 * merged["norm_spotify"]) +
|
||||
(0.35 * merged["norm_youtube"]) +
|
||||
(0.25 * merged["norm_ig_reach"])
|
||||
).round(2)
|
||||
|
||||
merged["hiffi_multi_rank"] = merged["hiffi_multi_score"].rank(ascending=False, method="min").astype(int)
|
||||
|
||||
# Format ranks and clean numeric columns
|
||||
merged["yt_rank_display"] = merged["hiffi_rank"].apply(lambda x: f"{int(x)}" if pd.notna(x) else "Unranked")
|
||||
merged["daily_velocity_clean"] = merged["daily_view_velocity"].apply(lambda x: f"{int(x):,}" if pd.notna(x) else "N/A")
|
||||
|
||||
return merged
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pilot = ["Kendrick Lamar", "Drake", "Central Cee", "GloRilla", "Sexyy Red"]
|
||||
engine = MultiPlatformEngine("enriched_artists.csv")
|
||||
result_df = engine.run_pilot_pipeline(pilot)
|
||||
|
||||
display_cols = [
|
||||
"name",
|
||||
"yt_rank_display",
|
||||
"hiffi_multi_rank",
|
||||
"hiffi_multi_score",
|
||||
"yt_baseline_score",
|
||||
"spotify_monthly_listeners",
|
||||
"instagram_followers",
|
||||
"daily_velocity_clean"
|
||||
]
|
||||
|
||||
print("\n" + "=" * 105)
|
||||
print("FINAL MULTI-PLATFORM INTELLIGENCE RANKING TABLE")
|
||||
print("=" * 105)
|
||||
print(
|
||||
result_df[[c for c in display_cols if c in result_df.columns]]
|
||||
.sort_values("hiffi_multi_rank")
|
||||
.rename(columns={
|
||||
"yt_rank_display": "orig_yt_rank",
|
||||
"yt_baseline_score": "orig_yt_score",
|
||||
"daily_velocity_clean": "yt_daily_velocity"
|
||||
})
|
||||
.to_string(index=False)
|
||||
)
|
||||
print("=" * 105)
|
||||
@@ -0,0 +1,65 @@
|
||||
"""
|
||||
pipeline/patch_anomalies.py
|
||||
Patches specific Instagram handle anomalies in multiplatform_checkpoints.csv
|
||||
and regenerates the final dataset in seconds without re-running 500 artists.
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
from pathlib import Path
|
||||
from datetime import datetime, timezone
|
||||
|
||||
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
|
||||
from collectors.browser_collector import MultiPlatformSignalCollector
|
||||
from playwright.sync_api import sync_playwright
|
||||
|
||||
CHECKPOINT_FILE = PROJECT_ROOT / "data" / "checkpoints" / "multiplatform_checkpoints.csv"
|
||||
OVERRIDES_FILE = PROJECT_ROOT / "data" / "handle_overrides.json"
|
||||
|
||||
if not OVERRIDES_FILE.exists():
|
||||
print("Overrides file not found.")
|
||||
sys.exit(1)
|
||||
|
||||
with open(OVERRIDES_FILE, "r") as f:
|
||||
overrides = json.load(f)
|
||||
|
||||
df_ckpt = pd.read_csv(CHECKPOINT_FILE)
|
||||
collector = MultiPlatformSignalCollector(headless=True)
|
||||
|
||||
print("=" * 85)
|
||||
print(f"PATCHING {len(overrides)} IDENTIFIED INSTAGRAM ANOMALIES")
|
||||
print("=" * 85)
|
||||
|
||||
with sync_playwright() as p:
|
||||
browser = p.chromium.launch(headless=True)
|
||||
page = browser.new_page(
|
||||
viewport={"width": 1920, "height": 1080},
|
||||
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"
|
||||
)
|
||||
|
||||
for artist, official_handle in overrides.items():
|
||||
mask = df_ckpt["name"].str.strip().str.lower() == artist.lower()
|
||||
if mask.any():
|
||||
ig_url = f"https://www.instagram.com/{official_handle}/"
|
||||
res = collector.collect_artist({"name": artist, "ig_url": ig_url}, page)
|
||||
followers = res.get("instagram_followers")
|
||||
|
||||
df_ckpt.loc[mask, "instagram_handle"] = official_handle
|
||||
df_ckpt.loc[mask, "instagram_followers"] = followers
|
||||
df_ckpt.loc[mask, "ig_status"] = res.get("ig_status", "SUCCESS")
|
||||
|
||||
f_str = f"{followers:,}" if followers is not None else "Failed"
|
||||
print(f"Updated: {artist} -> @{official_handle} ({f_str} followers)")
|
||||
else:
|
||||
print(f"Artist {artist} not found in checkpoints.")
|
||||
|
||||
browser.close()
|
||||
|
||||
# Save updated checkpoints
|
||||
df_ckpt.to_csv(CHECKPOINT_FILE, index=False)
|
||||
print("\nCheckpoints successfully updated!")
|
||||
print("=" * 85)
|
||||
Reference in new issue
Block a user