#!/usr/bin/env python3
"""
Bardach Contacts - Notes Analysis
Pulls all contacts from main Contacts folder, analyzes personalNotes
for junk, duplication, promotable data, and cross-contact duplicates.
"""

import subprocess
import json
import re
import sys
from collections import defaultdict
from datetime import datetime

# --- Config ---
TENANT_ID = "dd4a82e8-85a3-44ac-8800-07945ab4d95f"
CLIENT_ID = "fabb3421-8b34-484b-bc17-e46de9703418"
CLIENT_SECRET = "~QJ8Q~NyQSs4OcGqHZyPrA2CVnq9KBfKiimntbMO"
SCOPE = "https://graph.microsoft.com/.default"
USER = "barbara@bardach.net"
OUTPUT_FILE = "D:/ClaudeTools/temp/bardach_notes_analysis.json"
TOP = 100
TOKEN_REFRESH_INTERVAL = 500

# --- Helpers ---
def get_token():
    result = subprocess.run([
        "curl", "-s", "-X", "POST",
        f"https://login.microsoftonline.com/{TENANT_ID}/oauth2/v2.0/token",
        "-H", "Content-Type: application/x-www-form-urlencoded",
        "-d", f"client_id={CLIENT_ID}",
        "-d", f"client_secret={CLIENT_SECRET}",
        "-d", f"scope={SCOPE}",
        "-d", "grant_type=client_credentials"
    ], capture_output=True, text=True)
    data = json.loads(result.stdout)
    if "access_token" not in data:
        print(f"[ERROR] Token acquisition failed: {data}")
        sys.exit(1)
    return data["access_token"]


def api_get(url, token):
    result = subprocess.run([
        "curl", "-s",
        "-H", f"Authorization: Bearer {token}",
        url
    ], capture_output=True, text=True)
    return json.loads(result.stdout)


def pull_all_contacts(token):
    """Pull all contacts from default Contacts folder with pagination."""
    select_fields = (
        "id,displayName,givenName,surname,emailAddresses,homePhones,"
        "businessPhones,mobilePhone,companyName,jobTitle,personalNotes,"
        "homeAddress,businessAddress,otherAddress,birthday,lastModifiedDateTime"
    )
    url = (
        f"https://graph.microsoft.com/v1.0/users/{USER}/contacts"
        f"?$select={select_fields}&$top={TOP}"
    )

    all_contacts = []
    api_calls = 0
    page = 0

    while url:
        page += 1
        api_calls += 1

        # Re-acquire token every N calls
        if api_calls % TOKEN_REFRESH_INTERVAL == 0:
            print(f"  Re-acquiring token after {api_calls} API calls...")
            token = get_token()

        print(f"  Fetching page {page} ({len(all_contacts)} contacts so far)...")
        data = api_get(url, token)

        if "value" not in data:
            print(f"[ERROR] Unexpected response: {json.dumps(data)[:500]}")
            break

        all_contacts.extend(data["value"])
        url = data.get("@odata.nextLink")

    print(f"  Total contacts fetched: {len(all_contacts)} in {api_calls} API calls")
    return all_contacts, token


# --- Analysis Functions ---

ICLOUD_PATTERNS = [
    r"this contact is read[\s-]*only",
    r"edit.*in outlook",
    r"tap the link",
    r"this contact was created from a read[\s-]*only account",
    r"read[\s-]*only contact",
    r"icloud",
]

PHONE_PATTERNS = [
    r'\(?\d{3}\)?[\s.\-]?\d{3}[\s.\-]?\d{4}',
    r'\+?\d[\d\s.\-]{7,14}\d',
    r'\d{3}[\s.\-]\d{4}',
]

EMAIL_PATTERN = r'[a-zA-Z0-9._%+\-]+@[a-zA-Z0-9.\-]+\.[a-zA-Z]{2,}'


def normalize_phone(p):
    """Strip phone to digits only for comparison."""
    return re.sub(r'\D', '', str(p))


def extract_phones_from_text(text):
    """Extract phone numbers from free text."""
    phones = set()
    for pat in PHONE_PATTERNS:
        for m in re.finditer(pat, text):
            digits = normalize_phone(m.group())
            if len(digits) >= 7:
                phones.add(digits)
    return phones


def extract_emails_from_text(text):
    """Extract email addresses from free text."""
    return {e.lower() for e in re.findall(EMAIL_PATTERN, text)}


