"""
IDS Backend FastAPI - Intrusion Detection System
Gestisce training ML, detection real-time e comunicazione con router MikroTik
"""

from fastapi import FastAPI, HTTPException, BackgroundTasks, Security, Header
from fastapi.middleware.cors import CORSMiddleware
from fastapi.security import APIKeyHeader
from pydantic import BaseModel
from typing import List, Optional, Dict
from datetime import datetime, timedelta
import pandas as pd
import psycopg2
from psycopg2.extras import RealDictCursor
import os
from dotenv import load_dotenv
import asyncio
import secrets

from ml_analyzer import MLAnalyzer
from mikrotik_manager import MikroTikManager

# Load environment variables
load_dotenv()

# API Key Security
API_KEY_NAME = "X-API-Key"
api_key_header = APIKeyHeader(name=API_KEY_NAME, auto_error=False)

def get_api_key():
    """Get API key from environment"""
    return os.getenv("IDS_API_KEY")

async def verify_api_key(api_key: str = Security(api_key_header)):
    """Verify API key for internal service communication"""
    expected_key = get_api_key()
    
    # In development without API key, allow access (backward compatibility)
    if not expected_key:
        return True
    
    if not api_key or api_key != expected_key:
        raise HTTPException(
            status_code=403,
            detail="Invalid or missing API key"
        )
    return True

app = FastAPI(title="IDS API", version="1.0.0")

# CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Global instances
ml_analyzer = MLAnalyzer(model_dir="models")
mikrotik_manager = MikroTikManager()

# Database connection
def get_db_connection():
    return psycopg2.connect(
        host=os.getenv("PGHOST"),
        port=os.getenv("PGPORT"),
        database=os.getenv("PGDATABASE"),
        user=os.getenv("PGUSER"),
        password=os.getenv("PGPASSWORD")
    )

# Pydantic models
class TrainRequest(BaseModel):
    max_records: int = 10000
    hours_back: int = 24
    contamination: float = 0.01

class DetectRequest(BaseModel):
    max_records: int = 5000
    hours_back: int = 1
    risk_threshold: float = 60.0
    auto_block: bool = False

class BlockIPRequest(BaseModel):
    ip_address: str
    list_name: str = "ddos_blocked"
    comment: Optional[str] = None
    timeout_duration: str = "1h"

class UnblockIPRequest(BaseModel):
    ip_address: str
    list_name: str = "ddos_blocked"


# API Endpoints

@app.get("/")
async def root():
    return {
        "service": "IDS API",
        "version": "1.0.0",
        "status": "running",
        "model_loaded": ml_analyzer.model is not None
    }

@app.get("/health")
async def health_check():
    """Check system health"""
    try:
        conn = get_db_connection()
        conn.close()
        db_status = "connected"
    except Exception as e:
        db_status = f"error: {str(e)}"
    
    return {
        "status": "healthy",
        "database": db_status,
        "ml_model": "loaded" if ml_analyzer.model is not None else "not_loaded",
        "timestamp": datetime.now().isoformat()
    }

@app.post("/train")
async def train_model(request: TrainRequest, background_tasks: BackgroundTasks):
    """
    Addestra il modello ML sui log recenti
    Esegue in background per non bloccare l'API
    """
    def do_training():
        conn = None
        cursor = None
        try:
            print("[TRAIN] Inizio training...")
            conn = get_db_connection()
            cursor = conn.cursor(cursor_factory=RealDictCursor)
            
            # Fetch logs recenti
            min_timestamp = datetime.now() - timedelta(hours=request.hours_back)
            query = """
                SELECT * FROM network_logs 
                WHERE timestamp >= %s 
                ORDER BY timestamp DESC 
                LIMIT %s
            """
            cursor.execute(query, (min_timestamp, request.max_records))
            logs = cursor.fetchall()
            
            if not logs:
                print("[TRAIN] Nessun log trovato per training")
                return
            
            print(f"[TRAIN] Trovati {len(logs)} log per training")
            
            # Converti in DataFrame
            df = pd.DataFrame(logs)
            
            # Training
            print("[TRAIN] Addestramento modello...")
            result = ml_analyzer.train(df, contamination=request.contamination)
            print(f"[TRAIN] Modello addestrato: {result}")
            
            # Salva nel database
            print("[TRAIN] Salvataggio training history nel database...")
            cursor.execute("""
                INSERT INTO training_history 
                (model_version, records_processed, features_count, training_duration, status, notes)
                VALUES (%s, %s, %s, %s, %s, %s)
            """, (
                "1.0.0",
                result['records_processed'],
                result['features_count'],
                0,  # duration non ancora implementato
                result['status'],
                f"Anomalie: {result['anomalies_detected']}/{result['unique_ips']}"
            ))
            conn.commit()
            print("[TRAIN] ✅ Training history salvato con successo!")
            
            cursor.close()
            conn.close()
            
            print(f"[TRAIN] Completato: {result}")
            
        except Exception as e:
            print(f"[TRAIN ERROR] ❌ Errore durante training: {e}")
            import traceback
            traceback.print_exc()
            if conn:
                conn.rollback()
        finally:
            if cursor:
                cursor.close()
            if conn:
                conn.close()
    
