398 lines
No EOL
16 KiB
Python
398 lines
No EOL
16 KiB
Python
#!/usr/bin/env python3
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"""
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WhatsApp Enrichment Using Names - Search for WhatsApp Business Accounts
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This script enriches existing person profiles with WhatsApp-related information
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using ONLY publicly available data found through name-based searches.
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NO data fabrication or hallucination - all enrichment is based on real search results.
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"""
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import json
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import os
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import re
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import hashlib
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Dict, List, Optional, Any
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class WhatsAppNameEnricher:
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"""Enrich person profiles with WhatsApp data using name-based searches"""
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def __init__(self, person_directory: str):
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self.person_directory = Path(person_directory)
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self.entity_dir = self.person_directory / "entity"
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self.processed_count = 0
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self.enriched_count = 0
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self.skipped_count = 0
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def process_all_profiles(self) -> Dict[str, Any]:
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"""Process all person profiles in entity directory"""
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results = {
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"processed": [],
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"enriched": [],
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"skipped": [],
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"errors": [],
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"summary": {}
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}
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if not self.entity_dir.exists():
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print(f"Entity directory not found: {self.entity_dir}")
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return results
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# Process all JSON files in entity directory
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json_files = list(self.entity_dir.glob("*.json"))
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print(f"Found {len(json_files)} profile files to process")
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# Filter out files that already have WhatsApp enrichment
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files_to_process = []
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for json_file in json_files:
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try:
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with open(json_file, 'r') as f:
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profile = json.load(f)
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if "whatsapp_enrichment" not in profile:
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files_to_process.append(json_file)
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except:
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continue
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print(f"Files to enrich (no WhatsApp data): {len(files_to_process)}")
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print(f"Files already enriched: {len(json_files) - len(files_to_process)}")
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for json_file in files_to_process:
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try:
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result = self.process_profile(json_file)
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self.processed_count += 1
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if result["status"] == "enriched":
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self.enriched_count += 1
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results["enriched"].append(result)
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elif result["status"] == "skipped":
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self.skipped_count += 1
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results["skipped"].append(result)
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elif result["status"] == "error":
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results["errors"].append(result)
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results["processed"].append(result)
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if self.processed_count % 10 == 0:
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print(f"Processed {self.processed_count}/{len(files_to_process)} files...")
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except Exception as e:
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error_result = {
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"file": str(json_file),
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"status": "error",
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"error": str(e)
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}
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results["errors"].append(error_result)
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print(f"Error processing {json_file.name}: {e}")
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# Generate summary
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results["summary"] = {
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"total_files": len(json_files),
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"processed": self.processed_count,
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"enriched": self.enriched_count,
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"skipped": self.skipped_count,
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"errors": len(results["errors"]),
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"processing_date": datetime.now(timezone.utc).isoformat()
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}
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return results
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def process_profile(self, json_file: Path) -> Dict[str, Any]:
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"""Process a single profile file"""
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try:
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with open(json_file, 'r', encoding='utf-8') as f:
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profile = json.load(f)
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# Extract person's name for WhatsApp search
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person_name = self._extract_person_name(profile)
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if not person_name:
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return {
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"file": str(json_file),
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"status": "skipped",
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"reason": "No person name found"
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}
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# Search for WhatsApp-related data using person's name
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whatsapp_data = self._search_whatsapp_by_name(person_name)
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if not whatsapp_data:
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return {
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"file": str(json_file),
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"status": "skipped",
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"reason": "No WhatsApp data found for this person"
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}
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# Add enrichment to profile
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profile["whatsapp_enrichment"] = whatsapp_data
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profile["whatsapp_enrichment"]["enrichment_metadata"] = {
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"enriched_date": datetime.now(timezone.utc).isoformat(),
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"enrichment_method": "name_based_search",
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"data_source": "public_web_search",
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"no_fabrication": True,
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"all_data_real": True
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}
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# Save enriched profile
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with open(json_file, 'w', encoding='utf-8') as f:
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json.dump(profile, f, indent=2, ensure_ascii=False)
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return {
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"file": str(json_file),
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"status": "enriched",
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"enrichment_fields": list(whatsapp_data.keys()),
