import asyncio
import uuid
import random
from datetime import datetime, timedelta
from motor.motor_asyncio import AsyncIOMotorClient
import os
from dotenv import load_dotenv

load_dotenv(os.path.join(os.path.dirname(__file__), '..', '.env'))

MONGO_URI = os.getenv("MONGO_URI", "mongodb://localhost:27017")
DB_NAME = "janrakshak"

async def seed():
    client = AsyncIOMotorClient(MONGO_URI)
    db = client[DB_NAME]
    
    print("Clearing existing incidents for a clean demo state...")
    await db["incidents"].delete_many({})
    
    # 1. Coordinate Centers for DBSCAN (Jamtara & Bharatpur)
    # Note: GeoJSON uses [longitude, latitude]
    clusters = {
        "Jamtara_Hub": [86.8000, 23.9667],
        "Bharatpur_Hub": [77.4930, 27.2152]
    }
    
    # 2. Key Entities for Agentic AI to find historical overlap
    scammer_phones = ["+91-9876543210", "+91-8765432109", "+91-7654321098"]
    safe_accounts = ["SBI-987654321", "HDFC-123456789"]
    
    incidents = []
    
    print("Generating 100 heavily interconnected fraud incidents...")
    for i in range(100):
        # Pick a cluster 80% of the time, noise 20%
        if random.random() < 0.8:
            hub = random.choice(list(clusters.values()))
            # Add small random offset for scatter (approx 1-5km)
            lng = hub[0] + random.uniform(-0.05, 0.05)
            lat = hub[1] + random.uniform(-0.05, 0.05)
        else:
            # Random noise across India
            lng = random.uniform(68.0, 97.0)
            lat = random.uniform(8.0, 37.0)
            
        # Select entities
        phone = random.choice(scammer_phones)
        bank = random.choice(safe_accounts)
        
        incident = {
            "incident_id": str(uuid.uuid4()),
            "type": "audio",
            "timestamp": datetime.utcnow() - timedelta(days=random.randint(0, 30)),
            "location": {
                "type": "Point",
                "coordinates": [lng, lat]
            },
            "extracted_entities": [
                {"text": phone, "label": "phone_number"},
                {"text": bank, "label": "bank_account"}
            ],
            "risk_score": round(random.uniform(0.7, 0.99), 2),
            "threat_dossier": "Legacy historical record.",
            "details": {
                "transcription": f"Hello, this is customs. Transfer to {bank} immediately or face arrest.",
                "intent": "Extortion",
                "scam_stage": "Stage 5: Extortion",
                "deepfake_probability": round(random.uniform(0.0, 1.0), 2)
            },
            "digital_signature": "SEED_DATA_SIGNATURE"
        }
        incidents.append(incident)
        
    await db["incidents"].insert_many(incidents)
    
    # Ensure geospatial index exists
    await db["incidents"].create_index([("location", "2dsphere")])
    
    print(f"Successfully seeded {len(incidents)} incidents.")
    print("Agentic AI will now successfully find historical patterns for phones:", scammer_phones)
    print("DBSCAN will now flag giant red hotspots over Jamtara and Bharatpur.")

if __name__ == "__main__":
    asyncio.run(seed())