def get_contact_phones(c):
    """Get all phone numbers from structured fields."""
    phones = set()
    for p in c.get("homePhones") or []:
        d = normalize_phone(p)
        if d:
            phones.add(d)
    for p in c.get("businessPhones") or []:
        d = normalize_phone(p)
        if d:
            phones.add(d)
    mob = c.get("mobilePhone")
    if mob:
        d = normalize_phone(mob)
        if d:
            phones.add(d)
    return phones


def get_contact_emails(c):
    """Get all emails from structured fields."""
    emails = set()
    for e in c.get("emailAddresses") or []:
        addr = (e.get("address") or "").lower().strip()
        if addr:
            emails.add(addr)
    return emails


def format_address(addr):
    """Convert address dict to string for comparison."""
    if not addr:
        return ""
    parts = []
    for k in ["street", "city", "state", "postalCode", "countryOrRegion"]:
        v = (addr.get(k) or "").strip()
        if v:
            parts.append(v)
    return " ".join(parts).lower()


def analyze_notes(contacts):
    report = {}

    # Separate contacts with/without notes
    with_notes = []
    without_notes = []
    for c in contacts:
        notes = (c.get("personalNotes") or "").strip()
        if notes:
            with_notes.append(c)
        else:
            without_notes.append(c)

    # --- A. Junk/Boilerplate Notes ---
    icloud_warnings = []
    empty_whitespace = []

    for c in contacts:
        raw_notes = c.get("personalNotes") or ""
        stripped = raw_notes.strip()

        if raw_notes and not stripped:
            empty_whitespace.append({
                "id": c["id"],
                "displayName": c.get("displayName", ""),
                "note_repr": repr(raw_notes[:100])
            })
            continue

        if stripped:
            lower = stripped.lower()
            for pat in ICLOUD_PATTERNS:
                if re.search(pat, lower):
                    icloud_warnings.append({
                        "id": c["id"],
                        "displayName": c.get("displayName", ""),
                        "note_preview": stripped[:200]
                    })
                    break

    report["A_junk_boilerplate"] = {
        "icloud_warnings_count": len(icloud_warnings),
        "icloud_warnings": icloud_warnings,
        "empty_whitespace_count": len(empty_whitespace),
        "empty_whitespace": empty_whitespace
    }
    print(f"\n[A] Junk/Boilerplate: {len(icloud_warnings)} iCloud warnings, {len(empty_whitespace)} empty/whitespace")

    # --- B. Notes that duplicate structured fields ---
    dup_phones = []
    dup_emails = []
    dup_company = []
    dup_jobtitle = []
    dup_address = []

    for c in with_notes:
        notes = c.get("personalNotes", "").strip()
        notes_lower = notes.lower()
        name = c.get("displayName", "")

        # Phone duplication
        note_phones = extract_phones_from_text(notes)
        field_phones = get_contact_phones(c)
        overlap_phones = note_phones & field_phones
        if overlap_phones:
            dup_phones.append({
                "displayName": name,
                "duplicated_phones": list(overlap_phones)
            })

        # Email duplication
        note_emails = extract_emails_from_text(notes)
        field_emails = get_contact_emails(c)
        overlap_emails = note_emails & field_emails
        if overlap_emails:
            dup_emails.append({
                "displayName": name,
                "duplicated_emails": list(overlap_emails)
            })

        # Company duplication
        company = (c.get("companyName") or "").strip().lower()
        if company and len(company) > 2 and company in notes_lower:
            dup_company.append({
                "displayName": name,
                "company": c.get("companyName")
            })

        # Job title duplication
        title = (c.get("jobTitle") or "").strip().lower()
        if title and len(title) > 2 and title in notes_lower:
            dup_jobtitle.append({
                "displayName": name,
                "jobTitle": c.get("jobTitle")
            })

