import os
import json
import time
import joblib
import pandas as pd
import numpy as np
from pathlib import Path
from datetime import datetime
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor

# Import delle utility esistenti
from .logger_utils import log_msg as log
from .csv_config import load_gateway_features_csv

def process_train_jobs():
    """Monitora ed esegue i job di addestramento salvando backup cronologici."""
    JOBS_DIR = Path("/data/train/train_jobs")
    JOBS_DIR.mkdir(parents=True, exist_ok=True)
    
    job_files = list(JOBS_DIR.glob("*.lock"))
    if not job_files:
        return

    for job_path in job_files:
        try:
            log(f"[TRAIN-CORE] Rilevato nuovo job: {job_path.name}")
            
            with open(job_path, "r") as f:
                job = json.load(f)
            
            campagna = job["campaign"]
            knn_cfg = job["knn"]
            nan_fill = job["nan_fill"]
            gw_csv = job["gateways_csv"]
            
            # --- GENERAZIONE NOME FILE CON TIMESTAMP (BACKUP) ---
            now_str = datetime.now().strftime("%Y%m%d_%H%M%S")
            model_filename = f"model_camp_{campagna}_{now_str}.joblib"
            model_path = Path("/data/model") / model_filename
            
            # Caricamento Gateway
            gws = load_gateway_features_csv(gw_csv)
            gateways_order = [g.mac for g in gws]

            # Analisi file campioni
            samples_dir = Path("/data/train/samples")
            sample_files = list(samples_dir.glob(f"{campagna}_*.csv"))
            
            X_list, y_z, y_xy = [], [], []
            for fp in sample_files:
                try:
                    df = pd.read_csv(fp, sep=";")
                    if df.empty: continue
                    row = df.iloc[0]
                    # Mapping RSSI basato su gateway.csv (risolve errore 'mac')
                    X_list.append([float(row.get(gw, nan_fill)) for gw in gateways_order])
                    y_z.append(int(round(float(row.get("z")))))
                    y_xy.append([float(row.get("x")), float(row.get("y"))])
                except: continue

            if not X_list:
                log(f"[TRAIN-CORE] ERRORE: Dati non validi per campagna {campagna}")
                job_path.unlink()
                continue

            X, Y_z, Y_xy = np.array(X_list), np.array(y_z), np.array(y_xy)

            # Fitting KNN
            log(f"[TRAIN-CORE] Fitting modello per {model_filename}...")
            floor_clf = KNeighborsClassifier(
                n_neighbors=int(knn_cfg.get('k', 5)), 
                weights=knn_cfg.get('weights', 'distance'),
                metric=knn_cfg.get('metric', 'euclidean')
            ).fit(X, Y_z)
            
            models_xy = {}
            for z in np.unique(Y_z):
                idx = np.where(Y_z == z)[0]
                models_xy[int(z)] = KNeighborsRegressor(
                    n_neighbors=min(int(knn_cfg.get('k', 5)), len(idx)),
                    weights=knn_cfg.get('weights', 'distance'),
                    metric=knn_cfg.get('metric', 'euclidean')
                ).fit(X[idx], Y_xy[idx])

            # Salvataggio Pacchetto
            model_pkg = {
                "floor_clf": floor_clf,
                "xy_by_floor": models_xy,
                "gateways_order": gateways_order,
                "nan_fill": nan_fill,
                "created_at": datetime.now().isoformat(),
                "campaign": campagna,
                "filename": model_filename
            }
            
            Path("/data/model").mkdir(parents=True, exist_ok=True)
            joblib.dump(model_pkg, model_path)
            
            log(f"[TRAIN-CORE] ✅ Addestramento COMPLETATO: {model_filename}")

        except Exception as e:
            log(f"[TRAIN-CORE] ❌ ERRORE CRITICO: {str(e)}")
        
        finally:
            if job_path.exists():
                job_path.unlink()

def run_train_monitor():
    """Loop di monitoraggio per il Core Orchestrator."""
    while True:
        process_train_jobs()
        time.sleep(5)