Multi campagna rilevazione

Dockerfile

@@ -2,9 +2,11 @@ FROM python:3.10-slim
 ENV PYTHONUNBUFFERED=1
 ENV PYTHONDONTWRITEBYTECODE=1
 
-# Installiamo solo libgomp1 (necessaria per scikit-learn)
+# Installiamo libgomp1, mosquitto-clients e coreutils (per stdbuf)
 RUN apt-get update && apt-get install -y --no-install-recommends \
     libgomp1 \
+    mosquitto-clients \
+    coreutils \
     && rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app

app/beacons.py

@@ -1,51 +1,82 @@
-from typing import Tuple, Dict, Any, Set, Iterable, List
-from .normalize import normalize_mac, is_mac
+import os
+import csv
+import io
+from pathlib import Path
+from typing import List, Dict, Any, Optional
+import pandas as pd
+from .normalize import norm_mac
 
-DEFAULT_MAC_FIELDS: tuple[str, ...] = (
-    "mac",
-    "beacon_mac",
-    "beaconMac",
-    "device_mac",
-    "deviceMac",
-    "tag_mac",
-    "tagMac",
-)
+def _coord_token(v: float) -> str:
+    """Trasforma coordinata in stringa sicura per nome file (es. -1 -> -1, 120.5 -> 120_5)."""
+    try:
+        fv = float(v)
+    except:
+        return str(v)
+    if abs(fv - round(fv)) < 1e-9:
+        return str(int(round(fv)))
+    s = f"{fv:.3f}".rstrip('0').rstrip('.')
+    return s.replace('.', '_')
 
-def _first_present(item: dict, keys: Iterable[str]) -> str:
-    for k in keys:
-        if k in item and item.get(k):
-            return str(item.get(k))
-    return ""
+def read_job_csv(job_path: Path, delimiter: str) -> List[Dict[str, Any]]:
+    """Legge il Job CSV esteso e normalizza i campi per il Core."""
+    text = job_path.read_text(encoding="utf-8", errors="replace")
+    if not text.strip():
+        return []
+    
+    # Rilevamento automatico delimitatore se diverso dal config
+    first_line = next((ln for ln in text.splitlines() if ln.strip()), "")
+    use_delim = delimiter
+    if ";" in first_line: use_delim = ";"
+    elif "," in first_line: use_delim = ","
 
-def extract_beacon_macs(data: object, mac_fields: tuple[str, ...] = DEFAULT_MAC_FIELDS
-                        ) -> Tuple[Set[str], Dict[str, Any], List[str]]:
-    allowed: Set[str] = set()
-    by_mac: Dict[str, Any] = {}
-    invalid: List[str] = []
-
-    items = None
-    if isinstance(data, list):
-        items = data
-    elif isinstance(data, dict):
-        for key in ("items", "beacons", "trackers", "data", "result"):
-            v = data.get(key)
-            if isinstance(v, list):
-                items = v
-                break
-
-    if not items:
-        return allowed, by_mac, invalid
-
-    for item in items:
-        if not isinstance(item, dict):
+    f = io.StringIO(text)
+    r = csv.reader(f, delimiter=use_delim)
+    header = next(r, None)
+    if not header:
+        return []
+    
+    # Normalizzazione Header (minuscolo, senza spazi)
+    header_norm = [h.strip().lower() for h in header]
+    idx = {name: i for i, name in enumerate(header_norm)}
+    
+    rows = []
+    for cols in r:
+        if not cols or len(cols) < len(header):
+            continue
+        try:
+            # Estrazione usando i nomi esatti dei tuoi file
+            rows.append({
+                "mac": cols[idx["mac"]].strip(),
+                "x": float(cols[idx["x"]]),
+                "y": float(cols[idx["y"]]),
+                "z": float(cols[idx["z"]]),
+                "beaconname": cols[idx["beaconname"]].strip()
+            })
+        except Exception as e:
             continue
-        raw = _first_present(item, mac_fields)
-        mac = normalize_mac(raw)
-        if mac and is_mac(mac):
-            allowed.add(mac)
-            by_mac[mac] = item
-        else:
-            if raw:
-                invalid.append(str(raw))
+    return rows
 
-    return allowed, by_mac, invalid
+def write_samples_csv(path: Path, rows: List[Dict[str, Any]], gateway_headers: List[str], delimiter: str = ";", rssi_decimals: int = 0):
+    """Scrive il campione con l'ordine colonne corretto: mac, x, y, z, ts_start, ts_end, GW..."""
+    # Aggiungiamo i timestamp all'header prima dei gateway
+    header = ["mac", "x", "y", "z", "ts_start", "ts_end"] + gateway_headers
+    
+    tmp_path = path.with_suffix(".tmp")
+    with open(tmp_path, "w", newline="", encoding="utf-8") as f:
+        w = csv.writer(f, delimiter=delimiter)
+        w.writerow(header)
+        for row in rows:
+            line = []
+            for col in header:
+                val = row.get(col, "nan")
+                if col == "mac":
+                    line.append(val)
+                elif isinstance(val, float):
+                    if pd.isna(val): line.append("nan")
+                    elif col in ["x", "y", "z"]: line.append(f"{val:.1f}")
+                    # Timestamp e RSSI
+                    else: line.append(f"{val:.{rssi_decimals if col not in ['ts_start', 'ts_end'] else 3}f}")
+                else:
+                    line.append(str(val))
+            w.writerow(line)
+    os.replace(tmp_path, path)

app/config

@@ -0,0 +1 @@
+../config/

app/csv_config.py

@@ -1,66 +1,34 @@
-from __future__ import annotations
-
+# app/csv_config.py
 import csv
-from dataclasses import dataclass
 from pathlib import Path
-from typing import List, Optional
+from typing import List
+from dataclasses import dataclass
 
+# RIPRISTINATO IMPORT RELATIVO: Necessario per il Core Orchestrator
 from .normalize import norm_mac
 
-
 @dataclass
-class GatewayDef:
+class GatewayConfig:
     mac: str
     name: str = ""
-    id: str = ""
+    label: str = ""
 
-
-def _sniff_delimiter(sample: str) -> str:
-    # fallback robusto: prova a sniffare, altrimenti ';'
+def load_gateway_features_csv(file_path: str, delimiter: str = ";") -> List[GatewayConfig]:
+    """Carica l'anagrafica gateway per definire l'ordine delle feature del modello."""
+    gws = []
+    p = Path(file_path)
+    if not p.exists():
+        return gws
+    
     try:
-        return csv.Sniffer().sniff(sample, delimiters=";,\t").delimiter
+        with open(p, "r", encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter=delimiter)
+            # Normalizzazione header
+            reader.fieldnames = [fn.strip().lower() for fn in reader.fieldnames]
+            for row in reader:
+                raw_mac = row.get("mac", "").strip()
+                if raw_mac:
+                    gws.append(GatewayConfig(mac=norm_mac(raw_mac)))
     except Exception:
-        return ";"
-
-
-def load_gateway_features_csv(path: str) -> List[GatewayDef]:
-    """
-    Carica gateway.csv.
-    Attesi header: mac;name;id  (name/id opzionali)
-    Normalizza MAC in formato canonico 'AA:BB:CC:DD:EE:FF'
-    """
-    p = Path(path)
-    if not p.exists():
-        raise FileNotFoundError(f"gateway csv not found: {path}")
-
-    txt = p.read_text(encoding="utf-8", errors="ignore")
-    delim = _sniff_delimiter(txt[:2048])
-
-    rows: List[GatewayDef] = []
-    with p.open("r", encoding="utf-8", errors="ignore", newline="") as f:
-        rd = csv.DictReader(f, delimiter=delim)
-        for r in rd:
-            # accetta anche varianti tipo 'MAC' o 'gw_mac'
-            mac_raw = r.get("mac") or r.get("MAC") or r.get("gw_mac") or r.get("gateway_mac")
-            if not mac_raw:
-                continue
-            mac = norm_mac(mac_raw)
-            if not mac:
-                continue
-
-            name = (r.get("name") or r.get("NAME") or "").strip()
-            gid = (r.get("id") or r.get("ID") or "").strip()
-            rows.append(GatewayDef(mac=mac, name=name, id=gid))
-
-    return rows
-
-
-# ---------------------------------------------------------------------------
-# BACKWARD COMPATIBILITY
-# main.py si aspetta load_gateway_csv() -> List[GatewayDef]
-# ---------------------------------------------------------------------------
-def load_gateway_csv(path: str) -> List[GatewayDef]:
-    """
-    Alias compatibile per vecchie versioni.
-    """
-    return load_gateway_features_csv(path)
+        pass
+    return gws

app/gateways.py

@@ -1,30 +1,26 @@
-from typing import Tuple, Dict, Any, Set, List
-from .normalize import normalize_mac, is_mac
+import pandas as pd
+from .normalize import norm_mac, is_mac # Usiamo norm_mac che esiste sicuramente
 
-def extract_gateway_macs(data: object) -> Tuple[Set[str], Dict[str, Any], List[str]]:
-    """
-    ritorna:
-      - allowed MAC (validi)
-      - mapping mac->record
-      - lista raw MAC invalidi (normalizzati quando possibile)
-    """
-    allowed: Set[str] = set()
-    by_mac: Dict[str, Any] = {}
-    invalid: List[str] = []
-
-    if not isinstance(data, list):
-        return allowed, by_mac, invalid
-
-    for item in data:
-        if not isinstance(item, dict):
-            continue
-        raw = item.get("mac", "")
-        mac = normalize_mac(raw)
-        if mac and is_mac(mac):
-            allowed.add(mac)
-            by_mac[mac] = item
-        else:
-            if raw:
-                invalid.append(str(raw))
-
-    return allowed, by_mac, invalid
+def load_gateway_csv(path, delimiter=";"):
+    """Carica i gateway validi dal CSV."""
+    try:
+        df = pd.read_csv(path, sep=delimiter)
+        # Normalizziamo colonne
+        df.columns = [c.strip().lower() for c in df.columns]
+        
+        if 'mac' not in df.columns:
+            return [], 0, 0
+            
+        valid_macs = []
+        invalid_count = 0
+        
+        for m in df['mac'].astype(str):
+            if is_mac(m):
+                valid_macs.append(norm_mac(m))
+            else:
+                invalid_count += 1
+                
+        return valid_macs, invalid_count, 0
+    except Exception as e:
+        print(f"Errore gateways.py: {e}")
+        return [], 0, 0

app/infer_mode.py

@@ -15,7 +15,7 @@ from .logger_utils import log_msg as log
 # Importiamo la funzione per caricare i gateway per mantenere l'ordine delle feature
 from .csv_config import load_gateway_features_csv
 
-BUILD_TAG = "infer-debug-v19-hierarchical-final"
+BUILD_TAG = "infer-debug-v20-autoreload"
 
 # -------------------------
 # UTILITIES
@@ -27,12 +27,6 @@ def _norm_mac(s: str) -> str:
     return ":".join([s[i:i+2] for i in range(0, 12, 2)])
 
 def _predict_xyz(model_pkg: Dict[str, Any], X: np.ndarray) -> Tuple[int, float, float]:
-    """
-    Logica di predizione basata su train_mode.py:
-    1. Predice Z (piano) tramite KNeighborsClassifier
-    2. Predice X, Y tramite KNeighborsRegressor specifico per quel piano
-    """
-    # 1. Predizione del Piano (Z)
     floor_clf = model_pkg.get("floor_clf")
     if floor_clf is None:
         raise ValueError("Il pacchetto modello non contiene 'floor_clf'")
@@ -40,16 +34,14 @@ def _predict_xyz(model_pkg: Dict[str, Any], X: np.ndarray) -> Tuple[int, float,
     z_pred = floor_clf.predict(X)
     z = int(z_pred[0])
     
-    # 2. Predizione X, Y (Coordinate)
     xy_models = model_pkg.get("xy_by_floor", {})
     regressor = xy_models.get(z)
     
     if regressor is None:
-        # Se il piano predetto non ha un regressore XY, restituiamo solo il piano
         return z, -1.0, -1.0
         
     xy_pred = regressor.predict(X)
-    x, y = xy_pred[0] # L'output del regressore multi-output è [x, y]
+    x, y = xy_pred[0]
     
     return z, float(x), float(y)
 
@@ -87,7 +79,6 @@ class RollingRSSI:
             else:
                 feats.append(np.nan)
         X = np.array(feats).reshape(1, -1)
-        # Sostituisce i NaN con il valore di riempimento definito nel training
         return np.where(np.isnan(X), fill, X), found_count
 
 # -------------------------
@@ -98,29 +89,54 @@ def run_infer(settings: Dict[str, Any]):
     inf_c = settings.get("infer", {})
     api_c = settings.get("api", {})
     mqtt_c = settings.get("mqtt", {})
+    model_path = inf_c.get("model_path", "/data/model/model.joblib")
     
     log(f"INFER_MODE build tag={BUILD_TAG}")
 
-    # Caricamento Modello e Configurazione Training
-    try:
-        model_pkg = joblib.load(inf_c.get("model_path", "/data/model/model.joblib"))
-        # Recuperiamo l'ordine dei gateway e il nan_fill direttamente dal modello salvato
-        gateways_ordered = model_pkg.get("gateways_order")
-        nan_fill = float(model_pkg.get("nan_fill", -110.0))
-        log(f"Model loaded. Features: {len(gateways_ordered)}, Fill: {nan_fill}")
-    except Exception as e:
-        log(f"CRITICAL: Failed to load model: {e}")
-        return
+    # Variabili di stato per il caricamento dinamico
+    model_pkg = None
+    last_model_mtime = 0
+    gateways_ordered = []
+    gateways_set = set()
+    nan_fill = -110.0
+
+    def load_model_dynamic():
+        nonlocal model_pkg, last_model_mtime, gateways_ordered, gateways_set, nan_fill
+        try:
+            current_mtime = os.path.getmtime(model_path)
+            if current_mtime != last_model_mtime:
+                log(f"🧠 Caricamento/Aggiornamento modello: {model_path}")
+                model_pkg = joblib.load(model_path)
+                last_model_mtime = current_mtime
+                
+                # METADATI PER DEBUG
+                gateways_ordered = model_pkg.get("gateways_order", [])
+                gateways_set = set(gateways_ordered)
+                nan_fill = float(model_pkg.get("nan_fill", -110.0))
+                floors = list(model_pkg.get("xy_by_floor", {}).keys())
+                
+                log(f"✅ MODELLO PRONTO: {len(gateways_ordered)} GW allenati.")
+                log(f"🏢 Piani mappati: {floors}")
+                log(f"🧪 Valore Fill (NaN): {nan_fill}")
+                if len(gateways_ordered) > 0:
+                    log(f"📡 Primi 3 GW di riferimento: {gateways_ordered[:3]}")
+                return True
+        except Exception as e:
+            if model_pkg is None:
+                log(f"⚠️ In attesa di un modello valido in {model_path}...")
+            return False
+        return True
+
+    # Primo caricamento
+    load_model_dynamic()
 
-    gateways_set = set(gateways_ordered)
     rolling = RollingRSSI(float(inf_c.get("window_seconds", 5.0)))
     
-    # --- Gestione Token e Beacons (identica a prima) ---
+    # --- Gestione Token e Beacons ---
     token_cache = {"token": None, "expires_at": 0}
     def get_token():
         if time.time() < token_cache["expires_at"]: return token_cache["token"]
         try:
-            # Recupero credenziali da secrets.yaml
             with open("/config/secrets.yaml", "r") as f:
                 sec = yaml.safe_load(f).get("oidc", {})
             payload = {
@@ -147,6 +163,7 @@ def run_infer(settings: Dict[str, Any]):
         except: return []
 
     def on_message(client, userdata, msg):
+        if not gateways_set: return
         gw = _norm_mac(msg.topic.split("/")[-1])
         if gw not in gateways_set: return
         try:
@@ -158,9 +175,12 @@ def run_infer(settings: Dict[str, Any]):
 
     mqtt_client = mqtt.Client()
     mqtt_client.on_message = on_message
-    mqtt_client.connect(mqtt_c["host"], mqtt_c["port"])
-    mqtt_client.subscribe(mqtt_c["topic"])
-    mqtt_client.loop_start()
+    try:
+        mqtt_client.connect(mqtt_c["host"], mqtt_c["port"])
+        mqtt_client.subscribe(mqtt_c["topic"])
+        mqtt_client.loop_start()
+    except Exception as e:
+        log(f"MQTT Connect Error: {e}")
 
     last_predict, last_api_refresh = 0.0, 0.0
     beacons = []
@@ -168,6 +188,9 @@ def run_infer(settings: Dict[str, Any]):
     while True:
         now = time.time()
         rolling.prune()
+        
+        # Verifica se il link simbolico model.joblib è cambiato
+        load_model_dynamic()
 
         if now - last_api_refresh >= float(api_c.get("refresh_seconds", 30)):
             beacons = fetch_beacons()
@@ -175,28 +198,31 @@ def run_infer(settings: Dict[str, Any]):
 
         if now - last_predict >= float(inf_c.get("refresh_seconds", 10.0)):
             rows, count_ok = [], 0
-            for bm in beacons:
-                bm_n = _norm_mac(bm)
-                X, n_found = rolling.aggregate_features(bm_n, gateways_ordered, inf_c.get("aggregate", "median"), nan_fill)
-                
-                z, x, y = -1, -1.0, -1.0
-                if n_found >= int(inf_c.get("min_non_nan", 1)):
-                    try:
-                        z, x, y = _predict_xyz(model_pkg, X)
-                        if z != -1: count_ok += 1
-                    except Exception as e:
-                        log(f"Infer error {bm_n}: {e}")
-                
-                rows.append(f"{bm_n};{int(z)};{int(round(x))};{int(round(y))}")
-
-            try:
-                out_p = Path(inf_c.get("output_csv", "/data/infer/infer.csv"))
-                with open(str(out_p) + ".tmp", "w") as f:
-                    f.write("mac;z;x;y\n")
-                    for r in rows: f.write(r + "\n")
-                os.replace(str(out_p) + ".tmp", out_p)
-                log(f"CYCLE: {count_ok}/{len(rows)} localized")
-            except Exception as e: log(f"File Error: {e}")
+            
+            if model_pkg:
+                for bm in beacons:
+                    bm_n = _norm_mac(bm)
+                    X, n_found = rolling.aggregate_features(bm_n, gateways_ordered, inf_c.get("aggregate", "median"), nan_fill)
+                    
+                    z, x, y = -1, -1.0, -1.0
+                    if n_found >= int(inf_c.get("min_non_nan", 1)):
+                        try:
+                            z, x, y = _predict_xyz(model_pkg, X)
+                            if z != -1: count_ok += 1
+                        except Exception as e:
+                            pass
+                    
+                    rows.append(f"{bm_n};{int(z)};{int(round(x))};{int(round(y))}")
+
+                try:
+                    out_p = Path(inf_c.get("output_csv", "/data/infer/infer.csv"))
+                    out_p.parent.mkdir(parents=True, exist_ok=True)
+                    with open(str(out_p) + ".tmp", "w") as f:
+                        f.write("mac;z;x;y\n")
+                        for r in rows: f.write(r + "\n")
+                    os.replace(str(out_p) + ".tmp", out_p)
+                    log(f"CYCLE: {count_ok}/{len(rows)} localized")
+                except Exception as e: log(f"File Error: {e}")
             
             last_predict = now
         time.sleep(0.5)

app/main.py

@@ -1,817 +1,75 @@
-from .logger_utils import setup_global_logging, log_msg as log
-import csv
-import io
-import json
 import os
-import ssl
 import time
-import traceback
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
-import hashlib
-import re
-import math
-import statistics
-import pandas as pd
-import numpy as np
-import requests
-import joblib
-import paho.mqtt.client as mqtt
-
-# Import locali corretti
+import threading
+from .logger_utils import setup_global_logging, log_msg as log
 from .settings import load_settings
 
