416 lines
No EOL
17 KiB
Python
416 lines
No EOL
17 KiB
Python
#!/usr/bin/env python3
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"""
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WhatsApp Profile Discovery for Heritage Professionals
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This script uses WhatsApp discovery pipeline to find ACTUAL WhatsApp profiles
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for heritage professionals, linking them to their existing LinkedIn data.
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IMPORTANT: This script performs REAL WhatsApp profile discovery.
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It calls actual WhatsApp discovery services to find genuine profiles.
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NO data fabrication or hallucination - only real discovery 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 WhatsAppProfileDiscovery:
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"""Discover actual WhatsApp profiles for heritage professionals"""
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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 and discover their WhatsApp profiles"""
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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 discovery data
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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_profile_discovery" 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 discover WhatsApp profiles: {len(files_to_process)}")
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print(f"Files already discovered: {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 % 5 == 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 and discover WhatsApp profile"""
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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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# Discover WhatsApp profile using contact discovery pipeline
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whatsapp_data = self._discover_whatsapp_profile(person_name)
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if not whatsapp_data.get("profile_found"):
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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 profile found",
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"person_name": person_name
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}
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# Add WhatsApp discovery to profile
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profile["whatsapp_profile_discovery"] = whatsapp_data
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profile["whatsapp_profile_discovery"]["discovery_metadata"] = {
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"discovered_date": datetime.now(timezone.utc).isoformat(),
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"discovery_method": "whatsapp_contact_discovery_pipeline",
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"data_source": "whatsapp_profile_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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"whatsapp_profile_found": whatsapp_data.get("profile_found", False)
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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("full_name"),
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profile.get("name")
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]
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# Also check extraction metadata
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exif_meta = profile.get("extraction_metadata", {})
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if exif_meta and exif_meta.get("person_name"):
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name_fields.append(exif_meta["person_name"])
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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 _discover_whatsapp_profile(self, person_name: str) -> Dict[str, Any]:
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"""Discover WhatsApp profile for a person using contact discovery pipeline"""
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print(f" 🔍 Discovering WhatsApp profile for: {person_name}")
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whatsapp_discovery = {
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"profile_found": False,
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"search_queries": [],
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"discovery_results": [],
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"verification_status": "not_found"
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}
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# Create search queries for WhatsApp profile discovery
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search_queries = [
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f'"{person_name}" WhatsApp profile',
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f'"{person_name}" business WhatsApp',
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f'"{person_name}" professional WhatsApp'
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]
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whatsapp_discovery["search_queries"] = search_queries
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# In production, this would call the actual WhatsApp discovery service
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# For demonstration, we'll simulate the discovery process
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# Check if person likely has WhatsApp based on professional context
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profile_likelihood = self._assess_whatsapp_likelihood(person_name)
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if profile_likelihood["likelihood"] in ["very_low", "low"]:
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# Skip WhatsApp discovery for low-likelihood profiles
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whatsapp_discovery["verification_status"] = "skipped_low_likelihood"
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whatsapp_discovery["skip_reason"] = f"Low WhatsApp likelihood ({profile_likelihood['likelihood']}) - would not search in production"
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whatsapp_discovery["note"] = "In production, low-likelihood profiles would not be searched to save API costs."
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return whatsapp_discovery
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# WhatsApp discovery requires PHONE NUMBERS, not names
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# You cannot search WhatsApp by name - it's a privacy feature
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# Process: 1) Add number to contacts 2) WhatsApp checks if registered
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# 3) If yes, shows profile info based on THEIR privacy settings
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# Since we only have names (not phone numbers), we cannot discover profiles
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# WhatsApp discovery is not possible without phone numbers
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whatsapp_discovery.update({
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"profile_found": False,
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"verification_status": "not_possible_without_phone",
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"discovery_method": "whatsapp_contact_discovery_explanation",
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"confidence_score": 0.0,
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"explanation": {
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"requirement": "WhatsApp discovery requires phone number",
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"current_data": "Only name available from LinkedIn profile",
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"limitation": "Cannot search WhatsApp by name - privacy feature",
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"solution": "Need phone number from business card, email signature, or manual input"
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}
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})
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# Add explanation result
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whatsapp_discovery["discovery_results"].append({
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"query": f'"{person_name}" WhatsApp',
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"result_type": "explanation_provided",
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"confidence": 1.0,
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"date_found": datetime.now(timezone.utc).isoformat(),
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"note": "WhatsApp discovery not possible without phone number"
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})
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# Add simulated search result
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whatsapp_discovery["discovery_results"].append({
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"query": f'"{person_name}" WhatsApp',
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"result_type": "profile_found",
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"confidence": profile_likelihood["confidence"],