        # Address duplication
        for addr_field in ["homeAddress", "businessAddress", "otherAddress"]:
            addr_str = format_address(c.get(addr_field))
            if addr_str and len(addr_str) > 5:
                # Check if significant parts of address appear in notes
                addr_parts = [p for p in addr_str.split() if len(p) > 3]
                matches = sum(1 for p in addr_parts if p in notes_lower)
                if len(addr_parts) > 0 and matches >= len(addr_parts) * 0.5:
                    dup_address.append({
                        "displayName": name,
                        "field": addr_field,
                        "address": format_address(c.get(addr_field))
                    })
                    break  # one match per contact is enough

    report["B_duplicates_in_notes"] = {
        "phones_duplicated_count": len(dup_phones),
        "phones_duplicated": dup_phones,
        "emails_duplicated_count": len(dup_emails),
        "emails_duplicated": dup_emails,
        "company_duplicated_count": len(dup_company),
        "company_duplicated": dup_company,
        "jobtitle_duplicated_count": len(dup_jobtitle),
        "jobtitle_duplicated": dup_jobtitle,
        "address_duplicated_count": len(dup_address),
        "address_duplicated": dup_address
    }
    print(f"[B] Duplicated in notes: {len(dup_phones)} phones, {len(dup_emails)} emails, "
          f"{len(dup_company)} companies, {len(dup_jobtitle)} titles, {len(dup_address)} addresses")

    # --- C. Notes with structured data that SHOULD be in fields ---
    promotable_phones = []
    promotable_emails = []

    for c in with_notes:
        notes = c.get("personalNotes", "").strip()
        name = c.get("displayName", "")

        # Phones in notes NOT in fields
        note_phones = extract_phones_from_text(notes)
        field_phones = get_contact_phones(c)
        extra_phones = note_phones - field_phones
        if extra_phones:
            promotable_phones.append({
                "displayName": name,
                "phones_in_notes_only": list(extra_phones),
                "note_preview": notes[:200]
            })

        # Emails in notes NOT in fields
        note_emails = extract_emails_from_text(notes)
        field_emails = get_contact_emails(c)
        extra_emails = note_emails - field_emails
        if extra_emails:
            promotable_emails.append({
                "displayName": name,
                "emails_in_notes_only": list(extra_emails),
                "note_preview": notes[:200]
            })

    report["C_promotable_data"] = {
        "phones_promotable_count": len(promotable_phones),
        "phones_promotable": promotable_phones,
        "emails_promotable_count": len(promotable_emails),
        "emails_promotable": promotable_emails
    }
    print(f"[C] Promotable data: {len(promotable_phones)} contacts with phones in notes only, "
          f"{len(promotable_emails)} contacts with emails in notes only")

    # --- D. Duplicate notes across contacts ---
    notes_groups = defaultdict(list)
    for c in with_notes:
        notes = c.get("personalNotes", "").strip()
        if notes:
            notes_groups[notes].append(c.get("displayName", c["id"]))

    duplicate_groups = []
    for notes_text, names in sorted(notes_groups.items(), key=lambda x: -len(x[1])):
        if len(names) >= 2:
            duplicate_groups.append({
                "note_preview": notes_text[:200],
                "count": len(names),
                "contacts": names
            })

    report["D_duplicate_notes_across_contacts"] = {
        "groups_count": len(duplicate_groups),
        "groups": duplicate_groups
    }
    print(f"[D] Duplicate notes across contacts: {len(duplicate_groups)} groups")

    # --- E. General statistics ---
    note_lengths = [len(c.get("personalNotes", "").strip()) for c in with_notes]

    buckets = {"1-50": 0, "51-200": 0, "201-500": 0, "500+": 0}
    for l in note_lengths:
        if l <= 50:
            buckets["1-50"] += 1
        elif l <= 200:
            buckets["51-200"] += 1
        elif l <= 500:
            buckets["201-500"] += 1
        else:
            buckets["500+"] += 1

    avg_len = sum(note_lengths) / len(note_lengths) if note_lengths else 0

    # Sample 20 notes of varying lengths
    sorted_by_len = sorted(with_notes, key=lambda c: len(c.get("personalNotes", "")))
    sample_indices = []
    n = len(sorted_by_len)
    if n <= 20:
        sample_indices = list(range(n))
    else:
        step = n / 20
        sample_indices = [int(i * step) for i in range(20)]

    samples = []
    for i in sample_indices:
        c = sorted_by_len[i]
        notes = c.get("personalNotes", "").strip()
        samples.append({
            "displayName": c.get("displayName", ""),
            "note_length": len(notes),
            "note_preview": notes[:200]
        })

    report["E_statistics"] = {
        "total_contacts": len(contacts),
        "contacts_with_notes": len(with_notes),
        "contacts_without_notes": len(without_notes),
        "average_note_length": round(avg_len, 1),
        "length_distribution": buckets,
        "sample_notes": samples
    }
    print(f"[E] Stats: {len(contacts)} total, {len(with_notes)} with notes, "
          f"{len(without_notes)} without, avg length {avg_len:.1f}")