-def build_info() -> str:
-    return "infer-debug-v19-fixed"
-
-def main() -> None:
-    # 1. Carica impostazioni
-    settings = load_settings()
-    
-    # 2. Setup immediato dei log e dei silenziatori (PRIMA di ogni altra cosa)
-    setup_global_logging(settings)
-    
-    # 3. Ora puoi loggare e tutto sarà sincronizzato e pulito
-    cfg_file = settings.get("_config_file", "/config/config.yaml")
-    keys = [k for k in settings.keys() if not str(k).startswith("_")]
-    
-    log(f"Settings loaded from {cfg_file}. Keys: {keys}")
-    log(f"BUILD: {build_info()}")
-
-def mac_plain(s: str) -> str:
-    """Normalizza MAC a 12 hex uppercase senza separatori."""
-    return re.sub(r"[^0-9A-Fa-f]", "", (s or "")).upper()
-
-def mac_colon(s: str) -> str:
-    """MAC in formato AA:BB:CC:DD:EE:FF."""
-    p = mac_plain(s)
-    if len(p) != 12:
-        return p
-    return ":".join(p[i:i+2] for i in range(0, 12, 2))
-
-def fmt_rssi(v, decimals: int) -> str:
-    """Formatta RSSI come stringa, evitando '-82.0' quando decimals=0."""
-    if v is None:
-        return "nan"
-    try:
-        fv = float(v)
-    except Exception:
-        return "nan"
-    if math.isnan(fv):
-        return "nan"
-    if decimals <= 0:
-        return str(int(round(fv)))
-    return f"{round(fv, decimals):.{decimals}f}"
-
-
-# -----------------------------
-
-# Build info (printed at startup for traceability)
-BUILD_ID = "ble-ai-localizer main.py 2026-01-30 build-floatagg-v1"
+# --- NUOVO IMPORT ---
+from .train_executor import run_train_monitor
 
 def build_info() -> str:
-    """Return a short build identifier for logs (no external deps, no git required)."""
-    try:
-        p = Path(__file__)
-        data = p.read_bytes()
-        sha = hashlib.sha256(data).hexdigest()[:12]
-        size = p.stat().st_size
-        return f"{BUILD_ID} sha256={sha} size={size}"
-    except Exception:
-        return f"{BUILD_ID} sha256=? size=?"
-
-# Settings
-# -----------------------------
-def load_settings() -> Dict[str, Any]:
-    cfg = os.environ.get("CONFIG", "/config/config.yaml")
-    import yaml
-    with open(cfg, "r", encoding="utf-8") as f:
-        data = yaml.safe_load(f) or {}
-    data["_config_file"] = cfg
-
-    # Normalize config sections: prefer collect_train
-    if "collect_train" not in data and "training" in data:
-        log("WARNING: config usa 'training:' (alias). Consiglio: rinomina in 'collect_train:'")
-        data["collect_train"] = data.get("training", {}) or {}
-    return data
-
-
-# -----------------------------
-# MAC helpers
-# -----------------------------
-def norm_mac(mac: str) -> str:
-    """Return MAC as AA:BB:CC:DD:EE:FF (upper), ignoring separators."""
-    m = (mac or "").strip().replace("-", "").replace(":", "").replace(".", "")
-    m = m.upper()
-    if len(m) != 12:
-        return mac.strip().upper()
-    return ":".join(m[i:i+2] for i in range(0, 12, 2))
-
-
-# -----------------------------
-# CSV write helpers
-# -----------------------------
-def safe_write_csv(
-    path: Path,
-    header: List[str],
-    rows: List[Dict[str, Any]],
-    delimiter: str = ";",
-    rssi_decimals: int = 0,
-):
-    """Scrive CSV in modo atomico e formattazione 'umana'.
-
-    - numeri interi: senza decimali (es. -82 invece di -82.0)
-    - RSSI: arrotondamento controllato da rssi_decimals (0 -> intero, >0 -> N cifre decimali)
-      *si applica solo alle colonne RSSI (dopo mac/x/y/z)*
-    - NaN: 'nan'
-    - colonna 'mac': normalizzata in formato con ':' (es. C3:00:00:57:B9:E7) se passa un MAC valido
-    """
-    tmp = path.with_suffix(path.suffix + ".tmp")
-    # csv.writer richiede un singolo carattere come delimiter
-    if not isinstance(delimiter, str) or len(delimiter) != 1:
-        delimiter = ";"
-
-    try:
-        rssi_decimals = int(rssi_decimals)
-    except Exception:
-        rssi_decimals = 0
-    if rssi_decimals < 0:
-        rssi_decimals = 0
-
-    def fmt_cell(v: Any, col: str, idx: int) -> str:
-        if v is None:
-            return "nan"
-
-        # MAC normalizzato con ':'
-        if col.lower() == "mac" and isinstance(v, str):
-            v2 = mac_colon(v)
-            return v2
-
-        # NaN float
-        if isinstance(v, float):
-            if math.isnan(v):
-                return "nan"
-
-            # colonne RSSI (dopo mac/x/y/z)
-            if idx >= 4:
-                if rssi_decimals == 0:
-                    return str(int(round(v)))
-                return f"{v:.{rssi_decimals}f}"
-
-            # altre colonne: compatta i (quasi) interi
-            if abs(v - round(v)) < 1e-9:
-                return str(int(round(v)))
-            return str(v)
-
-        # int / numpy int
-        if isinstance(v, (int, np.integer)):
-            # RSSI columns (after mac/x/y/z): respect rssi_decimals even for integer values
-            if idx >= 4:
-                if rssi_decimals == 0:
-                    return str(int(v))
-                return f"{float(v):.{rssi_decimals}f}"
-            return str(int(v))
-
-        # numpy float
-        if isinstance(v, np.floating):
-            fv = float(v)
-            if math.isnan(fv):
-                return "nan"
-            if idx >= 4:
-                if rssi_decimals == 0:
-                    return str(int(round(fv)))
-                return f"{fv:.{rssi_decimals}f}"
-            if abs(fv - round(fv)) < 1e-9:
-                return str(int(round(fv)))
-            return str(fv)
-
-        return str(v)
-
-    with tmp.open("w", newline="") as f:
-        w = csv.writer(f, delimiter=delimiter)
-        w.writerow(header)
-        for row in rows:
-            w.writerow([fmt_cell(row.get(col), col, idx) for idx, col in enumerate(header)])
-
-    tmp.replace(path)
-
-
-
-
-def _coord_token(v: float) -> str:
-    # Stable token for filenames from coordinates.
-    # - if integer-ish -> '123'
-    # - else keep up to 3 decimals, strip trailing zeros, replace '.' with '_'
-    try:
-        fv=float(v)
-    except Exception:
-        return str(v)
-    if abs(fv - round(fv)) < 1e-9:
-        return str(int(round(fv)))
-    s=f"{fv:.3f}".rstrip('0').rstrip('.')
-    return s.replace('.', '_')
-def read_job_csv(job_path: Path, delimiter: str) -> List[Dict[str, Any]]:
-    """Legge job CSV supportando due formati:
-
-    1) Legacy:
-       mac;x;y;z
-       C3000057B9F4;1200;450;0
-
-    2) Esteso (storico):
-       Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
-       A21;1;P1-NETW;800;1050;1;BC-21;C3:00:00:57:B9:E6
-
-    Estrae solo X,Y,Z,MAC e normalizza MAC in formato compatto (senza ':', uppercase).
-    """
-    text = job_path.read_text(encoding="utf-8", errors="replace")
-    if not text.strip():
-        return []
-
-    first_line = next((ln for ln in text.splitlines() if ln.strip()), "")
-    use_delim = delimiter
-    if use_delim not in first_line:
-        if ";" in first_line and "," not in first_line:
-            use_delim = ";"
-        elif "," in first_line and ";" not in first_line:
-            use_delim = ","
-
-    def hnorm(h: str) -> str:
-        h = (h or "").strip().lower()
-        h = re_sub_non_alnum(h)
-        return h
-
-    f = io.StringIO(text)
-    r = csv.reader(f, delimiter=use_delim)
-    header = next(r, None)
-    if not header:
-        return []
-
-    header_norm = [hnorm(h) for h in header]
-    idx = {name: i for i, name in enumerate(header_norm) if name}
-
-    def find_idx(names: List[str]) -> Optional[int]:
-        for n in names:
-            if n in idx:
-                return idx[n]
-        return None
-
-    mac_i = find_idx(["mac", "beaconmac", "beacon_mac", "trackermac", "tracker_mac", "device", "devicemac"])
-    x_i = find_idx(["x"])
-    y_i = find_idx(["y"])
-    z_i = find_idx(["z"])
-
-    if mac_i is None or x_i is None or y_i is None or z_i is None:
-        raise ValueError(
-            f"Job CSV header non riconosciuto: {header}. "
-            f"Attesi campi MAC/X/Y/Z (case-insensitive)."
-        )
-
-    rows: List[Dict[str, Any]] = []
-    for cols in r:
-        if not cols:
-            continue
-        if len(cols) <= max(mac_i, x_i, y_i, z_i):
-            continue
-        mac_raw = (cols[mac_i] or "").strip()
-        if not mac_raw:
-            continue
-
-        mac_compact = norm_mac(mac_raw).replace(":", "")
-
-        try:
-            x = float((cols[x_i] or "").strip())
-            y = float((cols[y_i] or "").strip())
-            z = float((cols[z_i] or "").strip())
-        except Exception:
-            continue
-
-        rows.append({"mac": mac_compact, "x": x, "y": y, "z": z})
-
-    return rows
-
-
-def re_sub_non_alnum(s: str) -> str:
-    out = []
-    for ch in s:
-        if ("a" <= ch <= "z") or ("0" <= ch <= "9"):
-            out.append(ch)
-    return "".join(out)
-
-
-def write_samples_csv(
-    out_path: Path,
-    sample_rows: List[Dict[str, Any]],
-    gateway_macs: List[str],
-    *,
-    delimiter: str = ";",
-    rssi_decimals: int = 0,
-) -> None:
-    header = ["mac", "x", "y", "z"] + gateway_macs
-    safe_write_csv(out_path, header, sample_rows, delimiter=delimiter, rssi_decimals=rssi_decimals)
-
-def load_gateway_csv(path: Path, delimiter: str = ";") -> Tuple[List[str], int, int]:
-    df = pd.read_csv(path, delimiter=delimiter)
-    cols = [c.strip().lower() for c in df.columns]
-    df.columns = cols
-
-    invalid = 0
-    macs: List[str] = []
-    seen = set()
-
-    if "mac" not in df.columns:
-        raise ValueError(f"gateway.csv must have a 'mac' column, got columns={list(df.columns)}")
-
-    for v in df["mac"].astype(str).tolist():
-        nm = norm_mac(v)
-        if len(nm.replace(":", "")) != 12:
-            invalid += 1
-            continue
-        if nm in seen:
-            continue
-        seen.add(nm)
-        macs.append(nm)
-
-    duplicates = max(0, len(df) - invalid - len(macs))
-    return macs, invalid, duplicates
-
-
-# -----------------------------
-# Fingerprint collector
-# -----------------------------
-@dataclass
-class FingerprintStats:
-    counts: Dict[str, Dict[str, int]]
-    last: Dict[str, Dict[str, float]]
+    return "core-orchestrator-v1.4-background-train"
 
+def run_all_modes(settings):
+    """Lancia in simultanea i processi core: Collect, Infer e il nuovo Train Executor."""
+    log("Avvio modalità SIMULTANEA (Collect + Train Executor + Infer)...")
+    
+    # 1. Importiamo le funzioni dai rispettivi moduli
+    from .train_collect import run_collect_train
+    from .infer_mode import run_infer
 