    # Esegui in background
    background_tasks.add_task(do_training)
    
    return {
        "message": "Training avviato in background",
        "max_records": request.max_records,
        "hours_back": request.hours_back
    }

@app.post("/detect")
async def detect_anomalies(request: DetectRequest):
    """
    Rileva anomalie nei log recenti
    Opzionalmente blocca automaticamente IP anomali
    """
    if ml_analyzer.model is None:
        # Prova a caricare modello salvato
        if not ml_analyzer.load_model():
            raise HTTPException(
                status_code=400,
                detail="Modello non addestrato. Esegui /train prima."
            )
    
    try:
        conn = get_db_connection()
        cursor = conn.cursor(cursor_factory=RealDictCursor)
        
        # Fetch logs recenti
        min_timestamp = datetime.now() - timedelta(hours=request.hours_back)
        query = """
            SELECT * FROM network_logs 
            WHERE timestamp >= %s 
            ORDER BY timestamp DESC 
            LIMIT %s
        """
        cursor.execute(query, (min_timestamp, request.max_records))
        logs = cursor.fetchall()
        
        if not logs:
            return {"detections": [], "message": "Nessun log da analizzare"}
        
        # Converti in DataFrame
        df = pd.DataFrame(logs)
        
        # Detection
        detections = ml_analyzer.detect(df, risk_threshold=request.risk_threshold)
        
        # Salva detections nel database
        for det in detections:
            # Controlla se già esiste
            cursor.execute(
                "SELECT id FROM detections WHERE source_ip = %s ORDER BY detected_at DESC LIMIT 1",
                (det['source_ip'],)
            )
            existing = cursor.fetchone()
            
            if existing:
                # Aggiorna esistente
                cursor.execute("""
                    UPDATE detections 
                    SET risk_score = %s, confidence = %s, anomaly_type = %s, 
                        reason = %s, log_count = %s, last_seen = %s
                    WHERE id = %s
                """, (
                    det['risk_score'], det['confidence'], det['anomaly_type'],
                    det['reason'], det['log_count'], det['last_seen'],
                    existing['id']
                ))
            else:
                # Inserisci nuovo
                cursor.execute("""
                    INSERT INTO detections 
                    (source_ip, risk_score, confidence, anomaly_type, reason, 
                     log_count, first_seen, last_seen)
                    VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
                """, (
                    det['source_ip'], det['risk_score'], det['confidence'],
                    det['anomaly_type'], det['reason'], det['log_count'],
                    det['first_seen'], det['last_seen']
                ))
        
        conn.commit()
        
        # Auto-block se richiesto
        blocked_count = 0
        if request.auto_block and detections:
            # Fetch routers abilitati
            cursor.execute("SELECT * FROM routers WHERE enabled = true")
            routers = cursor.fetchall()
            
            if routers:
                for det in detections:
                    if det['risk_score'] >= 80:  # Solo rischio CRITICO
                        # Controlla whitelist
                        cursor.execute(
                            "SELECT id FROM whitelist WHERE ip_address = %s AND active = true",
                            (det['source_ip'],)
                        )
                        if cursor.fetchone():
                            continue  # Skip whitelisted
                        
                        # Blocca su tutti i router
                        results = await mikrotik_manager.block_ip_on_all_routers(
                            routers,
                            det['source_ip'],
                            comment=f"IDS: {det['anomaly_type']}"
                        )
                        
                        if any(results.values()):
                            # Aggiorna detection
                            cursor.execute("""
                                UPDATE detections 
                                SET blocked = true, blocked_at = NOW()
                                WHERE source_ip = %s
                            """, (det['source_ip'],))
                            blocked_count += 1
                
                conn.commit()
        
        cursor.close()
        conn.close()
        
        return {
            "detections": detections,
            "total": len(detections),
            "blocked": blocked_count if request.auto_block else 0,
            "message": f"Trovate {len(detections)} anomalie"
        }
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/block-ip")
async def block_ip(request: BlockIPRequest):
    """Blocca manualmente un IP su tutti i router"""
    try:
        conn = get_db_connection()
        cursor = conn.cursor(cursor_factory=RealDictCursor)
        
        # Controlla whitelist
        cursor.execute(
            "SELECT id FROM whitelist WHERE ip_address = %s AND active = true",
            (request.ip_address,)
        )
        if cursor.fetchone():
            raise HTTPException(
                status_code=400,
                detail=f"IP {request.ip_address} è in whitelist"
            )
        
        # Fetch routers
        cursor.execute("SELECT * FROM routers WHERE enabled = true")
        routers = cursor.fetchall()
        
        if not routers:
            raise HTTPException(status_code=400, detail="Nessun router configurato")
        
        # Blocca su tutti i router
        results = await mikrotik_manager.block_ip_on_all_routers(
            routers,
            request.ip_address,
            list_name=request.list_name,
            comment=request.comment or "Manual block",
            timeout_duration=request.timeout_duration
        )
        
        success_count = sum(1 for v in results.values() if v)
        
        cursor.close()
        conn.close()
        
        return {
            "ip_address": request.ip_address,
            "blocked_on": success_count,
            "total_routers": len(routers),
            "results": results
        }
        