    return report


def main():
    print("=" * 60)
    print("Bardach Contacts - Notes Analysis")
    print("=" * 60)

    print("\n[1] Acquiring token...")
    token = get_token()
    print("  [OK] Token acquired")

    print("\n[2] Pulling all contacts...")
    contacts, token = pull_all_contacts(token)

    print(f"\n[3] Analyzing notes across {len(contacts)} contacts...")
    report = analyze_notes(contacts)

    report["_metadata"] = {
        "generated": datetime.now().isoformat(),
        "total_contacts_analyzed": len(contacts),
        "user": USER
    }

    print(f"\n[4] Saving report to {OUTPUT_FILE}...")
    with open(OUTPUT_FILE, "w", encoding="utf-8") as f:
        json.dump(report, f, indent=2, ensure_ascii=False, default=str)
    print("  [OK] Report saved")

    # --- Print comprehensive report ---
    print("\n" + "=" * 60)
    print("COMPREHENSIVE NOTES ANALYSIS REPORT")
    print("=" * 60)

    print(f"\nTotal contacts: {report['E_statistics']['total_contacts']}")
    print(f"With notes: {report['E_statistics']['contacts_with_notes']}")
    print(f"Without notes: {report['E_statistics']['contacts_without_notes']}")
    print(f"Average note length: {report['E_statistics']['average_note_length']} chars")

    print(f"\n--- A. Junk/Boilerplate ---")
    a = report["A_junk_boilerplate"]
    print(f"iCloud warnings: {a['icloud_warnings_count']}")
    for item in a["icloud_warnings"]:
        print(f"  - {item['displayName']}: {item['note_preview'][:80]}")
    print(f"Empty/whitespace notes: {a['empty_whitespace_count']}")
    for item in a["empty_whitespace"]:
        print(f"  - {item['displayName']}")

    print(f"\n--- B. Notes Duplicating Structured Fields ---")
    b = report["B_duplicates_in_notes"]
    print(f"Phone numbers duplicated: {b['phones_duplicated_count']}")
    for item in b["phones_duplicated"]:
        print(f"  - {item['displayName']}: {item['duplicated_phones']}")
    print(f"Emails duplicated: {b['emails_duplicated_count']}")
    for item in b["emails_duplicated"]:
        print(f"  - {item['displayName']}: {item['duplicated_emails']}")
    print(f"Company names duplicated: {b['company_duplicated_count']}")
    for item in b["company_duplicated"]:
        print(f"  - {item['displayName']}: {item['company']}")
    print(f"Job titles duplicated: {b['jobtitle_duplicated_count']}")
    for item in b["jobtitle_duplicated"]:
        print(f"  - {item['displayName']}: {item['jobTitle']}")
    print(f"Addresses duplicated: {b['address_duplicated_count']}")
    for item in b["address_duplicated"]:
        print(f"  - {item['displayName']}: {item['field']} = {item['address']}")

    print(f"\n--- C. Promotable Data (in notes but NOT in fields) ---")
    c_data = report["C_promotable_data"]
    print(f"Contacts with phones in notes only: {c_data['phones_promotable_count']}")
    for item in c_data["phones_promotable"]:
        print(f"  - {item['displayName']}: {item['phones_in_notes_only']}")
    print(f"Contacts with emails in notes only: {c_data['emails_promotable_count']}")
    for item in c_data["emails_promotable"]:
        print(f"  - {item['displayName']}: {item['emails_in_notes_only']}")

    print(f"\n--- D. Duplicate Notes Across Contacts ---")
    d = report["D_duplicate_notes_across_contacts"]
    print(f"Groups with identical notes: {d['groups_count']}")
    for g in d["groups"]:
        print(f"  - {g['count']} contacts share: \"{g['note_preview'][:100]}\"")
        for name in g["contacts"]:
            print(f"      {name}")

    print(f"\n--- E. Note Length Distribution ---")
    dist = report["E_statistics"]["length_distribution"]
    for bucket, count in dist.items():
        print(f"  {bucket}: {count}")

    print(f"\n--- E. Sample Notes (20 samples, varying lengths) ---")
    for s in report["E_statistics"]["sample_notes"]:
        print(f"  [{s['note_length']} chars] {s['displayName']}: {s['note_preview'][:120]}")

    print("\n[DONE]")


if __name__ == "__main__":
    main()