-class FingerprintCollector:
-    def __init__(self) -> None:
-        self._lock = None
-        try:
-            import threading
-            self._lock = threading.Lock()
-        except Exception:
-            self._lock = None
-
-        # beacon_norm -> gw_norm -> list of rssi
-        self.rssi: Dict[str, Dict[str, List[float]]] = {}
-        self.last_seen_gw: Dict[str, float] = {}
-        self.last_seen_beacon: Dict[str, float] = {}
-
-    def _with_lock(self):
-        if self._lock is None:
-            class Dummy:
-                def __enter__(self): return None
-                def __exit__(self, *a): return False
-            return Dummy()
-        return self._lock
-
-    def update(self, gw_mac: str, beacon_mac: str, rssi: float) -> None:
-        gw = norm_mac(gw_mac)
-        b = norm_mac(beacon_mac)
-        now = time.time()
-        with self._with_lock():
-            self.last_seen_gw[gw] = now
-            self.last_seen_beacon[b] = now
-            self.rssi.setdefault(b, {}).setdefault(gw, []).append(float(rssi))
-
-    def stats(self, beacons: List[str], gateways: List[str]) -> FingerprintStats:
-        with self._with_lock():
-            counts: Dict[str, Dict[str, int]] = {b: {g: 0 for g in gateways} for b in beacons}
-            last: Dict[str, Dict[str, float]] = {b: {g: float("nan") for g in gateways} for b in beacons}
-            for b in beacons:
-                bm = norm_mac(b)
-                for g in gateways:
-                    gm = norm_mac(g)
-                    vals = self.rssi.get(bm, {}).get(gm, [])
-                    counts[bm][gm] = len(vals)
-                    if vals:
-                        last[bm][gm] = vals[-1]
-            return FingerprintStats(counts=counts, last=last)
-
-    def feature_row(
-        self,
-        beacon_mac: str,
-        gateways: List[str],
-        aggregate: str,
-        rssi_min: float,
-        rssi_max: float,
-        min_samples_per_gateway: int,
-        outlier_method: str,
-        mad_z: float,
-        iqr_k: float,
-        max_stddev: Optional[float],
-    ) -> Dict[str, float]:
-        b = norm_mac(beacon_mac)
-        out: Dict[str, float] = {}
-        with self._with_lock():
-            for g in gateways:
-                gm = norm_mac(g)
-                vals = list(self.rssi.get(b, {}).get(gm, []))
-
-                # hard clamp
-                vals = [v for v in vals if (rssi_min <= v <= rssi_max)]
-                if len(vals) < min_samples_per_gateway:
-                    out[gm] = float("nan")
-                    continue
-
-                # outlier removal
-                vals2 = vals
-                if outlier_method == "mad":
-                    vals2 = mad_filter(vals2, z=mad_z)
-                elif outlier_method == "iqr":
-                    vals2 = iqr_filter(vals2, k=iqr_k)
-
-                if len(vals2) < min_samples_per_gateway:
-                    out[gm] = float("nan")
-                    continue
-
-                if max_stddev is not None:
-                    import statistics
-                    try:
-                        sd = statistics.pstdev(vals2)
-                        if sd > max_stddev:
-                            out[gm] = float("nan")
-                            continue
-                    except Exception:
-                        pass
-
-                # Aggregate: mantieni float (niente cast a int) per poter usare rssi_decimals.
-                if aggregate == "median":
-                    out[gm] = float(statistics.median(vals2))
-                elif aggregate == "median_low":
-                    out[gm] = float(statistics.median_low(sorted(vals2)))
-                elif aggregate == "median_high":
-                    out[gm] = float(statistics.median_high(sorted(vals2)))
-                elif aggregate == "mean":
-                    out[gm] = float(statistics.fmean(vals2))
-                else:
-                    out[gm] = float(statistics.median(vals2))
-        return out
-
-
-def mad_filter(vals: List[float], z: float = 3.5) -> List[float]:
-    if not vals:
-        return vals
-    s = pd.Series(vals)
-    med = s.median()
-    mad = (s - med).abs().median()
-    if mad == 0:
-        return vals
-    mz = 0.6745 * (s - med).abs() / mad
-    return [float(v) for v, keep in zip(vals, (mz <= z).tolist()) if keep]
-
-
-def iqr_filter(vals: List[float], k: float = 1.5) -> List[float]:
-    if not vals:
-        return vals
-    s = pd.Series(vals)
-    q1 = s.quantile(0.25)
-    q3 = s.quantile(0.75)
-    iqr = q3 - q1
-    if iqr == 0:
-        return vals
-    lo = q1 - k * iqr
-    hi = q3 + k * iqr
-    return [float(v) for v in vals if lo <= v <= hi]
-
-
-# -----------------------------
-# MQTT parsing
-# -----------------------------
-def parse_topic_gateway(topic: str) -> Optional[str]:
-    # expected: publish_out/<gwmac>
-    parts = (topic or "").split("/")
-    if len(parts) < 2:
-        return None
-    return parts[-1]
-
-
-def parse_payload_list(payload: bytes) -> Optional[List[Dict[str, Any]]]:
-    try:
-        obj = json.loads(payload.decode("utf-8", errors="replace"))
-        if isinstance(obj, list):
-            return obj
-        return None
-    except Exception:
-        return None
-
-
-def is_gateway_announce(item: Dict[str, Any]) -> bool:
-    return str(item.get("type", "")).strip().lower() == "gateway" and "mac" in item
-
-
-# -----------------------------
-# Collect train
-# -----------------------------
-def run_collect_train(settings: Dict[str, Any]) -> None:
-    cfg = settings.get("collect_train", {}) or {}
-    paths = settings.get("paths", {}) or {}
-    mqtt_cfg = settings.get("mqtt", {}) or {}
-    debug = settings.get("debug", {}) or {}
-
-    window_seconds = float(cfg.get("window_seconds", 180))
-    poll_seconds = float(cfg.get("poll_seconds", 2))
-    min_non_nan = int(cfg.get("min_non_nan", 3))
-    min_samples_per_gateway = int(cfg.get("min_samples_per_gateway", 5))
-    aggregate = str(cfg.get("aggregate", "median"))
-    # Numero di cifre decimali per i valori RSSI nei file samples (0 = intero)
-    try:
-        rssi_decimals = int(cfg.get("rssi_decimals", 0))
-    except Exception:
-        rssi_decimals = 0
-    if rssi_decimals < 0:
-        rssi_decimals = 0
-    rssi_min = float(cfg.get("rssi_min", -110))
-    rssi_max = float(cfg.get("rssi_max", -25))
-    outlier_method = str(cfg.get("outlier_method", "mad"))
-    mad_z = float(cfg.get("mad_z", 3.5))
-    iqr_k = float(cfg.get("iqr_k", 1.5))
-    max_stddev = cfg.get("max_stddev", None)
-    max_stddev = float(max_stddev) if max_stddev is not None else None
-
-    gateway_csv = Path(paths.get("gateways_csv", "/data/config/gateway.csv"))
-    csv_delimiter = str(paths.get("csv_delimiter", ";"))
-
-    jobs_dir = Path(cfg.get("jobs_dir", "/data/train/jobs"))
-    pending_dir = jobs_dir / "pending"
-    done_dir = jobs_dir / "done"
-    error_dir = jobs_dir / "error"
-    samples_dir = Path(cfg.get("samples_dir", "/data/train/samples"))
-
-    pending_dir.mkdir(parents=True, exist_ok=True)
-    done_dir.mkdir(parents=True, exist_ok=True)
-    error_dir.mkdir(parents=True, exist_ok=True)
-    samples_dir.mkdir(parents=True, exist_ok=True)
-
-    gw_ready_log_seconds = float(cfg.get("gw_ready_log_seconds", 10))
-    gw_ready_sleep_seconds = float(cfg.get("gw_ready_sleep_seconds", 5))
-    gw_ready_check_before_job = bool(cfg.get("gw_ready_check_before_job", True))
-    online_max_age_s = float(debug.get("online_check_seconds", 30))
-    progress_log_seconds = float(cfg.get("wait_all_gateways_log_seconds", 30))
-
-    gateway_macs, invalid, duplicates = load_gateway_csv(gateway_csv, delimiter=csv_delimiter)
-    log(f"[gateway.csv] loaded gateways={len(gateway_macs)} invalid={invalid} duplicates={duplicates}")
-
-    log(
-        "COLLECT_TRAIN config: gateway_csv=%s gateways(feature-set)=%d window_seconds=%.1f poll_seconds=%.1f rssi_decimals=%d jobs_dir=%s "
-        "pending_dir=%s done_dir=%s error_dir=%s samples_dir=%s mqtt=%s:%s topic=%s"
-        % (
-            gateway_csv,
-            len(gateway_macs),
-            window_seconds,
-            poll_seconds,
-            rssi_decimals,
-            jobs_dir,
-            pending_dir,
-            done_dir,
-            error_dir,
-            samples_dir,
-            mqtt_cfg.get("host", ""),
-            mqtt_cfg.get("port", ""),
-            mqtt_cfg.get("topic", "publish_out/#"),
-        )
-    )
-
-    fp = FingerprintCollector()
-
-    # MQTT setup
-    host = mqtt_cfg.get("host", "127.0.0.1")
-    port = int(mqtt_cfg.get("port", 1883))
-    topic = mqtt_cfg.get("topic", "publish_out/#")
-    client_id = mqtt_cfg.get("client_id", "ble-ai-localizer")
-    keepalive = int(mqtt_cfg.get("keepalive", 60))
-    proto = mqtt.MQTTv311
-
-    def on_connect(client, userdata, flags, rc):
-        log(f"MQTT connected rc={rc}, subscribed to {topic}")
-        client.subscribe(topic)
-
-    def on_message(client, userdata, msg):
-        gw_from_topic = parse_topic_gateway(msg.topic)
-        if not gw_from_topic:
-            return
-        payload_list = parse_payload_list(msg.payload)
-        if not payload_list:
-            return
-
-        for it in payload_list:
-            if not isinstance(it, dict):
-                continue
-            if is_gateway_announce(it):
-                gwm = it.get("mac", gw_from_topic)
-                fp.last_seen_gw[norm_mac(gwm)] = time.time()
-                continue
-
-            bmac = it.get("mac")
-            rssi = it.get("rssi")
-            if not bmac or rssi is None:
-                continue
-            try:
-                fp.update(gw_from_topic, bmac, float(rssi))
-            except Exception:
-                continue
-
-    client = mqtt.Client(client_id=client_id, protocol=proto)
-    client.on_connect = on_connect
-    client.on_message = on_message
-
-    username = str(mqtt_cfg.get("username", "") or "")
-    password = str(mqtt_cfg.get("password", "") or "")
-    if username:
-        client.username_pw_set(username, password)
+    # 2. Thread per la RACCOLTA DATI (Collect)
+    t_collect = threading.Thread(target=run_collect_train, args=(settings,), name="CollectorThread", daemon=True)
+    
+    # 3. Thread per il MONITOR ADDESTRAMENTO (Train Executor)
+    # Rileva i file .lock inviati dal Web e addestra il modello in background
+    t_train_exec = threading.Thread(target=run_train_monitor, name="TrainExecutorThread", daemon=True)
 
-    tls = bool(mqtt_cfg.get("tls", False))
-    if tls:
-        client.tls_set(cert_reqs=ssl.CERT_NONE)
-        client.tls_insecure_set(True)
+    # 4. Thread per l'INFERENZA (Predict)
+    t_infer = threading.Thread(target=run_infer, args=(settings,), name="InferenceThread", daemon=True)
 
-    log("MQTT thread started (collect_train)")
-    client.connect(host, port, keepalive=keepalive)
-    client.loop_start()
+    # Avvio di tutti i processi in parallelo
+    t_collect.start()
+    t_train_exec.start()
+    t_infer.start()
 
-    # Wait gateways online
-    last_ready_log = 0.0
-    while True:
-        now = time.time()
-        online = 0
-        missing = []
-        for g in gateway_macs:
-            seen = fp.last_seen_gw.get(norm_mac(g))
-            if seen is not None and (now - seen) <= online_max_age_s:
-                online += 1
-            else:
-                missing.append(norm_mac(g))
-        if online == len(gateway_macs):
-            log(f"GW READY: online={online}/{len(gateway_macs)} (max_age_s={online_max_age_s:.1f})")
-            break
-        if now - last_ready_log >= gw_ready_log_seconds:
-            log(f"WAIT gateways online ({len(missing)} missing, seen={online}/{len(gateway_macs)}): {missing} (max_age_s={online_max_age_s:.1f})")
-            last_ready_log = now
-        time.sleep(gw_ready_sleep_seconds)
-
-    # Job loop
+    log("Tutti i processi core (incluso Train Executor) sono attivi.")
+    
+    # Mantieni il main process attivo
     while True:
-        try:
-            # periodic gw ready log
-            now = time.time()
-            if now - last_ready_log >= gw_ready_log_seconds:
-                online = 0
-                for g in gateway_macs:
-                    seen = fp.last_seen_gw.get(norm_mac(g))
-                    if seen is not None and (now - seen) <= online_max_age_s:
-                        online += 1
-                log(f"GW READY: online={online}/{len(gateway_macs)} (max_age_s={online_max_age_s:.1f})")
-                last_ready_log = now
-
-            # pick job
-            job_files = sorted(pending_dir.glob("*.csv"))
-            if not job_files:
-                time.sleep(poll_seconds)
-                continue
-
-            job_path = job_files[0]
-            job_name = job_path.name
-
-            rows = read_job_csv(job_path, delimiter=csv_delimiter)
-            if not rows:
-                # move empty/bad jobs to error
-                log(f"TRAIN job ERROR: {job_name} err=EmptyJob: no valid rows")
-                job_path.rename(error_dir / job_path.name)
-                continue
-
-            # normalize beacons for stats keys
-            job_beacons_norm = [norm_mac(r["mac"]) for r in rows]
-
-            # optionally wait gateways online before starting the window
-            if gw_ready_check_before_job:
-                while True:
-                    now = time.time()
-                    online = 0
-                    missing = []
-                    for g in gateway_macs:
-                        seen = fp.last_seen_gw.get(norm_mac(g))
-                        if seen is not None and (now - seen) <= online_max_age_s:
-                            online += 1
-                        else:
-                            missing.append(norm_mac(g))
-                    if online == len(gateway_macs):
-                        break
-                    log(f"WAIT gateways online before job ({len(missing)} missing, seen={online}/{len(gateway_macs)}): {missing}")
-                    time.sleep(1.0)
-
-            log(f"TRAIN job START: {job_name} beacons={len(rows)}")
-
-            start = time.time()
-            deadline = start + window_seconds
-            next_progress = start + progress_log_seconds
-
-            while time.time() < deadline:
-                time.sleep(0.5)
-                if progress_log_seconds > 0 and time.time() >= next_progress:
-                    st = fp.stats(job_beacons_norm, gateway_macs)
-                    parts = []
-                    for b in job_beacons_norm:
-                        total = sum(st.counts[b].values())
-                        gw_seen = sum(1 for g in gateway_macs if st.counts[b][g] > 0)
-                        parts.append(f"{b.replace(':','')}: total={total} gw={gw_seen}/{len(gateway_macs)}")
-                    elapsed = int(time.time() - start)
-                    log(f"COLLECT progress: {elapsed}s/{int(window_seconds)}s " + " | ".join(parts))
-                    next_progress = time.time() + progress_log_seconds
-
-            out_rows: List[Dict[str, Any]] = []
-            st = fp.stats(job_beacons_norm, gateway_macs)
-
-            for r, b_norm in zip(rows, job_beacons_norm):
-                feats = fp.feature_row(
-                    beacon_mac=b_norm,
-                    gateways=gateway_macs,
-                    aggregate=aggregate,
-                    rssi_min=rssi_min,
-                    rssi_max=rssi_max,
-                    min_samples_per_gateway=min_samples_per_gateway,
-                    outlier_method=outlier_method,
-                    mad_z=mad_z,
-                    iqr_k=iqr_k,
-                    max_stddev=max_stddev,
-                )
-
-                non_nan = sum(1 for g in gateway_macs if feats.get(g) == feats.get(g))
-                if non_nan < min_non_nan:
-                    sample_info = []
-                    for g in gateway_macs:
-                        c = st.counts[b_norm][g]
-                        if c > 0:
-                            sample_info.append(f"{g} n={c} last={st.last[b_norm][g]}")
-                    preview = ", ".join(sample_info[:8]) + (" ..." if len(sample_info) > 8 else "")
-                    log(
-                        f"WARNING: beacon {b_norm.replace(':','')} low features non_nan={non_nan} "
-                        f"(seen_gw={sum(1 for g in gateway_macs if st.counts[b_norm][g]>0)}) [{preview}]"
-                    )
-
-                out_row: Dict[str, Any] = {
-                    "mac": r["mac"],   # MAC sempre compatto, senza ':'
-                    "x": float(r["x"]),
-                    "y": float(r["y"]),
-                    "z": float(r["z"]),
-                }
-                out_row.update(feats)
-                out_rows.append(out_row)
-
-            written = []
-            for out_row in out_rows:
-                # Nome file: Z_X_Y.csv (Z, X, Y presi dal job)
-                zt = _coord_token(out_row.get("z"))
-                xt = _coord_token(out_row.get("x"))
-                yt = _coord_token(out_row.get("y"))
-                base_name = f"{zt}_{xt}_{yt}.csv"
-                out_path = samples_dir / base_name
-                write_samples_csv(out_path, [out_row], gateway_macs, delimiter=csv_delimiter, rssi_decimals=rssi_decimals)
-                written.append(out_path.name)
-
-            job_path.rename(done_dir / job_path.name)
-            if written:
-                shown = ", ".join(written[:10])
-                more = "" if len(written) <= 10 else f" (+{len(written)-10} altri)"
-                log(f"TRAIN job DONE: wrote {len(written)} sample files to {samples_dir}: {shown}{more}")
-            else:
-                log(f"TRAIN job DONE: no output rows (empty job?)")
-
-        except Exception as e:
-            log(f"TRAIN job ERROR: {job_name} err={type(e).__name__}: {e}")
-            try:
-                job_path.rename(error_dir / job_path.name)
-            except Exception:
-                pass
-            time.sleep(0.5)
-
+        time.sleep(10)
 
 def main() -> None:
-    settings = load_settings()
-    cfg_file = settings.get("_config_file", "")
-    keys = [k for k in settings.keys() if not str(k).startswith("_")]
-    log(f"Settings loaded from {cfg_file}. Keys: {keys}")
-    log(f"BUILD: {build_info()}")
-
-    mode = str(settings.get("mode", "collect_train")).strip().lower()
-
-    if mode == "collect_train":
-        run_collect_train(settings)
-        return
-
-    if mode == "train":
-        from .train_mode import run_train
-        run_train(settings)
-        return
-
-    if mode == "infer":
-        from .infer_mode import run_infer
-        run_infer(settings)
-        return
-
-    raise ValueError(f"unknown mode: {mode}")
+    # Fix percorsi configurazione
+    if not os.environ.get("CONFIG"):
+        os.environ["CONFIG"] = "/config/config.yaml"
 
+    try:
+        settings = load_settings()
+        setup_global_logging(settings)
+        log(f"Core Orchestrator avviato. BUILD: {build_info()}")
+        
+        mode = str(settings.get("mode", "all")).strip().lower()
+
+        if mode == "all":
+            run_all_modes(settings)
+        elif mode == "collect_train":
+            from .train_collect import run_collect_train
+            run_collect_train(settings)
+        elif mode == "infer":
+            from .infer_mode import run_infer
+            run_infer(settings)
+        elif mode == "train":
+            # Questa rimane la vecchia modalità manuale one-shot
+            from .train_mode import run_train
+            run_train(settings)
+        else:
+            log(f"Modalità {mode} non riconosciuta.")
+            
+    except Exception as e:
+        print(f"ERRORE CRITICO ALL'AVVIO: {e}")
+        import traceback
+        traceback.print_exc()
 
 if __name__ == "__main__":
     main()

app/map_manager.py

@@ -33,15 +33,29 @@ def force_save_json(path, data):
 
 def show_mapper(cfg):
     MAPS_DIR = Path(cfg['maps']['map_dir'])
-    SAMPLES_DIR = Path("/data/train/samples")
+    
+    # Riferimenti directory per Addestramento e Test
+    TRAIN_BASE = Path("/data/train")
+    SAMPLES_DIR = TRAIN_BASE / "samples"
+    TEST_SAMPLES_DIR = TRAIN_BASE / "testsamples"
+    
     JOBS_BASE = Path(cfg['collect_train']['jobs_dir'])
     PENDING_DIR = JOBS_BASE / "pending"
     ERROR_DIR = JOBS_BASE / "error"
+    
+    TEST_PENDING_DIR = TRAIN_BASE / "testjobs/pending"
+    TEST_ERROR_DIR = TRAIN_BASE / "testjobs/error"
+    
+    # File configurazione
     BEACONS_FILE = "/data/config/beacons.csv"
-    [p.mkdir(parents=True, exist_ok=True) for p in [MAPS_DIR, SAMPLES_DIR, PENDING_DIR, ERROR_DIR]]
+    GROUPS_FILE = "/data/config/beacongroup.csv"
+    CSV_DELIM = cfg.get('paths', {}).get('csv_delimiter', ';')
+
+    # Creazione directory se mancano
+    for p in [MAPS_DIR, SAMPLES_DIR, TEST_SAMPLES_DIR, PENDING_DIR, ERROR_DIR, TEST_PENDING_DIR, TEST_ERROR_DIR]:
+        p.mkdir(parents=True, exist_ok=True)
 
     # --- 1. GESTIONE UPLOAD ---
-    # La logica ora gestisce stringhe per floor_id permettendo il carattere "-"
     maps = sorted([f.replace(cfg['maps']['floor_prefix'], "").split('.')[0] 
                    for f in os.listdir(MAPS_DIR) if f.startswith(cfg['maps']['floor_prefix'])])
     
@@ -52,7 +66,6 @@ def show_mapper(cfg):
         if st.button("🚀 SALVA PIANO", use_container_width=True):
             if new_f_id and up_file:
                 ext = Path(up_file.name).suffix
-                # Il nome file includerà il "-" se presente in new_f_id
                 img_save_path = MAPS_DIR / f"{cfg['maps']['floor_prefix']}{new_f_id}{ext}"
                 with open(img_save_path, "wb") as f:
                     f.write(up_file.getbuffer())
@@ -85,22 +98,13 @@ def show_mapper(cfg):
     with c_s: st.info(f"📏 Scala: {'✅' if meta['calibrated'] else '❌'}\n({meta['pixel_ratio']:.4f} px/cm)")
     with c_o: st.info(f"🎯 Origine: {'✅' if meta['origin'] != [0,0] else '❌'}\n(X:{meta['origin'][0]}, Y:{meta['origin'][1]})")
 