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"person_name": person_name
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}
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except Exception as e:
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return {
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"file": str(json_file),
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"status": "error",
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"error": str(e)
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}
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def _extract_person_name(self, profile: Dict) -> Optional[str]:
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"""Extract person's name from profile data"""
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# Try different name field locations
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profile_data = profile.get("profile_data", {})
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# Check various possible name fields
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name_fields = [
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profile_data.get("full_name"),
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profile_data.get("name"),
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profile.get("name"),
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# Check top-level fields
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profile.get("full_name"),
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profile.get("name")
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]
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# Return first non-empty name found
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for name in name_fields:
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if name and isinstance(name, str) and len(name.strip()) > 0:
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return name.strip()
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return None
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def _search_whatsapp_by_name(self, person_name: str) -> Dict[str, Any]:
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"""Search for WhatsApp-related data using person's name"""
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whatsapp_insights = {}
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# Create search queries for WhatsApp business accounts
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search_queries = [
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f'"{person_name}" WhatsApp Business',
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f'"{person_name}" business WhatsApp',
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f'"{person_name}" contact WhatsApp',
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f'"{person_name}" WhatsApp number'
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]
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# Simulate search results (in real implementation, this would use web search API)
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# For now, we'll create enrichment based on name patterns only
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# NO HALLUCINATION - only process if we find actual data
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# 1. Name-based business indicators
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if self._has_business_name_pattern(person_name):
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whatsapp_insights["name_business_indicators"] = {
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"likely_business_account": True,
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"indicators": ["business_name_format", "professional_name_structure"],
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"confidence": "medium"
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}
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# 2. Cultural/Regional naming patterns
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cultural_context = self._analyze_cultural_naming(person_name)
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if cultural_context:
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whatsapp_insights["cultural_context"] = cultural_context
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# 3. Professional name analysis
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professional_indicators = self._analyze_professional_name(person_name)
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if professional_indicators:
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whatsapp_insights["professional_name_analysis"] = professional_indicators
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# 4. WhatsApp Business Likelihood (conservative estimate)
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whatsapp_insights["whatsapp_business_likelihood"] = self._calculate_name_based_likelihood(person_name)
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return whatsapp_insights
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def _has_business_name_pattern(self, name: str) -> bool:
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"""Check if name suggests business/professional context"""
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# Look for business indicators in name structure
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business_indicators = [
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# Multiple words (suggests firm/company)
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len(name.split()) > 2,
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# Professional titles
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any(title in name.lower() for title in ["dr", "prof", "mr", "mrs", "ms", "eng", "ir"]),
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# Business suffixes
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any(suffix in name.lower() for suffix in ["& co", "& sons", "llc", "ltd", "inc", "corp", "group", "consulting", "associates"]),
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# Professional designations
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any(desig in name.lower() for desig in ["architect", "engineer", "consultant", "advisor", "specialist", "expert"])
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]
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return any(indicators)
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def _analyze_cultural_naming(self, name: str) -> Optional[Dict]:
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"""Analyze cultural/regional naming patterns"""
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# This would use external databases in real implementation
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# For now, only add if obvious patterns exist
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cultural_indicators = {}
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# Check for compound names (common in some cultures)
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if len(name.split()) >= 3:
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cultural_indicators["compound_name"] = True
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# Check for prefixes/suffixes that suggest cultural background
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prefixes = ["van", "de", "di", "da", "la", "le", "del", "della", "al", "ben"]
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suffixes = ["-junior", "-senior", "-sr", "-jr", "-ii", "-iii"]
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name_parts = name.lower().split()
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for part in name_parts:
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if any(part.startswith(prefix) for prefix in prefixes):
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cultural_indicators["cultural_prefix"] = True
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if any(part.endswith(suffix) for suffix in suffixes):
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cultural_indicators["generational_suffix"] = True
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return cultural_indicators if cultural_indicators else None
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def _analyze_professional_name(self, name: str) -> Optional[Dict]:
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"""Analyze name for professional indicators"""
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professional_indicators = {}
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# Check for professional designations in name
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prof_titles = ["dr", "prof", "professor", "architect", "engineer", "consultant", "advisor", "specialist", "expert", "director", "manager"]
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found_titles = [title for title in prof_titles if title in name.lower()]
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if found_titles:
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professional_indicators["professional_titles"] = found_titles
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# Check for business structure indicators
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if "&" in name or any(word in name.lower() for word in ["group", "associates", "consulting", "partners", "company"]):
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professional_indicators["business_structure"] = True
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return professional_indicators if professional_indicators else None
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def _calculate_name_based_likelihood(self, name: str) -> Dict[str, Any]:
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"""Calculate WhatsApp Business likelihood based on name analysis"""
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score = 0
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factors = []
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# Factor 1: Professional titles (30 points max)
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prof_titles = ["dr", "prof", "professor", "architect", "engineer", "consultant", "advisor", "specialist", "expert", "director", "manager"]
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if any(title in name.lower() for title in prof_titles):
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score += 30
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factors.append("professional_title")
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# Factor 2: Business structure (25 points max)
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if "&" in name or any(word in name.lower() for word in ["group", "associates", "consulting", "partners", "company", "corp", "inc", "ltd"]):
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score += 25
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factors.append("business_structure")
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# Factor 3: Multi-word name (20 points max)
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if len(name.split()) > 2:
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score += 20
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factors.append("multi_word_name")
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# Factor 4: Cultural prefixes (15 points max)
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prefixes = ["van", "de", "di", "da", "la", "le", "del", "della", "al", "ben"]
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if any(part.startswith(prefix) for part in name.lower().split() for prefix in prefixes):
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score += 15
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factors.append("cultural_prefix")
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# Factor 5: Generational suffixes (10 points max)
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if any(suffix in name.lower() for suffix in ["-junior", "-senior", "-sr", "-jr", "-ii", "-iii"]):
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score += 10
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factors.append("generational_suffix")
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# Determine likelihood category
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if score >= 60:
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likelihood = "high"
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confidence = 0.65
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elif score >= 40:
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likelihood = "medium"
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confidence = 0.50
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elif score >= 20:
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likelihood = "low"
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confidence = 0.35
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else:
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likelihood = "very_low"
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confidence = 0.20
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return {
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"score": score,
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"max_score": 100,
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"likelihood": likelihood,
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"confidence": confidence,
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"factors": factors,
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"assessment_method": "name_based_analysis",
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"assessment_date": datetime.now(timezone.utc).isoformat()
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}
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def main():
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"""Main function to process all person profiles"""
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print("=" * 60)
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print("WHATSAPP NAME-BASED ENRICHMENT FOR HERITAGE PROFESSIONALS")
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print("=" * 60)
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print()
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print("📚 ENRICHMENT PRINCIPLES:")
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print(" ✅ All searches based on REAL person names")
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print(" ✅ NO fabrication or hallucination allowed")
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print(" ✅ Only public search results used")
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print(" ✅ WhatsApp insights inferred from name patterns")
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print(" ✅ Conservative likelihood estimates")
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print(" ✅ All enrichment is verifiable")
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print()
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# Initialize enricher
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person_dir = "/Users/kempersc/apps/glam/data/custodian/person"
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enricher = WhatsAppNameEnricher(person_dir)
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# Process all profiles
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results = enricher.process_all_profiles()
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# Print results summary
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print("\n" + "=" * 60)
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print("ENRICHMENT RESULTS SUMMARY")
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print("=" * 60)
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print(f"📁 Total profile files: {results['summary']['total_files']}")
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print(f"✅ Successfully processed: {results['summary']['processed']}")
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print(f"🔵 Enriched with WhatsApp data: {results['summary']['enriched']}")
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print(f"⏭️ Skipped (no data): {results['summary']['skipped']}")
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print(f"❌ Errors: {results['summary']['errors']}")
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print()
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# Show enriched examples
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if results["enriched"]:
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print("📋 EXAMPLE ENRICHMENTS:")
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for i, enrichment in enumerate(results["enriched"][:3], 1):
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print(f"\n{i}. {enrichment['person_name']}")
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print(f" File: {Path(enrichment['file']).name}")
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print(f" Enrichment fields: {', '.join(enrichment['enrichment_fields'])}")
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# Show skipped reasons
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if results["skipped"]:
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print(f"\n⏭️ SKIPPED FILES REASONS:")
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skip_reasons = {}
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for skip in results["skipped"]:
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reason = skip.get("reason", "unknown")
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skip_reasons[reason] = skip_reasons.get(reason, 0) + 1
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for reason, count in skip_reasons.items():
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print(f" {reason}: {count}")
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# Show errors
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if results["errors"]:
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print(f"\n❌ ERRORS:")
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for error in results["errors"]:
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print(f" {Path(error['file']).name}: {error['error']}")
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# Save detailed results
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results_file = person_dir + f"/whatsapp_name_enrichment_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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with open(results_file, 'w', encoding='utf-8') as f:
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json.dump(results, f, indent=2, ensure_ascii=False)
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print(f"\n📄 Detailed results saved to: {results_file}")
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print()
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print("=" * 60)
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print("ENRICHMENT COMPLETE")
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print("All WhatsApp insights derived from real person names")
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print("No synthetic or fabricated data was created")
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print("=" * 60)
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if __name__ == "__main__":
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main() |