data/generate_data.py

"""
Generate realistic property listings + insurance + documents data.
Run: python data/generate_data.py
"""
import json, random, sqlite3
from pathlib import Path
from datetime import date, timedelta

random.seed(42)

LOCATIONS = [
    "Andheri West","Andheri East","Bandra West","Bandra East",
    "Juhu","Versova","Santacruz West","Santacruz East",
    "Khar","Vile Parle","Goregaon","Malad","Kandivali",
    "Borivali","Dadar","Worli","Lower Parel","Prabhadevi",
    "Powai","Vikhroli","Ghatkopar","Mulund","Thane",
    "Navi Mumbai","Kharghar","Panvel","Ulwe","Dombivli",
    "Kalyan","Mira Road","Vasai","Nalasopara",
]
PREMIUM = {"Bandra West","Juhu","Worli","Lower Parel","Prabhadevi","Khar","Santacruz West"}
BUILDERS = [
    "Lodha Group","Godrej Properties","Oberoi Realty","Hiranandani",
    "Shapoorji Pallonji","Runwal Group","Rustomjee","Kalpataru",
    "L&T Realty","Mahindra Lifespaces","Prestige Group",
    "Raymond Realty","Piramal Realty","Tata Housing",
]
PROP_TYPES  = ["Apartment","Flat","Villa","Row House","Penthouse","Studio","Duplex"]
FURNISHINGS = ["Fully Furnished","Semi Furnished","Unfurnished"]
STATUSES    = ["Ready to Move","Under Construction","Ready to Move","Ready to Move"]
AMENITIES_POOL = [
    "Gym","Swimming Pool","Club House","Parking","Security","Power Backup","Lift",
    "Garden","Jogging Track","Kids Play Area","Indoor Games","CCTV","Intercom",
    "Visitor Parking","Terrace","Co-working Space","Library","Mini Theatre","Pet-friendly",
]
INS_COMPANIES = [
    "New India Assurance","HDFC ERGO","Bajaj Allianz","ICICI Lombard",
    "National Insurance","Oriental Insurance","United India",
]
INS_STATUSES = ["ACTIVE","ACTIVE","ACTIVE","PENDING","EXPIRED"]
DOCUMENT_TYPES = [
    "Sale Agreement","NOC from Society","Occupation Certificate",
    "Property Card","Index II","Stamp Duty Receipt","Possession Letter",
    "Title Search Report","Encumbrance Certificate","Building Plan Approval",
]
DOC_STATUSES = ["RECEIVED","PENDING","RECEIVED","RECEIVED","MISSING"]

def price_range(bhk, location):
    base = {1:(0.45,1.2),2:(0.9,2.5),3:(1.5,4.5),4:(3.0,8.0),5:(6.0,18.0)}
    lo,hi = base.get(bhk,(1.0,3.0))
    if location in PREMIUM: lo,hi = lo*1.6, hi*2.0
    return round(random.uniform(lo,hi),2)

def area_range(bhk):
    ranges = {1:(380,550),2:(650,950),3:(1000,1600),4:(1800,3000),5:(3200,6000)}
    lo,hi = ranges.get(bhk,(600,1200))
    return random.randint(lo,hi)

properties, insurance, documents = [], [], []
pid = 1001

for _ in range(300):
    bhk      = random.choices([1,2,3,4,5], weights=[15,35,30,15,5])[0]
    location = random.choice(LOCATIONS)
    ptype    = random.choice(PROP_TYPES)
    price    = price_range(bhk, location)
    area     = area_range(bhk)
    floors   = random.randint(10,45)
    floor    = random.randint(1,floors)
    amenities= random.sample(AMENITIES_POOL, k=random.randint(5,12))
    furnish  = random.choice(FURNISHINGS)
    status   = random.choice(STATUSES)
    builder  = random.choice(BUILDERS)
    society  = f"{builder.split()[0]} {random.choice(['Grandeur','Heights','Residences','Enclave','Greens','Palms','Towers','Estates','Park','Vista','Elysium','Artesia'])}"
    age      = random.randint(0,15)
    ppsf     = round((price*100)/(area/100), 0)
    city     = "Mumbai" if location not in ["Thane","Navi Mumbai","Kharghar","Panvel","Ulwe","Dombivli","Kalyan","Mira Road","Vasai","Nalasopara"] else "MMR"

    prop = {
        "id": pid,
        "title": f"{bhk} BHK {ptype} in {location}",
        "bhk": bhk, "type": ptype,
        "location": location, "city": city,
        "price_cr": price,
        "price_display": f"₹{price} Cr",
        "area_sqft": area,
        "price_per_sqft": int(ppsf),
        "floor": floor, "total_floors": floors,
        "furnishing": furnish,
        "amenities": amenities,
        "parking": "Parking" in amenities,
        "pool": "Swimming Pool" in amenities,
        "age_years": age,
        "status": status,
        "builder": builder,
        "society": society,
        "bedrooms": bhk,
        "bathrooms": bhk if bhk<=3 else bhk-1,
        "balconies": random.randint(1,min(bhk,3)),
        "facing": random.choice(["East","West","North","South","North-East","North-West"]),
        "listed_days_ago": random.randint(1,90),
        "contact": f"+91-9{random.randint(100000000,999999999)}",
        "description": (
            f"Spacious {bhk} BHK {ptype.lower()} in {society}, {location}. "
            f"{'Ready to move. ' if status=='Ready to Move' else 'Under construction. '}"
            f"{furnish} unit, {area} sqft, floor {floor}/{floors}. "
            f"Key amenities: {', '.join(amenities[:5])}. "
            f"Priced at ₹{price} Cr (₹{int(ppsf):,}/sqft). Built by {builder}."
        ),
        "available": True,
        "featured": random.random() < 0.1,
    }
    properties.append(prop)

    # Insurance record
    ins_status = random.choice(INS_STATUSES)
    start = date.today() - timedelta(days=random.randint(10,350))
    expiry = start + timedelta(days=365)
    insurance.append({
        "property_id": pid,
        "company": random.choice(INS_COMPANIES),
        "policy_no": f"POL-{pid}-{random.randint(10000,99999)}",
        "status": ins_status,
        "start_date": start.isoformat(),
        "expiry_date": expiry.isoformat(),
        "premium_annual": random.randint(8000,45000),
        "followup_person": random.choice(["Rajan Sharma","Priya Ghosh","Amit Das","Sneha Patil","Rahul Mehta"]),
        "followup_contact": f"+91-9{random.randint(100000000,999999999)}",
        "notes": random.choice(["Renewal reminder sent","Follow up urgently","Paid","","On hold"]),
    })

    # 2-4 documents per property
    doc_types = random.sample(DOCUMENT_TYPES, k=random.randint(2,5))
    for dt in doc_types:
        documents.append({
            "property_id": pid,
            "document_type": dt,
            "status": random.choice(DOC_STATUSES),
            "received_date": (date.today() - timedelta(days=random.randint(0,180))).isoformat() if random.random()>0.3 else None,
            "notes": "",
        })

    pid += 1

# Save JSON
DATA_DIR = Path(__file__).parent
(DATA_DIR / "properties.json").write_text(json.dumps(properties, indent=2))
print(f"Generated {len(properties)} properties, {len(insurance)} insurance records, {len(documents)} documents")
print(f"Price range: ₹{min(p['price_cr'] for p in properties)} – ₹{max(p['price_cr'] for p in properties)} Cr")
print(f"Locations: {len(set(p['location'] for p in properties))}")