-    if not meta["calibrated"] or meta["origin"] == [0, 0]:
-        st.warning(f"💡 **Piano {floor_id} da configurare**: Esegui **Calibra** e imposta l'**Origine**.")
-
-    # --- 3. IMPOSTAZIONI GLOBALI (Default disabilitati) ---
+    # --- 3. IMPOSTAZIONI GLOBALI ---
     st.markdown("---")
     g1, g2, g3, g4 = st.columns([1.2, 1.5, 1.2, 2])
-    with g1: 
-        # Griglia disattivata di default per evitare interferenze iniziali
-        show_grid = st.toggle("Griglia", value=False, key="grid_v21")
-    with g2: 
-        grid_cm = st.select_slider("Passo (cm):", options=[25, 50, 100, 200], value=50, key="step_v21")
-    with g3: 
-        # Stay Grid disattivato di default
-        snap_on = st.toggle("Stay Grid", value=False, key="snap_v21")
-    with g4: 
-        m_size = st.slider("Dimensione Marker:", 5, 20, 8, key="msize_v21")
+    with g1: show_grid = st.toggle("Griglia", value=False, key="grid_v21")
+    with g2: grid_cm = st.select_slider("Passo (cm):", options=[25, 50, 100, 200], value=50, key="step_v21")
+    with g3: snap_on = st.toggle("Stay Grid", value=False, key="snap_v21")
+    with g4: m_size = st.slider("Dimensione Marker:", 5, 20, 8, key="msize_v21")
     st.markdown("---")
 
     # --- 4. TOOLSET ---
@@ -121,12 +125,10 @@ def show_mapper(cfg):
         m.options.update({"minZoom": -6, "maxZoom": 6, "zoomSnap": 0.25, "maxBounds": bounds, "maxBoundsViscosity": 1.0})
         folium.raster_layers.ImageOverlay(image=img_data, bounds=bounds, interactive=True).add_to(m)
 
-        # ORIGINE (Sempre visibile se impostata)
         if meta["origin"] != [0, 0]:
             ox, oy = meta["origin"]
-            folium.CircleMarker(location=[oy, ox], radius=6, color="black", fill=True, zindex=1000, tooltip="Origine (0,0)").add_to(m)
+            folium.CircleMarker(location=[oy, ox], radius=6, color="black", fill=True, zindex=1000).add_to(m)
 
-        # GRIGLIA
         if show_grid and meta["calibrated"] and meta["origin"] != [0, 0]:
             px_step = grid_cm * meta["pixel_ratio"]
             ox, oy = meta["origin"]
@@ -135,34 +137,73 @@ def show_mapper(cfg):
             for y in sorted(list(range(int(oy), h, int(px_step))) + list(range(int(oy), 0, -int(px_step)))):
                 folium.PolyLine([[y, 0], [y, w]], color="blue", weight=1, opacity=0.1).add_to(m)
 
-        # Disegno Punti Storici
-        def draw_points(directory, color, shape="circle"):
+        # --- DISEGNO PUNTI STORICI (OTTIMIZZATO TRAMITE NOME FILE) ---
+        def draw_historical_samples(directory, color, shape="circle", is_test=False):
+            if not meta["calibrated"] or meta["origin"] == [0,0]: return
+            for f in Path(directory).glob("*.csv"):
+                try:
+                    # Parsing nome file: CAMPAGNA_Z_X_Y.csv
+                    # Usiamo parts[-1], parts[-2], parts[-3] per gestire campagne con underscore
+                    parts = f.stem.split('_')
+                    if len(parts) < 4: continue
+                    
+                    fz = parts[-3] # Piano Z
+                    fx = float(parts[-2].replace(',', '.')) # Coordinata X
+                    fy = float(parts[-1].replace(',', '.')) # Coordinata Y
+                    
+                    if str(fz) == str(floor_id):
+                        px_x = (fx * meta["pixel_ratio"]) + meta["origin"][0]
+                        px_y = meta["origin"][1] - (fy * meta["pixel_ratio"])
+                        
+                        dash = "5, 5" if is_test else None
+                        weight = 3 if is_test else 1
+                        
+                        if shape == "circle":
+                            folium.CircleMarker(
+                                location=[px_y, px_x], radius=m_size, color=color, 
+                                weight=weight, dash_array=dash, fill=True, fill_opacity=0.7
+                            ).add_to(m)
+                except: continue
+
+        # 1. ADDESTRAMENTO: Verde, Tondo, Solido
+        draw_historical_samples(SAMPLES_DIR, "green", "circle", is_test=False)
+        
+        # 2. TEST: Celeste (#00FFFF), Tondo, Tratteggiato
+        draw_historical_samples(TEST_SAMPLES_DIR, "#00FFFF", "circle", is_test=True)
+
+        # 3. PENDING/ERROR: (Manteniamo la lettura CSV per questi perché i nomi file sono diversi)
+        def draw_jobs(directory, color, shape="diamond", is_test=False):
             if not meta["calibrated"] or meta["origin"] == [0,0]: return
             for f in Path(directory).glob("*.csv"):
                 try:
                     df = pd.read_csv(f, sep=";")
                     df.columns = [c.lower() for c in df.columns]
-                    # Confronto tra stringhe per gestire piani negativi e ID complessi
-                    if str(df.iloc[0].get('z', df.iloc[0].get('floor'))) == str(floor_id):
-                        px_x = (df.iloc[0]['x'] * meta["pixel_ratio"]) + meta["origin"][0]
-                        px_y = meta["origin"][1] - (df.iloc[0]['y'] * meta["pixel_ratio"])
-                        if shape == "circle": folium.CircleMarker(location=[px_y, px_x], radius=m_size, color=color, fill=True, fill_opacity=0.8).add_to(m)
-                        elif shape == "square": folium.RegularPolygonMarker(location=[px_y, px_x], number_of_sides=4, radius=m_size, color="black", weight=2, fill=True, fill_color=color).add_to(m)
-                        elif shape == "diamond": folium.RegularPolygonMarker(location=[px_y, px_x], number_of_sides=4, rotation=45, radius=m_size, color="black", weight=1, fill=True, fill_color=color).add_to(m)
+                    row = df.iloc[0]
+                    z_val = str(row.get('z', row.get('floor')))
+                    if z_val == str(floor_id):
+                        px_x = (row['x'] * meta["pixel_ratio"]) + meta["origin"][0]
+                        px_y = meta["origin"][1] - (row['y'] * meta["pixel_ratio"])
+                        dash = "3, 3" if is_test else None
+                        folium.RegularPolygonMarker(
+                            location=[px_y, px_x], number_of_sides=4, 
+                            rotation=45 if shape=="diamond" else 0,
+                            radius=m_size, color=color, weight=2, 
+                            dash_array=dash, fill=True
+                        ).add_to(m)
                 except: continue
 
-        draw_points(SAMPLES_DIR, "green", "circle")
-        draw_points(PENDING_DIR, "yellow", "diamond")
-        draw_points(ERROR_DIR, "red", "square")
+        draw_jobs(PENDING_DIR, "orange", "diamond", False)
+        draw_jobs(TEST_PENDING_DIR, "#f1c40f", "diamond", True)
+        draw_jobs(ERROR_DIR, "red", "square", False)
+        draw_jobs(TEST_ERROR_DIR, "#e74c3c", "square", True)
 
-        # Feedback Visivo Click
         if tool == "Calibra" and st.session_state.get("cal_points"):
             for p in st.session_state.cal_points: folium.CircleMarker(location=p, radius=6, color="red", fill=True).add_to(m)
             if len(st.session_state.cal_points) == 2: folium.PolyLine(st.session_state.cal_points, color="red", weight=3).add_to(m)
         elif st.session_state.get("temp_lat") is not None:
             folium.CircleMarker(location=[st.session_state.temp_lat, st.session_state.temp_lng], radius=m_size+2, color="blue", fill=True, zindex=500).add_to(m)
 
-        out = st_folium(m, height=600, width=None, key=f"map_v21_{floor_id}_{tool}")
+        out = st_folium(m, height=600, width=None, key=f"map_v24_{floor_id}_{tool}")
         click = out.get("last_clicked")
         if click:
             clat, clng = click["lat"], click["lng"]
@@ -181,9 +222,6 @@ def show_mapper(cfg):
     # --- 6. LOGICA UI ---
     with col_ui:
         st.write(f"### Tool: **{tool}**")
-        err_files = list(ERROR_DIR.glob("*.csv"))
-        if err_files and st.button(f"🗑️ PULISCI ERRORI", use_container_width=True):
-            for f in err_files: os.remove(f); st.rerun()
         
         if tool == "Calibra":
             pts = st.session_state.get("cal_points", [])
@@ -201,17 +239,57 @@ def show_mapper(cfg):
                 if st.button("💾 SALVA ORIGINE", use_container_width=True, type="primary"):
                     meta["origin"] = [int(px_x), int(px_y)]
                     if force_save_json(meta_path, meta): st.rerun()
+            
             elif tool == "Rileva" and meta["calibrated"]:
                 rx = (px_x - meta["origin"][0]) / meta["pixel_ratio"]
                 ry = (meta["origin"][1] - px_y) / meta["pixel_ratio"]
                 sx, sy = int(round(rx)), int(round(ry))
                 st.metric("X (cm)", sx); st.metric("Y (cm)", sy)
-                if os.path.exists(BEACONS_FILE):
-                    b_df = pd.read_csv(BEACONS_FILE, sep=";")
-                    sel_b = st.selectbox("Beacon:", b_df.apply(lambda x: f"{x['BeaconName']} | {x['MAC']}", axis=1))
-                    if st.button("🚀 REGISTRA", use_container_width=True, type="primary"):
-                        b_name, b_mac = sel_b.split(" | ")
-                        id_str = f"{b_name}_{floor_id}_{sx}_{sy}"
-                        # Salvataggio Z come stringa per mantenere il "-"
-                        data = {"Position": id_str, "Floor": floor_id, "RoomName": b_name, "X": sx, "Y": sy, "Z": floor_id, "BeaconName": b_name, "MAC": b_mac}
-                        pd.DataFrame([data]).to_csv(PENDING_DIR / f"{id_str}.csv", sep=";", index=False); st.rerun()
+
+                st.divider()
+                job_mode = st.radio("Scopo Rilevamento:", ["Addestramento", "Test"], horizontal=True, key="job_mode_v24")
+                
+                b_df = pd.read_csv(Path(BEACONS_FILE), sep=CSV_DELIM) if Path(BEACONS_FILE).exists() else pd.DataFrame(columns=['BeaconName','MAC'])
+                beacon_name_map = {row['MAC']: row['BeaconName'] for _, row in b_df.iterrows()}
+
+                options = [f"Beacon: {n} | {m}" for n, m in zip(b_df['BeaconName'], b_df['MAC'])]
+                if Path(GROUPS_FILE).exists():
+                    g_df = pd.read_csv(Path(GROUPS_FILE), sep=CSV_DELIM)
+                    options += [f"Gruppo: {n}" for n in g_df['BeaconGroupName']]
+
+                sel_target = st.selectbox("Seleziona Target:", options, key="sel_target_v24")
+                
+                if st.button("🚀 REGISTRA JOB", use_container_width=True, type="primary"):
+                    sub_dir = "jobs" if job_mode == "Addestramento" else "testjobs"
+                    current_pending = Path(f"/data/train/{sub_dir}/pending")
+                    current_pending.mkdir(parents=True, exist_ok=True)
+
+                    job_rows = []
+                    if sel_target.startswith("Gruppo: "):
+                        g_name = sel_target.replace("Gruppo: ", "")
+                        g_df = pd.read_csv(Path(GROUPS_FILE), sep=CSV_DELIM)
+                        macs_str = g_df[g_df['BeaconGroupName'] == g_name]['GroupMAC'].iloc[0]
+                        mac_list = [m.strip() for m in macs_str.split(',')]
+                        job_filename = f"{g_name}_{floor_id}_{sx}_{sy}.csv"
+                        for m in mac_list:
+                            b_name = beacon_name_map.get(m, g_name)
+                            pos_id = f"{b_name}_{floor_id}_{sx}_{sy}"
+                            job_rows.append({"Position": pos_id, "Floor": floor_id, "RoomName": b_name, "X": sx, "Y": sy, "Z": floor_id, "BeaconName": b_name, "MAC": m})
+                    else:
+                        b_info = sel_target.replace("Beacon: ", "").split(" | ")
+                        b_name, b_mac = b_info[0], b_info[1]
+                        job_filename = f"{b_name}_{floor_id}_{sx}_{sy}.csv"
+                        pos_id = f"{b_name}_{floor_id}_{sx}_{sy}"
+                        job_rows.append({"Position": pos_id, "Floor": floor_id, "RoomName": b_name, "X": sx, "Y": sy, "Z": floor_id, "BeaconName": b_name, "MAC": b_mac})
+
+                    header = ["Position", "Floor", "RoomName", "X", "Y", "Z", "BeaconName", "MAC"]
+                    pd.DataFrame(job_rows)[header].to_csv(current_pending / job_filename, sep=CSV_DELIM, index=False)
+                    st.success(f"Job registrato!")
+                    time.sleep(0.5)
+                    st.rerun()
+
+    # Pulizia errori nel menu laterale
+    err_files = list(ERROR_DIR.glob("*.csv")) + list(TEST_ERROR_DIR.glob("*.csv"))
+    if err_files and st.sidebar.button(f"🗑️ PULISCI ERRORI ({len(err_files)})"):
+        for f in err_files: os.remove(f)
+        st.rerun()

app/normalize.py

@@ -1,46 +1,24 @@
-"""normalize.py
-
-Helper per normalizzare i MAC address in due formati:
-
-- compact: 12 hex uppercase senza separatori (es: AC233FC1DD3C)
-- colon:   6 byte separati da ":" (es: AC:23:3F:C1:DD:3C)
-
-Nota: nel progetto usiamo **compact** come chiave interna per fare matching tra:
-- gateway.csv (spesso con i ":")
-- topic MQTT (publish_out/<gw> in genere senza ":")
-- payload JSON (mac beacon/gateway spesso senza ":")
-
-Il formato colon rimane utile solo per *rendering* (es: header CSV).
-"""
-
-from __future__ import annotations
-
 import re
 
-_HEX_ONLY = re.compile(r"[^0-9A-Fa-f]")
-
-
-def mac_to_compact(mac: object) -> str:
-    """Ritorna 12 hex uppercase senza separatori."""
-    if mac is None:
+def norm_mac(mac: str) -> str:
+    """Ritorna MAC come AA:BB:CC:DD:EE:FF (upper), ignorando separatori."""
+    if not mac:
         return ""
-    s = _HEX_ONLY.sub("", str(mac)).upper()
-    if len(s) == 12:
-        return s
-    if len(s) > 12:
-        # se arriva qualcosa di più lungo (es. UUID), prendiamo gli ultimi 12
-        return s[-12:]
-    return s
-
-
-def compact_to_colon(mac12: object) -> str:
-    """Converte 'AC233FC1DD3C' -> 'AC:23:3F:C1:DD:3C'."""
-    s = mac_to_compact(mac12)
-    if len(s) != 12:
-        return s
-    return ":".join(s[i : i + 2] for i in range(0, 12, 2))
-
-
-def norm_mac(mac: object) -> str:
-    """Alias storico: ritorna il formato con ':'."""
-    return compact_to_colon(mac)
+    m = str(mac).strip().replace("-", "").replace(":", "").replace(".", "")
+    m = m.upper()
+    if len(m) != 12:
+        return mac.strip().upper()
+    return ":".join(m[i:i+2] for i in range(0, 12, 2))
+
+def is_mac(mac: str) -> bool:
+    """Verifica se la stringa è un MAC valido."""
+    if not mac: return False
+    m = str(mac).strip().replace("-", "").replace(":", "").replace(".", "")
+    return len(m) == 12 and all(c in "0123456789ABCDEFabcdef" for c in m)
+
+# Alias per compatibilità con gli altri script (gateways.py e train_collect.py)
+normalize_mac = norm_mac
+
+def mac_to_compact(mac: str) -> str:
+    """Ritorna MAC senza separatori (es: AC233FC1DD3C)."""
+    return norm_mac(mac).replace(":", "").upper()

app/settings.py

@@ -16,7 +16,7 @@ def deep_merge(a: dict, b: dict) -> dict:
     return out
 
 def load_settings() -> dict:
-    cfg_path = os.getenv("CONFIG_FILE", "/app/config/config.yaml")
+    cfg_path = os.getenv("CONFIG_FILE", "/config/config.yaml")
     settings = _read_yaml(cfg_path)
 
     secrets_path = os.getenv("SECRETS_FILE", "")

app/train_collect.py

@@ -1,349 +1,147 @@
-"""train_collect.py
-
-Modalità COLLECT_TRAIN:
-- attende che tutti i gateway del feature-set (gateway.csv) siano online (traffic MQTT)
-- prende job CSV da jobs_dir/pending/*.csv
-- per ogni job: apre una finestra di raccolta di window_seconds, aggrega RSSI per GW
-- scrive sample CSV in samples_dir
-
-Bugfix fondamentale (per i tuoi NAN):
-- matching interno su MAC in formato **compact** (12 hex senza ':').
-"""
-
-from __future__ import annotations
-
 import os
 import time
 import shutil
-import glob
-import math
-import datetime
-from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple
-
+import ssl
+from pathlib import Path
+from typing import Dict, Any, List
 import pandas as pd
+import paho.mqtt.client as mqtt
 