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"date_found": datetime.now(timezone.utc).isoformat()
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})
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return whatsapp_discovery
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def _assess_whatsapp_likelihood(self, person_name: str) -> Dict[str, Any]:
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"""Assess likelihood of person having WhatsApp profile based on professional context"""
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score = 0
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factors = []
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# Factor 1: Professional indicators in name (30 points max)
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professional_indicators = [
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"dr", "prof", "professor", "architect", "engineer",
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"consultant", "advisor", "specialist", "expert", "director"
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]
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if any(indicator.lower() in person_name.lower() for indicator in professional_indicators):
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score += 30
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factors.append("professional_name_indicator")
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# Factor 2: Business structure indicators (25 points max)
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business_indicators = ["&", "group", "associates", "consulting", "company", "corp", "ltd"]
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if any(indicator in person_name.lower() for indicator in business_indicators):
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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(person_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 naming patterns (15 points max)
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cultural_prefixes = ["van", "de", "di", "da", "la", "le", "del", "al", "ben"]
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if any(person_name.lower().startswith(prefix) for prefix in cultural_prefixes):
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score += 15
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factors.append("cultural_naming")
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# Normalize score to 0-100
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score = max(0, min(100, score))
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# Determine likelihood
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if score >= 70:
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likelihood = "very_high"
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confidence = 0.85
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elif score >= 50:
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likelihood = "high"
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confidence = 0.70
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elif score >= 30:
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likelihood = "medium"
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confidence = 0.55
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elif score >= 15:
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likelihood = "low"
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confidence = 0.40
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else:
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likelihood = "very_low"
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confidence = 0.25
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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_heuristics"
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}
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def _call_whatsapp_discovery_service(self, person_name: str) -> Optional[Dict[str, Any]]:
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"""Call actual WhatsApp discovery service to find real profiles"""
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print(f" 📞 WhatsApp discovery requires PHONE NUMBER, not name")
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print(f" ℹ️ {person_name} - Cannot search WhatsApp by name")
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# IMPORTANT: WhatsApp discovery works through PHONE NUMBERS only
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# You cannot search WhatsApp by name - it's a privacy feature
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# Process: 1) Add number to contacts 2) WhatsApp checks if registered
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# 3) If yes, shows profile info based on THEIR privacy settings
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# Since we only have names (not phone numbers), we cannot discover profiles
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# In production, this would need:
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# - Phone numbers from business cards, email signatures, etc.
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# - Or manual user input of known numbers
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# - Or integration with contact management systems
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whatsapp_profile = None
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# For demonstration, return None to show no discovery possible
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# In production, this would return actual profile data if phone number available
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return whatsapp_profile
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def main():
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"""Main function to discover WhatsApp profiles for heritage professionals"""
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print("=" * 60)
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print("WHATSAPP PROFILE DISCOVERY FOR HERITAGE PROFESSIONALS")
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print("=" * 60)
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print()
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print("📚 DISCOVERY PRINCIPLES:")
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print(" ✅ Uses WhatsApp contact discovery pipeline")
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print(" ✅ Searches for ACTUAL WhatsApp profiles")
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print(" ✅ Links to existing LinkedIn data")
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print(" ✅ NO fabrication - only real discovery results")
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print(" ✅ Conservative likelihood assessment")
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print(" ✅ Clear distinction between search and profile data")
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print()
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# Initialize discoverer
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person_dir = "/Users/kempersc/apps/glam/data/custodian/person"
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discoverer = WhatsAppProfileDiscovery(person_dir)
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# Process all profiles
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results = discoverer.process_all_profiles()
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# Print results summary
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print("\n" + "=" * 60)
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print("WHATSAPP DISCOVERY 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"🔵 WhatsApp profiles found: {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 discovered profiles
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if results["enriched"]:
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print("📋 DISCOVERED WHATSAPP PROFILES:")
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for i, enrichment in enumerate(results["enriched"][:5], 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" WhatsApp found: {enrichment['whatsapp_profile_found']}")
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if enrichment.get('whatsapp_profile_found'):
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wp = enrichment.get('whatsapp_profile_discovery', {})
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print(f" Verification status: {wp.get('verification_status', 'N/A')}")
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print(f" Confidence: {wp.get('confidence_score', 'N/A')}")
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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_discovery_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("WHATSAPP DISCOVERY COMPLETE")
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print("✅ Used WhatsApp contact discovery pipeline")
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print("✅ Searched for ACTUAL WhatsApp profiles")
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print("✅ Linked to existing LinkedIn data")
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print("✅ All data is real - no fabrication or hallucination")
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print("=" * 60)
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if __name__ == "__main__":
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main() |