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/unblock-ip")
async def unblock_ip(request: UnblockIPRequest):
    """Sblocca un IP da tutti i router"""
    try:
        conn = get_db_connection()
        cursor = conn.cursor(cursor_factory=RealDictCursor)
        
        # Fetch routers
        cursor.execute("SELECT * FROM routers WHERE enabled = true")
        routers = cursor.fetchall()
        
        if not routers:
            raise HTTPException(status_code=400, detail="Nessun router configurato")
        
        # Sblocca da tutti i router
        results = await mikrotik_manager.unblock_ip_on_all_routers(
            routers,
            request.ip_address,
            list_name=request.list_name
        )
        
        success_count = sum(1 for v in results.values() if v)
        
        # Aggiorna database
        cursor.execute("""
            UPDATE detections 
            SET blocked = false 
            WHERE source_ip = %s
        """, (request.ip_address,))
        conn.commit()
        
        cursor.close()
        conn.close()
        
        return {
            "ip_address": request.ip_address,
            "unblocked_from": success_count,
            "total_routers": len(routers),
            "results": results
        }
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/stats")
async def get_stats():
    """Statistiche sistema"""
    try:
        conn = get_db_connection()
        cursor = conn.cursor(cursor_factory=RealDictCursor)
        
        # Log stats
        cursor.execute("SELECT COUNT(*) as total FROM network_logs")
        result = cursor.fetchone()
        total_logs = result['total'] if result else 0
        
        cursor.execute("""
            SELECT COUNT(*) as recent FROM network_logs 
            WHERE timestamp >= NOW() - INTERVAL '1 hour'
        """)
        result = cursor.fetchone()
        recent_logs = result['recent'] if result else 0
        
        # Detection stats
        cursor.execute("SELECT COUNT(*) as total FROM detections")
        result = cursor.fetchone()
        total_detections = result['total'] if result else 0
        
        cursor.execute("""
            SELECT COUNT(*) as blocked FROM detections WHERE blocked = true
        """)
        result = cursor.fetchone()
        blocked_ips = result['blocked'] if result else 0
        
        # Router stats
        cursor.execute("SELECT COUNT(*) as total FROM routers WHERE enabled = true")
        result = cursor.fetchone()
        active_routers = result['total'] if result else 0
        
        # Latest training
        cursor.execute("""
            SELECT * FROM training_history 
            ORDER BY trained_at DESC LIMIT 1
        """)
        latest_training = cursor.fetchone()
        
        cursor.close()
        conn.close()
        
        return {
            "logs": {
                "total": total_logs,
                "last_hour": recent_logs
            },
            "detections": {
                "total": total_detections,
                "blocked": blocked_ips
            },
            "routers": {
                "active": active_routers
            },
            "latest_training": dict(latest_training) if latest_training else None
        }
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


# Service Management Endpoints (Secured with API Key)

@app.get("/services/status")
async def get_services_status(authorized: bool = Security(verify_api_key)):
    """
    Verifica status di tutti i servizi gestiti da systemd
    RICHIEDE: API Key valida per accesso
    """
    try:
        import subprocess
        
        services_to_check = {
            "ml_backend": "ids-ml-backend",
            "syslog_parser": "ids-syslog-parser"
        }
        
        results = {}
        
        for service_name, systemd_unit in services_to_check.items():
            try:
                # Check systemd service status
                result = subprocess.run(
                    ["systemctl", "is-active", systemd_unit],
                    capture_output=True,
                    text=True,
                    timeout=5
                )
                
                is_active = result.stdout.strip() == "active"
                
                # Get more details if active
                details = {}
                if is_active:
                    status_result = subprocess.run(
                        ["systemctl", "status", systemd_unit, "--no-pager"],
                        capture_output=True,
                        text=True,
                        timeout=5
                    )
                    # Extract PID from status if available
                    for line in status_result.stdout.split('\n'):
                        if 'Main PID:' in line:
                            pid = line.split('Main PID:')[1].strip().split()[0]
                            details['pid'] = pid
                            break
                
                results[service_name] = {
                    "running": is_active,
                    "systemd_unit": systemd_unit,
                    "details": details
                }
                
            except subprocess.TimeoutExpired:
                results[service_name] = {
                    "running": False,
                    "error": "Timeout checking service"
                }
            except FileNotFoundError:
                # systemctl not available (likely development environment)
                results[service_name] = {
                    "running": False,
                    "error": "systemd not available"
                }
            except Exception as e:
                results[service_name] = {
                    "running": False,
                    "error": str(e)
                }
        
        return {
            "services": results,
            "timestamp": datetime.now().isoformat()
        }
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


if __name__ == "__main__":
    import uvicorn
    
    # Prova a caricare modello esistente
    ml_analyzer.load_model()
    
    print("🚀 Starting IDS API on http://0.0.0.0:8000")
    print("📚 Docs available at http://0.0.0.0:8000/docs")
    
    uvicorn.run(app, host="0.0.0.0", port=8000)