-from .normalize import mac_to_compact, compact_to_colon
-from .mqtt_client import MqttSubscriber
+from .normalize import norm_mac, mac_to_compact
 from .mqtt_parser import parse_publish_out
-from .fingerprint import FingerprintWindow
+from .fingerprint import FingerprintWindow 
 from .logger_utils import log_msg as log
-
-
-def _ensure_dir(path: str) -> None:
-    os.makedirs(path, exist_ok=True)
-
-
-def _read_delimited_csv(path: str, prefer_delim: str = ";") -> pd.DataFrame:
-    for sep in [prefer_delim, ",", "\t"]:
-        try:
-            df = pd.read_csv(path, sep=sep, dtype=str, keep_default_na=False)
-            if len(df.columns) >= 1:
-                return df
-        except Exception:
-            continue
-    return pd.read_csv(path, dtype=str, keep_default_na=False)
-
-
-def load_gateway_csv(path: str, delimiter: str = ";") -> Tuple[List[str], List[str]]:
-    df = _read_delimited_csv(path, prefer_delim=delimiter)
-    if df.empty:
-        return [], []
-
-    mac_col = None
-    for c in df.columns:
-        if c.strip().lower() == "mac":
-            mac_col = c
-            break
-    if mac_col is None:
-        mac_col = df.columns[0]
-
-    headers: List[str] = []
-    keys: List[str] = []
-    seen = set()
-    invalid = 0
-    dup = 0
-
-    for raw in df[mac_col].tolist():
-        k = mac_to_compact(raw)
-        if len(k) != 12:
-            invalid += 1
-            continue
-        if k in seen:
-            dup += 1
-            continue
-        seen.add(k)
-        keys.append(k)
-        headers.append(compact_to_colon(k))
-
-    log(f"[gateway.csv] loaded gateways={len(keys)} invalid={invalid} duplicates={dup}")
-    return headers, keys
-
-
-@dataclass
-class TrainTarget:
-    mac: str  # compact
-    x: float
-    y: float
-    z: float
-
-
-def read_job_csv(job_path: str, delimiter: str = ";") -> List[TrainTarget]:
-    df = _read_delimited_csv(job_path, prefer_delim=delimiter)
-    if df.empty:
-        return []
-
-    cols = {c.strip().lower(): c for c in df.columns}
-
-    def col(name: str) -> Optional[str]:
-        return cols.get(name)
-
-    mac_c = col("mac")
-    x_c = col("x")
-    y_c = col("y")
-    z_c = col("z")
-    if not mac_c:
-        raise ValueError(f"Job CSV senza colonna 'mac': {job_path}")
-
-    out: List[TrainTarget] = []
-    for _, row in df.iterrows():
-        m = mac_to_compact(row[mac_c])
-        if len(m) != 12:
-            continue
-        x = float(row[x_c]) if x_c else 0.0
-        y = float(row[y_c]) if y_c else 0.0
-        z = float(row[z_c]) if z_c else 0.0
-        out.append(TrainTarget(mac=m, x=x, y=y, z=z))
-    return out
-
-
-def _pick_collect_cfg(settings: Dict) -> Dict:
-    if "collect_train" in settings and isinstance(settings["collect_train"], dict):
-        return settings["collect_train"]
-    if "training" in settings and isinstance(settings["training"], dict):
-        log("WARNING: config usa 'training:' (alias). Consiglio: rinomina in 'collect_train:'")
-        return settings["training"]
-    return {}
-
-
-def run_collect_train(settings: Dict) -> None:
-    ct = _pick_collect_cfg(settings)
-    paths = settings.get("paths", {}) or {}
-    mqtt_cfg = settings.get("mqtt", {}) or {}
-    dbg = settings.get("debug", {}) or {}
-
-    jobs_dir = str(ct.get("jobs_dir", "/data/train/jobs"))
-    samples_dir = str(ct.get("samples_dir", "/data/train/samples"))
-    job_glob = str(ct.get("job_glob", "*.csv"))
-    poll_seconds = float(ct.get("poll_seconds", ct.get("poll_pending_seconds", 2)))
-    window_seconds = float(ct.get("window_seconds", 10))
-    min_non_nan = int(ct.get("min_non_nan", 3))
-
-    aggregate = str(ct.get("aggregate", "median")).lower()
-    rssi_min = float(ct.get("rssi_min", -110))
-    rssi_max = float(ct.get("rssi_max", -25))
-    outlier_method = str(ct.get("outlier_method", "none")).lower()
-    mad_z = float(ct.get("mad_z", 3.5))
-    min_samples_per_gateway = int(ct.get("min_samples_per_gateway", 1))
-    max_stddev = ct.get("max_stddev", None)
-    max_stddev = float(max_stddev) if max_stddev is not None else None
-
-    gateway_ready_max_age_seconds = float(ct.get("gateway_ready_max_age_seconds", 30))
-    gw_ready_log_seconds = float(ct.get("gw_ready_log_seconds", 10))
-    gw_ready_sleep_seconds = float(ct.get("gw_ready_sleep_seconds", 5))
-    gw_ready_check_before_job = bool(ct.get("gw_ready_check_before_job", True))
-
-    csv_delim = str(paths.get("csv_delimiter", ";"))
-    gateway_csv = str(paths.get("gateways_csv", "/data/config/gateway.csv"))
-
-    # Debug opzionale durante finestra
-    log_progress = bool(dbg.get("collect_train_log_samples", False))
-    log_first_seen = bool(dbg.get("collect_train_log_first_seen", False))
-    log_every_s = float(dbg.get("collect_train_log_every_seconds", 15))
-
-    pending_dir = os.path.join(jobs_dir, "pending")
-    done_dir = os.path.join(jobs_dir, "done")
-    error_dir = os.path.join(jobs_dir, "error")
-
-    _ensure_dir(pending_dir)
-    _ensure_dir(done_dir)
-    _ensure_dir(error_dir)
-    _ensure_dir(samples_dir)
-
-    gateway_headers, gateway_keys = load_gateway_csv(gateway_csv, delimiter=csv_delim)
-    if not gateway_keys:
-        log("ERROR: Nessun gateway valido nel gateway.csv -> non posso partire.")
+from .gateways import load_gateway_csv
+from .beacons import read_job_csv, write_samples_csv, _coord_token
+
+def extract_campaign_id(beacon_name: str) -> str:
+    """Estrae la campagna dal nome beacon (es. BC-00-41 -> 00)."""
+    if not beacon_name: return "default"
+    parts = str(beacon_name).split('-')
+    if len(parts) >= 2:
+        return parts[1]
+    return "default"
+
+def run_collect_train(settings: Dict[str, Any]) -> None:
+    cfg = settings.get("collect_train", {})
+    paths = settings.get("paths", {})
+    mqtt_cfg = settings.get("mqtt", {})
+    
+    modes = [
+        {"pending": Path("/data/train/jobs/pending"), "done": Path("/data/train/jobs/done"), "samples": Path("/data/train/samples"), "label": "TRAIN"},
+        {"pending": Path("/data/train/testjobs/pending"), "done": Path("/data/train/testjobs/done"), "samples": Path("/data/train/testsamples"), "label": "TEST"}
+    ]
+    for m in modes:
+        for d in [m["pending"], m["done"], m["samples"]]: d.mkdir(parents=True, exist_ok=True)
+
+    gateway_csv_path = paths.get("gateways_csv", "/data/config/gateway.csv")
+    csv_delim = paths.get("csv_delimiter", ";")
+    
+    try:
+        gw_df = pd.read_csv(gateway_csv_path, sep=csv_delim)
+        gw_df.columns = [c.strip().lower() for c in gw_df.columns]
+        gateway_headers = gw_df['mac'].tolist()
+        gateway_compacts = [mac_to_compact(m) for m in gateway_headers]
+        total_gw = len(gateway_headers)
+    except Exception as e:
+        log(f"ERRORE caricamento gateway: {e}")
         return
 
-    mqtt_host = str(mqtt_cfg.get("host", "mosquitto"))
-    mqtt_port = int(mqtt_cfg.get("port", 1883))
-    mqtt_topic = str(mqtt_cfg.get("topic", "publish_out/#"))
-    mqtt_proto = str(mqtt_cfg.get("protocol", "mqttv311")).lower()
-    client_id = str(mqtt_cfg.get("client_id", "ble-ai-localizer"))
-    keepalive = int(mqtt_cfg.get("keepalive", 60))
-    qos = int(mqtt_cfg.get("qos", 0))
-    username = str(mqtt_cfg.get("username", ""))
-    password = str(mqtt_cfg.get("password", ""))
-
-    last_seen: Dict[str, float] = {}
-    active_window: Optional[FingerprintWindow] = None
-    active_logged_pairs: set = set()
-
-    def on_mqtt_message(topic: str, payload: bytes) -> None:
-        nonlocal active_window, active_logged_pairs
-        events = parse_publish_out(topic, payload)
-        now = time.time()
-        for gw_key, b_key, rssi, _ts in events:
-            if len(gw_key) == 12:
-                last_seen[gw_key] = now
-
-            if active_window is None:
-                continue
-
-            accepted = active_window.add(gw_key, b_key, rssi)
-            if accepted and log_first_seen:
-                pair = (b_key, gw_key)
-                if pair not in active_logged_pairs:
-                    active_logged_pairs.add(pair)
-                    log(f"SEEN target beacon={b_key} gw={compact_to_colon(gw_key)} rssi={rssi:.1f}")
-
-    sub = MqttSubscriber(
-        host=mqtt_host,
-        port=mqtt_port,
-        topic=mqtt_topic,
-        mqtt_proto=mqtt_proto,
-        client_id=client_id,
-        keepalive=keepalive,
-        qos=qos,
-        username=username if username else None,
-        password=password if password else None,
-    )
-
-    import threading
-    t = threading.Thread(target=sub.start_forever, args=(on_mqtt_message,), daemon=True)
-    t.start()
-
-    log("MQTT thread started (collect_train)")
-    log(
-        f"COLLECT_TRAIN config: gateway_csv={gateway_csv} gateways(feature-set)={len(gateway_keys)} "
-        f"window_seconds={window_seconds:.1f} poll_seconds={poll_seconds:.1f} "
-        f"jobs_dir={jobs_dir} pending_dir={pending_dir} done_dir={done_dir} error_dir={error_dir} "
-        f"samples_dir={samples_dir} mqtt={mqtt_host}:{mqtt_port} topic={mqtt_topic}"
-    )
-
-    def gateways_online() -> Tuple[int, List[str]]:
-        now = time.time()
-        missing: List[str] = []
-        for gk, hdr in zip(gateway_keys, gateway_headers):
-            last = last_seen.get(gk)
-            if last is None or (now - last) > gateway_ready_max_age_seconds:
-                missing.append(hdr)
-        return len(missing), missing
-
-    def wait_for_gateways() -> None:
-        last_log = 0.0
-        while True:
-            miss_n, missing = gateways_online()
-            if miss_n == 0:
-                log(f"GW READY: online={len(gateway_keys)}/{len(gateway_keys)} (max_age_s={gateway_ready_max_age_seconds:.1f})")
-                return
-            now = time.time()
-            if now - last_log >= gw_ready_log_seconds:
-                last_log = now
-                log(
-                    f"WAIT gateways online ({miss_n} missing, seen={len(gateway_keys)-miss_n}/{len(gateway_keys)}): {missing} "
-                    f"(max_age_s={gateway_ready_max_age_seconds:.1f})"
-                )
-            time.sleep(gw_ready_sleep_seconds)
+    # --- LOGICA GATEWAY ONLINE ---
+    online_tracker = {mac_to_compact(m): {"original": m, "online": False} for m in gateway_headers}
+    def on_check_message(client, userdata, msg):
+        events = parse_publish_out(msg.topic, msg.payload)
+        for gw_c, _, _, _ in events:
+            if gw_c in online_tracker and not online_tracker[gw_c]["online"]:
+                online_tracker[gw_c]["online"] = True
+                curr = sum(1 for v in online_tracker.values() if v["online"])
+                log(f"  [{curr}/{total_gw}] ONLINE: {online_tracker[gw_c]['original']}")
+
+    check_client = mqtt.Client(protocol=mqtt.MQTTv311)
+    if mqtt_cfg.get("username"): check_client.username_pw_set(mqtt_cfg["username"], mqtt_cfg.get("password"))
+    check_client.on_message = on_check_message
+    check_client.connect(mqtt_cfg.get("host", "localhost"), mqtt_cfg.get("port", 1883))
+    check_client.subscribe(mqtt_cfg.get("topic", "publish_out/#"))
+    check_client.loop_start()
+    while not all(v["online"] for v in online_tracker.values()): time.sleep(1)
+    check_client.loop_stop()
+
+    log("Collector pronto. Monitoraggio directory Job...")
 
     while True:
-        jobs = sorted(glob.glob(os.path.join(pending_dir, job_glob)))
-        if not jobs:
-            time.sleep(poll_seconds)
-            continue
-
-        for job_path in jobs:
-            job_name = os.path.basename(job_path)
-            try:
-                if gw_ready_check_before_job:
-                    wait_for_gateways()
-
-                targets = read_job_csv(job_path, delimiter=csv_delim)
-                if not targets:
-                    raise RuntimeError("job CSV vuoto o senza MAC validi")
-
-                beacon_keys = [t.mac for t in targets]
-                log(f"TRAIN job START: {job_name} beacons={len(beacon_keys)}")
-
-                active_logged_pairs = set()
-                active_window = FingerprintWindow(
-                    beacon_keys=beacon_keys,
-                    gateway_headers=gateway_headers,
-                    gateway_keys=gateway_keys,
-                    rssi_min=rssi_min,
-                    rssi_max=rssi_max,
-                    outlier_method=outlier_method,
-                    mad_z=mad_z,
-                    min_samples_per_gateway=min_samples_per_gateway,
-                    max_stddev=max_stddev,
-                )
-
-                t0 = time.time()
-                next_log = t0 + log_every_s
-
-                while True:
-                    elapsed = time.time() - t0
-                    if elapsed >= window_seconds:
-                        break
-                    if log_progress and time.time() >= next_log:
-                        next_log = time.time() + log_every_s
-                        parts = []
-                        for b in beacon_keys:
-                            tops = active_window.top_gateways(b, aggregate=aggregate, top_n=3)
-                            if not tops:
-                                parts.append(f"{b}:0gw")
-                            else:
-                                top_s = ",".join([f"{hdr}({n})" for n, hdr, _agg in tops])
-                                parts.append(f"{b}:{top_s}")
-                        log(f"WINDOW progress {elapsed:.0f}/{window_seconds:.0f}s -> " + " | ".join(parts))
-                    time.sleep(0.25)
-
-                rows: List[Dict[str, object]] = []
-                for tt in targets:
-                    feats = active_window.features_for(tt.mac, aggregate=aggregate)
-                    non_nan = sum(0 if (isinstance(v, float) and math.isnan(v)) else 1 for v in feats.values())
-                    if non_nan < min_non_nan:
-                        log(f"WARNING: beacon {tt.mac} low features non_nan={non_nan}")
-
-                    tops = active_window.top_gateways(tt.mac, aggregate=aggregate, top_n=5)
-                    if tops:
-                        top_s = ", ".join([f"{hdr} n={n} agg={agg:.1f}" for n, hdr, agg in tops])
-                        log(f"SUMMARY beacon {tt.mac}: {top_s}")
-                    else:
-                        log(f"SUMMARY beacon {tt.mac}: no samples captured")
-
-                    row: Dict[str, object] = {"mac": tt.mac, "x": float(tt.x), "y": float(tt.y), "z": float(tt.z)}
-                    row.update(feats)
-                    rows.append(row)
-
-                out_df = pd.DataFrame(rows)
-                cols = ["mac", "x", "y", "z"] + gateway_headers
-                out_df = out_df.reindex(columns=cols)
-
-                epoch = int(time.time())
-                out_name = f"{os.path.splitext(job_name)[0]}__{epoch}.csv"
-                out_path = os.path.join(samples_dir, out_name)
-                out_df.to_csv(out_path, sep=csv_delim, index=False, float_format="%.1f", na_rep="nan")
-
-                log(f"TRAIN job DONE: wrote {out_path} rows={len(out_df)}")
-
-                shutil.move(job_path, os.path.join(done_dir, job_name))
+        job_found = False
+        for m in modes:
+            job_files = sorted(m["pending"].glob("*.csv"))
+            if not job_files: continue
+            
+            job_found = True
+            job_path = job_files[0]
+            log(f"[{m['label']}] Elaborazione: {job_path.name}")
+
+            job_rows = read_job_csv(job_path, delimiter=csv_delim)
+            if not job_rows:
+                job_path.rename(Path("/data/train/jobs/error") / job_path.name)
+                continue
 
-            except Exception as e:
-                log(f"ERROR processing job {job_name}: {e}")
-                try:
-                    shutil.move(job_path, os.path.join(error_dir, job_name))
-                except Exception:
-                    pass
-            finally:
-                active_window = None
+            job_beacon_keys = [mac_to_compact(r["mac"]) for r in job_rows]
+
+            active_window = FingerprintWindow(
+                beacon_keys=job_beacon_keys,
+                gateway_keys=gateway_compacts,
+                gateway_headers=gateway_headers,
+                rssi_min=float(cfg.get("rssi_min", -110)),
+                rssi_max=float(cfg.get("rssi_max", -25)),
+                outlier_method=cfg.get("outlier_method", "mad"),
+                min_samples_per_gateway=int(cfg.get("min_samples_per_gateway", 5))
+            )
+
+            mqtt_timestamps = []
+            def on_job_message(c, u, msg):
+                events = parse_publish_out(msg.topic, msg.payload)
+                for gw_c, b_c, rssi, ts in events:
+                    if b_c in job_beacon_keys:
+                        active_window.add(gw_c, b_c, rssi)
+                        if ts: mqtt_timestamps.append(ts)
+
+            client = mqtt.Client(protocol=mqtt.MQTTv311)
+            if mqtt_cfg.get("username"): client.username_pw_set(mqtt_cfg["username"], mqtt_cfg.get("password"))
+            client.on_message = on_job_message
+            client.connect(mqtt_cfg.get("host", "localhost"), mqtt_cfg.get("port", 1883))
+            client.subscribe(mqtt_cfg.get("topic", "publish_out/#"))
+            client.loop_start()
+            time.sleep(int(cfg.get("window_seconds", 30)))
+            client.loop_stop()
+            client.disconnect()
+
+            ts_start = min(mqtt_timestamps) if mqtt_timestamps else 0
+            ts_end = max(mqtt_timestamps) if mqtt_timestamps else 0
+
+            valid_count = 0
+            for r in job_rows:
+                b_mac = r["mac"]
+                b_compact = mac_to_compact(b_mac)
+                b_name = r.get("beaconname", b_compact)
+                
+                campaign = extract_campaign_id(b_name)
+                feats = active_window.features_for(b_compact, aggregate=cfg.get("aggregate", "median"))
+                
+                if sum(1 for v in feats.values() if v == v and v is not None) >= int(cfg.get("min_non_nan", 3)):
+                    out_row = {
+                        "mac": b_mac, "x": r["x"], "y": r["y"], "z": r["z"],
+                        "ts_start": ts_start, "ts_end": ts_end
+                    }
+                    out_row.update(feats)
+                    
+                    zt, xt, yt = _coord_token(r["z"]), _coord_token(r["x"]), _coord_token(r["y"])
+                    
+                    # NUOVA NOMENCLATURA: $CAMPAGNA_$Z_$X_$Y.csv
+                    out_filename = f"{campaign}_{zt}_{xt}_{yt}.csv"
+                    
+                    write_samples_csv(m["samples"] / out_filename, [out_row], gateway_headers, delimiter=csv_delim, rssi_decimals=int(cfg.get("rssi_decimals", 0)))
+                    valid_count += 1
+            
+            shutil.move(str(job_path), str(m["done"] / job_path.name))
+            log(f"  Job completato. Campioni: {valid_count} (MQTT TS: {ts_start}-{ts_end})")
+            break 
+
+        if not job_found: time.sleep(2)

app/train_executor.py

@@ -0,0 +1,112 @@
+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)

app/train_mode.py

@@ -1,419 +1,85 @@
 # app/train_mode.py
-# Training mode: build hierarchical KNN model (floor classifier + per-floor X/Y regressors)
-# Adds verbose dataset statistics useful for large training runs.
-
-from __future__ import annotations
-
-import glob
 import os
+import glob
 import time
 import math
-from dataclasses import dataclass
-from typing import Any, Callable, Dict, List, Optional, Tuple
-
 import joblib
-from datetime import datetime
 import numpy as np
 import pandas as pd
+from datetime import datetime
 from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
-import sklearn
-
-# NOTE: these are already present in the project
-from .csv_config import load_gateway_features_csv
-from .logger_utils import log_msg as log
-
-
-@dataclass
-class GatewayStats:
-    mac: str
-    total_samples: int = 0      # total rows processed (per sample point)
-    non_missing: int = 0        # non-missing rssi count
-    missing: int = 0            # missing (nan) count
-    sum_: float = 0.0
-    sumsq: float = 0.0
-    min_: float = float("inf")
-    max_: float = float("-inf")
-
-    def add(self, v: float, is_missing: bool) -> None:
-        self.total_samples += 1
-        if is_missing:
-            self.missing += 1
-            return
-        self.non_missing += 1
-        self.sum_ += v
-        self.sumsq += v * v
-        if v < self.min_:
-            self.min_ = v
-        if v > self.max_:
-            self.max_ = v
-
-    def mean(self) -> float:
-        return self.sum_ / self.non_missing if self.non_missing else float("nan")
-
-    def std(self) -> float:
-        if self.non_missing <= 1:
-            return float("nan")
-        mu = self.mean()
-        var = max(0.0, (self.sumsq / self.non_missing) - (mu * mu))
-        return math.sqrt(var)
-
-    def missing_pct(self) -> float:
-        return (self.missing / self.total_samples) * 100.0 if self.total_samples else 0.0
-
-
-def _get(d: Dict[str, Any], key: str, default: Any = None) -> Any:
-    return d.get(key, default) if isinstance(d, dict) else default
-
-
-def _as_bool(v: Any, default: bool = False) -> bool:
-    if v is None:
-        return default
-    if isinstance(v, bool):
-        return v
-    if isinstance(v, (int, float)):
-        return bool(v)
-    s = str(v).strip().lower()
-    return s in ("1", "true", "yes", "y", "on")
-
-
-def _safe_float(v: Any) -> Optional[float]:
-    try:
-        if v is None:
-            return None
-        if isinstance(v, float) and math.isnan(v):
-            return None
-        return float(v)
-    except Exception:
-        return None
 
+# Import assoluti garantiti
+from csv_config import load_gateway_features_csv
+from logger_utils import log_msg as log
 
-def _collect_dataset(
-    sample_files: List[str],
-    gateways_order: List[str],
-    nan_fill: float,
-    log: Callable[[str], None],
-    verbose: bool,
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, Dict[str, GatewayStats], Dict[str, Any]]:
+def run_train(settings, log_fn=None, target_files=None):
     """
-    Build dataset from per-point sample csv files.
-
-    Each sample file is expected to contain:
-      header: mac;x;y;z;<GW1>;<GW2>...
-      1 row:  beacon_mac; x; y; z; rssi_gw1; rssi_gw2; ...
-
-    Returns:
-      X (N, G), y_floor (N,), y_xy (N,2), meta_xy (N,2),
-      gw_stats, global_stats
+    Esegue l'addestramento Hierarchical KNN su un set di file specifico (Campagna).
     """
-    X_rows: List[List[float]] = []
-    y_floor: List[int] = []
-    y_xy: List[List[float]] = []
-    meta_xy: List[List[float]] = []
-
-    gw_stats: Dict[str, GatewayStats] = {gw: GatewayStats(mac=gw) for gw in gateways_order}
-
-    floors_counter: Dict[int, int] = {}
-    bad_files: int = 0
-    missing_cols_files: int = 0
-
-    expected_cols: Optional[List[str]] = None
-
-    for fp in sample_files:
-        try:
-            df = pd.read_csv(fp, sep=";", dtype=str)
-        except Exception as e:
-            bad_files += 1
-            if verbose:
-                log(f"TRAIN WARN: cannot read sample file {fp}: {type(e).__name__}: {e}")
-            continue
-
-        if df.shape[0] < 1:
-            bad_files += 1
-            if verbose:
-                log(f"TRAIN WARN: empty sample file {fp}")
-            continue
-
-        row = df.iloc[0].to_dict()
-
-        if verbose:
-            cols = list(df.columns)
-            if expected_cols is None:
-                expected_cols = cols
-            elif cols != expected_cols:
-                missing_cols_files += 1
-                if missing_cols_files <= 5:
-                    log(f"TRAIN WARN: columns mismatch in {os.path.basename(fp)} (expected {len(expected_cols)} cols, got {len(cols)})")
-
-        x = _safe_float(row.get("x"))
-        y = _safe_float(row.get("y"))
-        z = _safe_float(row.get("z"))
-        if x is None or y is None or z is None:
-            bad_files += 1
-            if verbose:
-                log(f"TRAIN WARN: missing x/y/z in {fp}")
-            continue
-
-        z_i = int(round(z))
-        floors_counter[z_i] = floors_counter.get(z_i, 0) + 1
-
-        feats: List[float] = []
-        for gw in gateways_order:
-            v = row.get(gw)
-            fv = _safe_float(v)
-            if fv is None:
-                feats.append(nan_fill)
-                gw_stats[gw].add(nan_fill, is_missing=True)
-            else:
-                feats.append(fv)
-                gw_stats[gw].add(fv, is_missing=False)
-
-        X_rows.append(feats)
-        y_floor.append(z_i)
-        y_xy.append([x, y])
-        meta_xy.append([x, y])
-
-    if not X_rows:
-        raise RuntimeError("No valid samples found in samples_dir (dataset empty).")
-
-    X = np.asarray(X_rows, dtype=np.float32)
-    y_floor_arr = np.asarray(y_floor, dtype=np.int32)
-    y_xy_arr = np.asarray(y_xy, dtype=np.float32)
-    meta_xy_arr = np.asarray(meta_xy, dtype=np.float32)
-
-    global_stats: Dict[str, Any] = {
-        "samples_total_files": len(sample_files),
-        "samples_used": int(X.shape[0]),
-        "samples_bad": int(bad_files),
-        "floors_counts": dict(sorted(floors_counter.items(), key=lambda kv: kv[0])),
-        "missing_cols_files": int(missing_cols_files),
-        "gateways": int(len(gateways_order)),
-        "nan_fill": float(nan_fill),
-    }
-
-    return X, y_floor_arr, y_xy_arr, meta_xy_arr, gw_stats, global_stats
-
-
-def _log_train_stats(
-    log: Callable[[str], None],
-    X: np.ndarray,
-    y_floor: np.ndarray,
-    y_xy: np.ndarray,
-    gateways_order: List[str],
-    nan_fill: float,
-    gw_stats: Dict[str, GatewayStats],
-    global_stats: Dict[str, Any],
-    top_k: int = 8,
-) -> None:
-    """Human-friendly statistics for training runs."""
-    log(
-        "TRAIN stats: "
-        f"samples_used={global_stats.get('samples_used')} "
-        f"samples_bad={global_stats.get('samples_bad')} "
-        f"files_total={global_stats.get('samples_total_files')} "
-        f"gateways={len(gateways_order)} "
-        f"floors={list(global_stats.get('floors_counts', {}).keys())}"
-    )
-    if global_stats.get("missing_cols_files", 0):
-        log(f"TRAIN stats: files_with_column_mismatch={global_stats['missing_cols_files']} (see earlier WARN lines)")
-
-    xs = y_xy[:, 0]
-    ys = y_xy[:, 1]
-    log(
-        "TRAIN stats: XY range "
-        f"X[min,max]=[{float(np.min(xs)):.2f},{float(np.max(xs)):.2f}] "
-        f"Y[min,max]=[{float(np.min(ys)):.2f},{float(np.max(ys)):.2f}]"
-    )
-
-    miss = int((X == nan_fill).sum())
-    total = int(X.size)
-    miss_pct = (miss / total) * 100.0 if total else 0.0
-    log(f"TRAIN stats: feature sparsity missing={miss}/{total} ({miss_pct:.1f}%) using nan_fill={nan_fill}")
-
-    gw_list = list(gw_stats.values())
-    gw_list_sorted = sorted(gw_list, key=lambda s: (s.missing_pct(), -s.non_missing), reverse=True)
-
-    worst = gw_list_sorted[: max(1, min(top_k, len(gw_list_sorted)))]
-    worst_str = " | ".join(
-        f"{g.mac}: miss={g.missing_pct():.1f}% (seen={g.non_missing}) mean={g.mean():.1f} std={g.std():.1f}"
-        for g in worst
-    )
-    log(f"TRAIN stats: gateways with highest missing%: {worst_str}")
-
-    best = list(reversed(gw_list_sorted))[: max(1, min(top_k, len(gw_list_sorted)))]
-    best_str = " | ".join(
-        f"{g.mac}: miss={g.missing_pct():.1f}% (seen={g.non_missing}) mean={g.mean():.1f} std={g.std():.1f}"
-        for g in best
-    )
-    log(f"TRAIN stats: gateways with lowest missing%: {best_str}")
-
-    floors = global_stats.get("floors_counts", {})
-    if floors:
-        floor_str = ", ".join(f"z={k}:{v}" for k, v in floors.items())
-        log(f"TRAIN stats: floor distribution: {floor_str}")
-
-
-def run_train(settings: Dict[str, Any], log: Optional[Callable[[str], None]] = None) -> None:
-    """
-    Train hierarchical KNN:
-      - KNeighborsClassifier for floor (Z)
-      - For each floor, a KNeighborsRegressor for (X,Y) as multioutput
-
-    Model saved with joblib to paths.model (or train.model_path).
-    """
-    if log is None:
-        def log(msg: str) -> None:
-            print(msg, flush=True)
-
-    # Build stamp for this module (helps verifying which file is running)
-    try:
-        import hashlib
-        from pathlib import Path
-        _b = Path(__file__).read_bytes()
-        log(f"TRAIN_MODE build sha256={hashlib.sha256(_b).hexdigest()[:12]} size={len(_b)}")
-    except Exception:
-        pass
-
-    train_cfg = _get(settings, "train", {})
-    paths = _get(settings, "paths", {})
-    debug = _get(settings, "debug", {})
-
-    samples_dir = _get(train_cfg, "samples_dir", _get(paths, "samples_dir", "/data/train/samples"))
-    gateways_csv = _get(train_cfg, "gateways_csv", _get(paths, "gateways_csv", "/data/config/gateway.csv"))
-    model_path = _get(train_cfg, "model_path", _get(paths, "model", "/data/model/model.joblib"))
-
-    nan_fill = float(_get(train_cfg, "nan_fill", -110.0))
-
-    k_floor = int(_get(train_cfg, "k_floor", _get(_get(settings, "ml", {}), "k", 7)))
-    k_xy = int(_get(train_cfg, "k_xy", _get(_get(settings, "ml", {}), "k", 7)))
-
-    weights = str(_get(train_cfg, "weights", _get(_get(settings, "ml", {}), "weights", "distance")))
-    metric = str(_get(train_cfg, "metric", _get(_get(settings, "ml", {}), "metric", "euclidean")))
-
-    verbose = _as_bool(_get(debug, "train_verbose", True), True)
-    top_k = int(_get(debug, "train_stats_top_k", 8))
-
-    backup_existing_model = _as_bool(_get(train_cfg, "backup_existing_model", True), True)
-
-    log(
-        "TRAIN config: "
-        f"samples_dir={samples_dir} "
-        f"gateways_csv={gateways_csv} "
-        f"model_path={model_path} "
-        f"nan_fill={nan_fill} "
-        f"k_floor={k_floor} k_xy={k_xy} "
-        f"weights={weights} metric={metric} "
-        f"train_verbose={verbose} backup_existing_model={backup_existing_model}"
-    )
-
-    # 1) Load gateways definition to know feature order
-    gws = load_gateway_features_csv(str(gateways_csv))
+    if log_fn is None:
+        log_fn = log
+
+    train_cfg = settings.get("train", {})
+    knn_cfg = train_cfg.get("knn", {})
+    
+    # Parametri da config.yaml
+    samples_dir = train_cfg.get("samples_dir", "/data/train/samples")
+    gateways_csv = train_cfg.get("gateways_csv", "/data/config/gateway.csv")
+    model_path = train_cfg.get("model_path", "/data/model/model.joblib")
+    nan_fill = float(train_cfg.get("nan_fill", -110.0))
+    
+    k_val = int(knn_cfg.get("k", 5))
+    weights = knn_cfg.get("weights", "distance")
+    metric = knn_cfg.get("metric", "euclidean")
+
+    log_fn(f"TRAIN: Caricamento gateway da {gateways_csv}")
+    gws = load_gateway_features_csv(gateways_csv)
     gateways_order = [g.mac for g in gws]
-    if not gateways_order:
-        raise RuntimeError("No gateways found in gateways_csv (feature-set empty).")
-
-    if verbose:
-        preview = ", ".join(gateways_order[: min(6, len(gateways_order))])
-        log(f"TRAIN: gateways(feature-order)={len(gateways_order)} first=[{preview}{'...' if len(gateways_order) > 6 else ''}]")
-
-    # 2) Collect sample files
-    sample_files = sorted(glob.glob(os.path.join(samples_dir, "*.csv")))
-    if not sample_files:
-        raise RuntimeError(f"No sample files found in samples_dir={samples_dir}")
-
-    X, y_floor, y_xy, meta_xy, gw_stats, global_stats = _collect_dataset(
-        sample_files=sample_files,
-        gateways_order=gateways_order,
-        nan_fill=nan_fill,
-        log=log,
-        verbose=verbose,
-    )
 
-    if verbose:
-        _log_train_stats(
-            log=log,
-            X=X,
-            y_floor=y_floor,
-            y_xy=meta_xy,
-            gateways_order=gateways_order,
-            nan_fill=nan_fill,
-            gw_stats=gw_stats,
-            global_stats=global_stats,
-            top_k=top_k,
-        )
+    # Selezione file (Campagna specifica o globale)
+    files = target_files if target_files else glob.glob(os.path.join(samples_dir, "*.csv"))
+    if not files:
+        raise RuntimeError("Nessun file CSV trovato per l'addestramento.")
 
-    # 3) Fit floor classifier
-    floor_clf = KNeighborsClassifier(
-        n_neighbors=k_floor,
-        weights=weights,
-        metric=metric,
-    )
-    floor_clf.fit(X, y_floor)
-
-    # 4) Fit per-floor XY regressors (multioutput)
-    models_xy: Dict[int, Any] = {}
-    floors = sorted(set(int(z) for z in y_floor.tolist()))
-    for z in floors:
-        idx = np.where(y_floor == z)[0]
-        Xz = X[idx, :]
-        yz = y_xy[idx, :]  # (N,2)
-        reg = KNeighborsRegressor(
-            n_neighbors=k_xy,
-            weights=weights,
-            metric=metric,
-        )
-        reg.fit(Xz, yz)
-        models_xy[int(z)] = reg
-
-        if verbose:
-            xs = yz[:, 0]
-            ys = yz[:, 1]
-            log(
-                f"TRAIN: floor z={z} samples={int(len(idx))} "
-                f"Xrange=[{float(np.min(xs)):.1f},{float(np.max(xs)):.1f}] "
-                f"Yrange=[{float(np.min(ys)):.1f},{float(np.max(ys)):.1f}]"
-            )
-
-    model = {
-        "type": "hier_knn_floor_xy",
-        "gateways_order": gateways_order,
-        "nan_fill": nan_fill,
-        "k_floor": k_floor,
-        "k_xy": k_xy,
-        "weights": weights,
-        "metric": metric,
+    # Costruzione dataset
+    X_list, y_z, y_xy = [], [], []
+    for fp in files:
+        try:
+            df = pd.read_csv(fp, sep=";")
+            if df.empty: continue
+            row = df.iloc[0]
+            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
+
+    X, Y_z, Y_xy = np.array(X_list), np.array(y_z), np.array(y_xy)
+
+    # Step 1: Classificatore Piano (Z)
+    log_fn(f"TRAIN: Fitting Piano Classifier (K={k_val})")
+    floor_clf = KNeighborsClassifier(n_neighbors=k_val, weights=weights, metric=metric).fit(X, Y_z)
+    
+    # Step 2: Regressori X,Y per ogni piano trovato
+    models_xy = {}
+    for z in np.unique(Y_z):
+        idx = np.where(Y_z == z)[0]
+        log_fn(f"TRAIN: Fitting XY Regressor piano {z} ({len(idx)} campioni)")
+        models_xy[int(z)] = KNeighborsRegressor(
+            n_neighbors=min(k_val, len(idx)), 
+            weights=weights, 
+            metric=metric
+        ).fit(X[idx], Y_xy[idx])
+
+    # Salvataggio
+    model_data = {
         "floor_clf": floor_clf,
         "xy_by_floor": models_xy,
-        "floors": floors,
+        "gateways_order": gateways_order,
+        "nan_fill": nan_fill,
+        "created_at": datetime.now().isoformat()
     }
-
+    
     os.makedirs(os.path.dirname(model_path), exist_ok=True)
-
-    # Backup previous model (così inferenza può continuare ad usare una versione nota)
-    backup_path = None
-    if backup_existing_model and os.path.exists(model_path):
-        root, ext = os.path.splitext(model_path)
-        ts = int(time.time())
-        # evita collisioni se lanci due train nello stesso secondo
-        for bump in range(0, 1000):
-            cand = f"{root}_{ts + bump}{ext}"
-            if not os.path.exists(cand):
-                backup_path = cand
-                break
-        try:
-            if backup_path:
-                os.replace(model_path, backup_path)
-                log(f"TRAIN: previous model moved to {backup_path}")
-        except Exception as e:
-            log(f"TRAIN WARNING: cannot backup previous model {model_path}: {type(e).__name__}: {e}")
-
-    # Metadata utile (tipo 'modinfo' minimale)
-    model["created_at_utc"] = datetime.utcnow().replace(microsecond=0).isoformat() + "Z"
-    model["sklearn_version"] = getattr(sklearn, "__version__", "unknown")
-    model["numpy_version"] = getattr(np, "__version__", "unknown")
-
-    joblib.dump(model, model_path)
-
-    log(
-        f"TRAIN DONE: model saved to {model_path} "
-        f"(samples={int(X.shape[0])}, gateways={len(gateways_order)}, floors={len(floors)})"
-    )
+    joblib.dump(model_data, model_path)
+    log_fn(f"✅ TRAIN SUCCESS: Modello salvato in {model_path}")

app/web_beacon.py

@@ -1,88 +1,110 @@
 import streamlit as st
 import pandas as pd
 import os
+from pathlib import Path
+# CORREZIONE: rimosso il punto per evitare ImportError
+from normalize import norm_mac
 
-def load_beacons(file_path, delimiter):
-    """Carica i beacon dal file CSV utilizzando il delimitatore configurato."""
-    if not os.path.exists(file_path):
-        return pd.DataFrame(columns=["BeaconName", "MAC"])
-    try:
-        return pd.read_csv(file_path, sep=delimiter)
-    except Exception:
-        return pd.DataFrame(columns=["BeaconName", "MAC"])
+def show_beacon_manager(cfg):
+    st.subheader("🏷️ Gestione Beacon e Gruppi")
+    
+    paths = cfg.get("paths", {})
+    beacon_csv = paths.get("beacons_csv", "/data/config/beacons.csv")
+    group_csv = "/data/config/beacongroup.csv"
+    csv_delim = paths.get("csv_delimiter", ";")
 
-def save_beacons(file_path, df, delimiter):
-    """Salva i beacon su CSV con il separatore corretto e crea le cartelle se necessario."""
-    folder = os.path.dirname(file_path)
-    if folder and not os.path.exists(folder):
-        os.makedirs(folder, exist_ok=True)
-    df.to_csv(file_path, index=False, sep=delimiter)
+    # --- CARICAMENTO DATI ---
+    def load_df(path, cols):
+        if os.path.exists(path):
+            try:
+                df = pd.read_csv(path, sep=csv_delim)
+                df.columns = [c.strip() for c in df.columns]
+                return df
+            except:
+                return pd.DataFrame(columns=cols)
+        return pd.DataFrame(columns=cols)
 
-def show_beacon_manager(config):
-    st.subheader("🏷️ Gestione Anagrafica Beacon")
-    
-    # Recupero parametri dalla gerarchia paths del config.yaml
-    paths_cfg = config.get("paths", {})
-    beacons_file = paths_cfg.get("beacons_csv", "/data/config/beacons.csv")
-    delimiter = paths_cfg.get("csv_delimiter", ";")
-    
-    # Caricamento dati
-    df = load_beacons(beacons_file, delimiter)
+    df_beacons = load_df(beacon_csv, ["BeaconName", "MAC"])
+    df_groups = load_df(group_csv, ["BeaconGroupName", "GroupMAC"])
 
-    # --- SEZIONE AGGIUNTA ---
-    with st.expander("➕ Aggiungi Nuovo Beacon", expanded=len(df) == 0):
-        col1, col2 = st.columns(2)
-        with col1:
-            name = st.text_input("Nome Beacon (es. BC-21)")
-        with col2:
-            mac = st.text_input("Indirizzo MAC (es. C3:00:00:57:B9:E6)")
-        
-        if st.button("REGISTRA BEACON"):
-            if name and mac:
-                mac = mac.strip().upper()
-                if mac in df['MAC'].values:
-                    st.error("Errore: Questo MAC è già presente in lista.")
-                else:
-                    new_line = pd.DataFrame([{"BeaconName": name, "MAC": mac}])
-                    df = pd.concat([df, new_line], ignore_index=True)
-                    save_beacons(beacons_file, df, delimiter)
-                    st.success(f"Beacon {name} registrato correttamente!")
-                    st.rerun()
-            else:
-                st.warning("Inserisci sia il Nome che il MAC address.")
+    # --- UI: TABS INTERNI ---
+    sub_tab1, sub_tab2 = st.tabs(["Individuali", "Gruppi"])
 
-    # --- SEZIONE VISUALIZZAZIONE E MODIFICA ---
-    if not df.empty:
-        st.markdown("---")
-        st.write("### Lista Beacon")
-        st.info("💡 Puoi modificare i nomi o i MAC direttamente cliccando nelle celle della tabella e poi cliccare su SALVA.")
-        
-        # Editor interattivo per modifiche "al volo"
-        edited_df = st.data_editor(
-            df, 
+    with sub_tab1:
+        st.markdown("### 📋 Anagrafica Beacon Singoli")
+        # Editor interattivo per i beacon
+        edited_beacons = st.data_editor(
+            df_beacons, 
+            num_rows="dynamic", 
             use_container_width=True, 
-            hide_index=True,
-            column_config={
-                "BeaconName": st.column_config.TextColumn("Nome Beacon", help="Nome identificativo", required=True),
-                "MAC": st.column_config.TextColumn("Indirizzo MAC", help="Formato AA:BB:CC...", required=True)
-            }
+            key="editor_beacons",
+            hide_index=True
         )
         
-        # Controllo se ci sono state modifiche
-        if not edited_df.equals(df):
-            if st.button("💾 SALVA MODIFICHE TABELLA"):
-                save_beacons(beacons_file, edited_df, delimiter)
-                st.success("Anagrafica aggiornata!")
+        if st.button("💾 Salva Beacon Individuali"):
+            # Applichiamo la normalizzazione ai MAC inseriti
+            try:
+                edited_beacons['MAC'] = edited_beacons['MAC'].apply(norm_mac)
+                edited_beacons.to_csv(beacon_csv, index=False, sep=csv_delim)
+                st.success("Anagrafica Beacon salvata correttamente!")
                 st.rerun()
+            except Exception as e:
+                st.error(f"Errore durante il salvataggio: {e}")
+
+    with sub_tab2:
+        st.markdown("### 🏗️ Creazione e Gestione Gruppi")
+        
+        # Mostra i gruppi esistenti se il file esiste
+        if not df_groups.empty:
+            st.write("#### Gruppi Attivi")
+            st.dataframe(df_groups, use_container_width=True, hide_index=True)
+        else:
+            st.info("Nessun gruppo configurato.")
+        
+        st.divider()
+        st.write("#### Aggiungi o Modifica un Gruppo")
+        
+        # Form per la gestione dei gruppi
+        with st.form("form_gruppo"):
+            gn = st.text_input("Nome Gruppo", help="Esempio: 4dBm_Power, Gruppo_A, ecc.")
+            
+            # Recuperiamo i beacon disponibili per popolare la selezione
+            available_macs = df_beacons["MAC"].tolist()
+            available_names = df_beacons["BeaconName"].tolist()
+            options = [f"{name} ({mac})" for name, mac in zip(available_names, available_macs)]
+            
+            selected_options = st.multiselect("Seleziona i Beacon da includere:", options)
+            
+            submit = st.form_submit_button("💾 Salva Gruppo")
+            
+            if submit:
+                if not gn:
+                    st.error("Inserisci un nome per il gruppo.")
+                elif not selected_options:
+                    st.error("Seleziona almeno un beacon.")
+                else:
+                    # Estraiamo i MAC eliminando la parte del nome
+                    selected_macs = [opt.split("(")[-1].replace(")", "") for opt in selected_options]
+                    mac_list_str = ",".join(selected_macs)
+                    
+                    # Aggiorna se esiste, altrimenti aggiungi
+                    if gn in df_groups["BeaconGroupName"].values:
+                        df_groups.loc[df_groups["BeaconGroupName"] == gn, "GroupMAC"] = mac_list_str
+                    else:
+                        new_row = pd.DataFrame([{"BeaconGroupName": gn, "GroupMAC": mac_list_str}])
+                        df_groups = pd.concat([df_groups, new_row], ignore_index=True)
+                    
+                    # Assicuriamoci che la cartella esista
+                    os.makedirs(os.path.dirname(group_csv), exist_ok=True)
+                    df_groups.to_csv(group_csv, index=False, sep=csv_delim)
+                    st.success(f"Gruppo '{gn}' creato/aggiornato con successo!")
+                    st.rerun()
+
+        if not df_groups.empty:
+            if st.button("🗑️ Elimina tutti i Gruppi"):
+                if os.path.exists(group_csv):
+                    os.remove(group_csv)
+                    st.warning("Configurazione gruppi rimossa.")
+                    st.rerun()
 
-        # --- SEZIONE ELIMINAZIONE ---
-        st.markdown("---")
-        st.subheader("Elimina Beacon")
-        to_del = st.selectbox("Seleziona il beacon da rimuovere:", df['BeaconName'].tolist())
-        if st.button("🗑️ ELIMINA SELEZIONATO"):
-            df = df[df['BeaconName'] != to_del]
-            save_beacons(beacons_file, df, delimiter)
-            st.success(f"Beacon {to_del} rimosso.")
-            st.rerun()
-    else:
-        st.info("Nessun beacon configurato. Inserisci il primo beacon per creare il file.")
+    return df_beacons, df_groups

app/web_status.py

@@ -1,128 +1,93 @@
 import streamlit as st
 import pandas as pd
-import psutil
 import os
 import time
-import json
-import paho.mqtt.client as mqtt
+from datetime import datetime
 import requests
+import paho.mqtt.client as mqtt
+import psutil  # AGGIUNTO per monitoraggio processi
 
-def get_system_metrics():
-    """Recupera statistiche hardware del server."""
-    return {
-        "cpu": psutil.cpu_percent(interval=None),
-        "ram": psutil.virtual_memory().percent,
-        "disk": psutil.disk_usage('/').percent,
-        "net_recv": psutil.net_io_counters().bytes_recv / (1024**2)
-    }
+def is_main_running():
+    """Verifica se il processo main.py è attualmente in esecuzione."""
+    for proc in psutil.process_iter(['cmdline']):
+        try:
+            cmd = proc.info['cmdline']
+            if cmd and 'app.main' in ' '.join(cmd):
+                return True
+        except (psutil.NoSuchProcess, psutil.AccessDenied):
+            continue
+    return False
 
-def get_api_data(cfg, sec):
-    """Fetch reale dei beacon dalle API OIDC."""
-    if not sec or 'oidc' not in sec:
-        return None, "Segreti mancanti"
+
+def check_mqtt_broker(host, port, protocol_str="mqttv311"):
     try:
-        auth_res = requests.post(
-            cfg['api']['token_url'],
-            data={
-                "grant_type": "password",
-                "client_id": cfg['api']['client_id'],
-                "client_secret": sec['oidc']['client_secret'],
-                "username": sec['oidc']['username'],
-                "password": sec['oidc']['password'],
-            },
-            verify=cfg['api'].get('verify_tls', False),
-            timeout=5
-        )
-        token = auth_res.json().get("access_token")
-        res = requests.get(
-            cfg['api']['get_beacons_url'],
-            headers={"Authorization": f"Bearer {token}"},
-            verify=cfg['api'].get('verify_tls', False),
-            timeout=5
-        )
-        return res.json(), "OK"
+        # Mappatura protocollo per compatibilità paho-mqtt
+        proto = mqtt.MQTTv311
+        if protocol_str == "mqttv5":
+            proto = mqtt.MQTTv5
+            
+        client = mqtt.Client(protocol=proto)
+        # Aumentiamo il timeout a 5 secondi per maggiore stabilità
+        client.connect(host, port, keepalive=5)
+        client.disconnect()
+        return True
     except Exception as e:
-        return None, str(e)
-
-def show_system_status(cfg, sec=None):
-    st.title("🛰️ Diagnostica & Sniffer")
+        # Utile per il debug interno se necessario
+        return False
 
-    # --- 1. RISORSE DI SISTEMA ---
-    st.subheader("🖥️ Risorse Hardware")
-    m = get_system_metrics()
-    c1, c2, c3, c4 = st.columns(4)
-    c1.metric("CPU", f"{m['cpu']}%")
-    c2.metric("RAM", f"{m['ram']}%")
-    c3.metric("Disco", f"{m['disk']}%")
-    c4.metric("Rete", f"{m['net_recv']:.1f} MB")
+def check_api_server(token_url, verify_tls):
+    try:
+        response = requests.get(token_url, timeout=2, verify=verify_tls)
+        return True
+    except:
+        return False
 
-    st.divider()
+def show_system_status(cfg, secrets):
+    st.subheader("🖥️ Stato Infrastruttura")
+    
+    col1, col2, col3, col4 = st.columns(4)
+    
+    # 1. CORE ENGINE
+    main_active = is_main_running()
+    col1.metric("Core Engine", "ON" if main_active else "OFF", 
+                delta=None, delta_color="normal")
+    if main_active: col1.success("✅ Main Attivo")
+    else: col1.error("❌ Main Fermo")
 
-    # --- 2. CONNETTIVITÀ & DATI API ---
-    st.subheader("🔌 Connettività & API")
-    col_api, col_mq = st.columns(2)
+    # 2. MQTT BROKER
+    mqtt_cfg = cfg.get('mqtt', {})
+    mqtt_ok = check_mqtt_broker(
+        mqtt_cfg.get('host', 'localhost'), 
+        mqtt_cfg.get('port', 1883),
+        mqtt_cfg.get('protocol', 'mqttv311')
+    )
+    col2.metric("MQTT Broker", "Online" if mqtt_ok else "Offline")
+    if mqtt_ok: col2.success("✅ Broker OK")
+    else: col2.error("❌ Errore MQTT")
 
-    with col_api:
-        st.markdown("**Server API OIDC**")
-        beacons_api_data, api_msg = get_api_data(cfg, sec)
-        if beacons_api_data:
-            st.success(f"✅ Connesso ({len(beacons_api_data)} beacon)")
-            with st.expander("Visualizza Tabella Dati API"):
-                # Mostriamo i dati API in una tabella senza scroll eccessivo
-                df_api = pd.DataFrame(beacons_api_data)
-                st.table(df_api.head(10)) 
-        else:
-            st.error(f"❌ Errore: {api_msg}")
+    # 3. API SERVER
+    api_cfg = cfg.get('api', {})
+    api_ok = check_api_server(api_cfg.get('token_url', ''), api_cfg.get('verify_tls', False))
+    col3.metric("API System", "Ready" if api_ok else "Error")
+    if api_ok: col3.success("✅ API OK")
+    else: col3.error("❌ API Down")
 
-    with col_mq:
-        st.markdown("**Broker MQTT**")
-        mq_host = cfg.get('mqtt', {}).get('host', '127.0.0.1')
-        st.info(f"Host: `{mq_host}`")
-        st.success("✅ Configurazione Caricata")
+    # 4. DISCO (Data Dir)
+    try:
+        usage = psutil.disk_usage('/data')
+        col4.metric("Spazio Disco", f"{usage.percent}%")
+        if usage.percent < 90: col4.success(f"✅ {usage.free // (1024**3)} GB Liberi")
+        else: col4.warning("⚠️ Disco quasi pieno")
+    except:
+        col4.write("N/D")
 
+    # LOG RECENTI (Ripristinati dal tuo originale)
     st.divider()
-
-    # --- 3. SNIFFER DATI MQTT ---
-    st.subheader("🎯 Sniffer Real-Time")
-    st.caption("Inserisci un MAC per intercettare il traffico live (10 secondi)")
-
-    target_mac = st.text_input("MAC da sniffare (es. ac233fc1dd49)", "").lower().replace(":", "")
-
-    if st.button("🚀 AVVIA CATTURA", use_container_width=True, type="primary"):
-        if not target_mac:
-            st.warning("Inserisci un MAC prima di iniziare.")
-        else:
-            captured = []
-            def on_message(client, userdata, message):
-                payload = message.payload.decode()
-                topic = message.topic
-                if target_mac in topic.lower() or target_mac in payload.lower():
-                    captured.append({
-                        "Ora": time.strftime("%H:%M:%S"),
-                        "Topic": topic,
-                        "Dati": payload[:100] + "..." # Accorciamo per la tabella
-                    })
-
-            client = mqtt.Client(client_id=f"Sniffer_{int(time.time())}")
-            try:
-                client.on_message = on_message
-                client.connect(cfg['mqtt']['host'], cfg['mqtt']['port'], 60)
-                client.subscribe("#")
-                client.loop_start()
-
-                with st.status("Cattura in corso...") as status:
-                    progress = st.progress(0)
-                    for i in range(100):
-                        time.sleep(0.1)
-                        progress.progress(i + 1)
-                    client.loop_stop()
-                    status.update(label="Cattura completata!", state="complete")
-
-                if captured:
-                    st.success(f"Intercettati {len(captured)} messaggi per `{target_mac}`")
-                    # Usiamo st.table per evitare le slidebar (renderizza tutto il contenuto)
-                    st.table(pd.DataFrame(captured))
-                else:
-                    st.warning("Nessun messaggio trovato. Controlla che il dispositivo sia acceso.")
-            except Exception as e:
-                st.error(f"Connessione fallita: {e}")
+    st.subheader("📝 Ultimi Eventi Log")
+    LOG_FILE = "/tmp/main_process.log"
+    if os.path.exists(LOG_FILE):
+        with open(LOG_FILE, "r") as f:
+            lines = f.readlines()
+            st.text_area("Log in tempo reale:", "".join(lines[-20:]), height=300)
+    else:
+        st.info("Nessun file di log trovato in /tmp/main_process.log")

app/web_suite.py

@@ -2,183 +2,177 @@ import streamlit as st
 import yaml
 import subprocess
 import os
-import web_status  # Nuovo modulo
+import time
+import signal
+import web_status 
+import psutil
 
-# --- COSTANTI E UTILS (Invariati) ---
-CONFIG_PATH = os.environ.get("CONFIG") or os.environ.get("CONFIG_FILE") or "/config/config.yaml"
-SECRETS_PATH = os.environ.get("SECRETS_FILE") or "/config/secrets.yaml"
-LOG_FILE = "/tmp/main_process.log"
-STATE_FILE = "/data/.web_state"
-
-def load_yaml(path):
-    if not os.path.exists(path): return {}
-    with open(path, 'r') as f: return yaml.safe_load(f) or {}
-
-def save_yaml(path, data):
-    with open(path, 'w') as f: yaml.dump(data, f, default_flow_style=False)
-
-cfg = load_yaml(CONFIG_PATH)
-
-# --- CONFIGURAZIONE PAGINA E MENU ABOUT ---
+# --- 1. CONFIGURAZIONE PAGINA ---
 st.set_page_config(
     page_title="BLE Localizer Manager",
     layout="wide",
-    initial_sidebar_state="auto",
-    menu_items={
-        'Get Help': None,
-        'Report a bug': None,
-        'About': "🛰️BLE AI Localizer - Suite\nSistema professionale di posizionamento BLE."
-    }
+    initial_sidebar_state="auto"
 )
 
-st.title("🛰️ BLE AI Localizer - Suite") # Forza il titolo qui
+# --- 2. CONFIGURAZIONE PERCORSI ---
+REAL_CONFIG_PATH = "/config/config.yaml"
+if not os.path.exists(REAL_CONFIG_PATH):
+    REAL_CONFIG_PATH = "/app/config/config.yaml"
 
-# --- CSS OTTIMIZZATO (Sidebar Fina + Mobile) ---
-st.markdown("""
-    <style>
-    [data-testid="stSidebar"] { min-width: 200px !important; max-width: 230px !important; }
-    [data-testid="stSidebar"] .stButton > button { width: 100%; height: 2.5rem; border-radius: 8px; font-size: 0.9rem !important; }
-    .stTextInput input, .stSelectbox div, .stNumberInput input { height: 3rem !important; }
-    button[data-baseweb="tab"] { height: 3.5rem !important; }
-    .stButton > button { border-radius: 10px; font-weight: bold; }
-    </style>
-    """, unsafe_allow_html=True)
+LOG_FILE = "/tmp/main_process.log"
 
-if "main_login_user" not in st.session_state:
-    st.session_state["main_login_user"] = ""
+def load_yaml(path):
+    if not os.path.exists(path): return {}
+    with open(path, 'r', encoding='utf-8') as f: return yaml.safe_load(f) or {}
 
-# --- LOGIN (Tua versione originale) ---
+def save_yaml(path, data):
+    with open(path, 'w', encoding='utf-8') as f: yaml.dump(data, f, default_flow_style=False)
+
+cfg = load_yaml(REAL_CONFIG_PATH)
+
+def stop_core_engine():
+    for proc in psutil.process_iter(['cmdline']):
+        try:
+            cmd = proc.info['cmdline']
+            if cmd and ('app.main' in ' '.join(cmd) or 'main.py' in ' '.join(cmd)):
+                proc.terminate()
+                proc.wait(timeout=2)
+        except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.TimeoutExpired):
+            try: proc.kill()
+            except: pass
+
+def start_core_engine():
+    env = os.environ.copy()
+    env["CONFIG"] = REAL_CONFIG_PATH 
+    env["PYTHONPATH"] = "/app"
+    with open(LOG_FILE, "a") as log_f:
+        subprocess.Popen(
+            ["python3", "-m", "app.main"],
+            env=env,
+            cwd="/app",
+            stdout=log_f,
+            stderr=log_f,
+            start_new_session=True
+        )
+
+# --- 3. TITOLO FISSO ---
+st.title("🛰️ BLE AI Localizer - Suite")
+
+# --- 4. LOGICA LOGIN ---
 if "password_correct" not in st.session_state:
     st.session_state["password_correct"] = False
 
 if not st.session_state["password_correct"]:
-    user = st.text_input("Username", key="main_login_user")
-    pw = st.text_input("Password", type="password", key="main_login_pw")
+    user = st.text_input("Username")
+    pw = st.text_input("Password", type="password")
     if st.button("ACCEDI"):
         if user == os.environ.get("UI_USER", "admin") and pw == os.environ.get("UI_PASSWORD", "password"):
             st.session_state["password_correct"] = True
             st.rerun()
-        else: st.error("Credenziali errate")
+        else:
+            st.error("Credenziali errate")
     st.stop()
 
-# Import moduli dopo login
-from map_manager import show_mapper
-from web_gateway import show_gateway_manager
-from web_beacon import show_beacon_manager
-import web_training_data
-import web_inference
+# --- 5. AUTO-START ---
+if not web_status.is_main_running():
+    if "core_auto_launched" not in st.session_state:
+        start_core_engine()
+        st.session_state["core_auto_launched"] = True
+        time.sleep(1) 
+        st.rerun()
 
-def is_proc_alive():
-    return st.session_state.get('proc') is not None and st.session_state.proc.poll() is None
-
-# Auto-riavvio (Tua logica)
-if os.path.exists(STATE_FILE):
-    with open(STATE_FILE, "r") as f:
-        if f.read().strip() == "running" and not is_proc_alive():
-            log_out = open(LOG_FILE, "a")
-            st.session_state.proc = subprocess.Popen(
-                ["python", "-u", "-m", "app.main"],
-                env={**os.environ, "PYTHONUNBUFFERED": "1", "CONFIG": CONFIG_PATH},
-                stdout=log_out, stderr=subprocess.STDOUT, text=True
-            )
-
-# --- SIDEBAR ---
+# --- 6. SIDEBAR: CONTROLLI SISTEMA ---
 with st.sidebar:
-    st.header("Stato Sistema")
-    alive = is_proc_alive()
-    if not alive:
-        if st.button("▶️ AVVIA MAIN"):
-            if os.path.exists(LOG_FILE): os.remove(LOG_FILE)
-            st.session_state.proc = subprocess.Popen(
-                ["python", "-u", "-m", "app.main"],
-                env={**os.environ, "PYTHONUNBUFFERED": "1", "CONFIG": CONFIG_PATH},
-                stdout=open(LOG_FILE, "w"), stderr=subprocess.STDOUT, text=True
-            )
-            with open(STATE_FILE, "w") as f: f.write("running")
-            st.rerun()
+    st.header("🛠️ Core Control")
+    
+    is_running = web_status.is_main_running()
+    if is_running:
+        st.markdown("STATO: :green[**CORE ATTIVO**]")
     else:
-        if st.button("⏹️ FERMA MAIN"):
-            st.session_state.proc.terminate()
-            st.session_state.proc = None
-            with open(STATE_FILE, "w") as f: f.write("stopped")
+        st.markdown("STATO: :red[**CORE FERMO**]")
+    
+    if st.button("🚀 RIAVVIA CORE" if is_running else "▶️ AVVIA CORE", use_container_width=True):
+        with st.spinner("Gestione Core..."):
+            stop_core_engine()
+            time.sleep(1)
+            start_core_engine()
+            time.sleep(2)
             st.rerun()
+
+    st.divider()
+    # --- GESTIONE MQTT RAW (DINAMICA DA CONFIG) ---
+    m_cfg = cfg.get('mqtt', {})
+    m_host = m_cfg.get('host', '127.0.0.1')
+    m_port = m_cfg.get('port', 1883)
+    m_proto = m_cfg.get('protocol', 'mqttv311')
+    # Estraiamo la parola chiave dal topic (es: 'publish_out' da 'publish_out/#')
+    m_filter = m_cfg.get('topic', 'publish_out').split('/')[0]
+
+    RAW_LOG_DIR = cfg.get('paths', {}).get('mqtt_raw_dir', '/data/mqtt_raw/')
+    os.makedirs(RAW_LOG_DIR, exist_ok=True)
+    raw_active = st.toggle("🔴 MQTT RAW RECORDING", value=st.session_state.get("mqtt_logging", False))
     
-    st.write(f"Stato: {':green[Running]' if alive else ':red[Stopped]'}")
-    st.write(f"Modo: **{cfg.get('mode', 'N/D').upper()}**")
-    if st.button("LOGOUT"):
-        st.session_state["password_correct"] = False
-        st.rerun()
+    if raw_active and not st.session_state.get("mqtt_logging"):
+        dt = time.strftime("%Y%m%d_%H%M%S")
+        filepath = os.path.join(RAW_LOG_DIR, f"mqtt_raw_{dt}.log")
+        
+        # Comando generato dinamicamente dai parametri YAML
+        cmd = f"stdbuf -oL mosquitto_sub -v -h {m_host} -p {m_port} -t '#' -V {m_proto} | stdbuf -oL grep '{m_filter}' > {filepath}"
+        
+        proc = subprocess.Popen(cmd, shell=True, preexec_fn=os.setsid)
+        st.session_state["mqtt_logging"] = True
+        st.session_state["mqtt_proc_pid"] = proc.pid
+        st.success(f"Log avviato: {m_host}")
     
-    st.divider()
-    st.caption("🚦 LIVE STATUS")
-    c1, c2, c3 = st.columns(3)
-    c1.markdown("`MQTT` 🟢")
-    c2.markdown("`GWs` 🟢")
-    c3.markdown("`API` 🟢")
-
-# --- TABS (Incluso il nuovo tab Stato) ---
-tab_log, tab_cfg, tab_sec, tab_gw, tab_beac, tab_map, tab_data, tab_infer, tab_status = st.tabs([
-    "📜 Log", "⚙️ Config", "🔑 Secrets", "🌐 Gateway", "🏷️ Beacon", "🗺️ Mappa", "📂 Dati", "🤖 Inferenza", "🖥️ Stato"
+    if not raw_active and st.session_state.get("mqtt_logging"):
+        try: 
+            os.killpg(os.getpgid(st.session_state["mqtt_proc_pid"]), signal.SIGTERM)
+        except: 
+            pass
+        st.session_state["mqtt_logging"] = False
+
+# --- 7. IMPORT MODULI E TABS ---
+from map_manager import show_mapper
+from web_gateway import show_gateway_manager
+from web_beacon import show_beacon_manager
+from web_test_inference import show_test_inference
+import web_training_data
+import web_inference
+
+tab_log, tab_cfg, tab_sec, tab_gw, tab_beac, tab_map, tab_model, tab_infer_test, tab_infer, tab_status = st.tabs([
+    "📜 Log", "⚙️ Config", "🔑 Secrets", "🌐 Gateway", "🏷️ Beacon", "📍 Rilevamento", "🧠 Modello", "🧪 InferTest", "🤖 Inferenza", "🖥️ Stato"
 ])
 
-with tab_status:
-    import web_status
-    # Carica i segreti e passali alla funzione
-    sec_data = load_yaml(SECRETS_PATH)
-    web_status.show_system_status(cfg, sec_data)
+with tab_log:
+    col_l1, col_l2 = st.columns(2)
+    if col_l1.button("🔄 AGGIORNA LOG", use_container_width=True): st.rerun()
+    if col_l2.button("🗑️ AZZERA LOG", use_container_width=True):
+        try:
+            with open(LOG_FILE, "w") as f: f.write(f"--- Log resettato il {time.strftime('%Y-%m-%d %H:%M:%S')} ---\n")
+            st.success("Log azzerato!"); time.sleep(1); st.rerun()
+        except: st.error("Errore pulizia log")
+    if os.path.exists(LOG_FILE):
+        with open(LOG_FILE, "r") as f: st.code("".join(f.readlines()[-100:]))
 
 with tab_cfg:
-    # RIPRISTINATA TUTTA LA TUA LOGICA ORIGINALE
-    c_cfg = load_yaml(CONFIG_PATH)
-    st.subheader("🚀 Stato Operativo")
-    mode_options = ["infer", "train", "collect_train"]
-    new_mode = st.selectbox("Modalità:", mode_options, index=mode_options.index(c_cfg.get('mode', 'infer')))
-
-    with st.expander("📊 Parametri Raccolta Dati", expanded=True):
-        p_cfg = c_cfg.get('collect_train', {})
-        col_a, col_b = st.columns(2)
-        win_sec = col_a.number_input("Finestra (sec)", value=int(p_cfg.get('window_seconds', 30)))
-        min_gw = col_a.number_input("Min. Gateway", value=int(p_cfg.get('min_non_nan', 3)))
-        rssi_min = col_b.slider("RSSI Min", -120, -50, int(p_cfg.get('rssi_min', -110)))
-        rssi_max = col_b.slider("RSSI Max", -40, 0, int(p_cfg.get('rssi_max', -25)))
-        outlier = col_b.selectbox("Outlier", ["mad", "iqr", "none"], index=0)
-
-    with st.expander("📡 MQTT & 🌐 API", expanded=False):
-        m_cfg = c_cfg.get('mqtt', {})
-        mq_host = st.text_input("Broker", value=m_cfg.get('host', '127.0.0.1'))
-        mq_port = st.number_input("Porta", value=int(m_cfg.get('port', 1883)))
-        a_cfg = c_cfg.get('api', {})
-        t_url = st.text_input("Token URL", value=a_cfg.get('token_url', ''))
-        v_tls = st.checkbox("Verify TLS", value=a_cfg.get('verify_tls', False))
-
-    with st.expander("🛠️ Expert Mode (YAML)", expanded=False):
-        cfg_text = st.text_area("Edit manuale", yaml.dump(c_cfg), height=300)
-
-    if st.button("💾 SALVA TUTTO"):
-        try:
-            final_cfg = yaml.safe_load(cfg_text)
-            final_cfg['mode'] = new_mode
-            # Aggiorna con i valori dei widget per sicurezza
-            if 'collect_train' not in final_cfg: final_cfg['collect_train'] = {}
-            final_cfg['collect_train'].update({'window_seconds': win_sec, 'rssi_min': rssi_min})
-            save_yaml(CONFIG_PATH, final_cfg)
-            st.success("Salvato!")
-        except Exception as e: st.error(e)
+    st.subheader("🚀 Configurazione Operativa")
+    cfg_text = st.text_area("Expert Mode (YAML)", yaml.dump(cfg), height=400)
+    if st.button("💾 SALVA CONFIG"):
+        save_yaml(REAL_CONFIG_PATH, yaml.safe_load(cfg_text)); st.success("Salvato!")
 
 with tab_sec:
-    sec = load_yaml(SECRETS_PATH)
+    CURR_SECRETS = os.environ.get("SECRETS_FILE") or "/config/secrets.yaml"
+    sec = load_yaml(CURR_SECRETS)
     sec_edit = st.text_area("Secrets YAML", yaml.dump(sec), height=200)
-    if st.button("SALVA SECRETS"):
-        save_yaml(SECRETS_PATH, yaml.safe_load(sec_edit))
-        st.success("ApiToken salvati!")
-
-with tab_log:
-    if os.path.exists(LOG_FILE):
-        with open(LOG_FILE, "r") as f: st.code("".join(f.readlines()[-50:]))
-    if st.button("🔄 AGGIORNA LOG"): st.rerun()
+    if st.button("SALVA SECRETS"): 
+        save_yaml(CURR_SECRETS, yaml.safe_load(sec_edit)); st.success("OK!")
 
+with tab_gw: show_gateway_manager(cfg)
+with tab_beac: show_beacon_manager(cfg)
 with tab_map: show_mapper(cfg)
-with tab_gw: show_gateway_manager(load_yaml(CONFIG_PATH))
-with tab_beac: show_beacon_manager(load_yaml(CONFIG_PATH))
-with tab_data: web_training_data.show_training_data_manager(cfg)
+with tab_model: web_training_data.show_training_data_manager(cfg)
+with tab_infer_test: show_test_inference(cfg) 
 with tab_infer: web_inference.show_inference_page(cfg)
+with tab_status:
+    sec_data = load_yaml(os.environ.get("SECRETS_FILE") or "/config/secrets.yaml")
+    web_status.show_system_status(cfg, sec_data)

app/web_test_inference.py

@@ -0,0 +1,146 @@
+import streamlit as st
+import pandas as pd
+import os
+import joblib
+import numpy as np
+import math
+import folium
+import json
+import base64
+from io import BytesIO
+from pathlib import Path
+from PIL import Image
+from streamlit_folium import st_folium
+
+# --- UTILS ---
+@st.cache_data
+def get_image_base64(img_path):
+    img = Image.open(img_path).convert("RGBA")
+    w, h = img.size
+    buffered = BytesIO()
+    img.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue()).decode("ascii")
+    return f"data:image/png;base64,{img_str}", w, h
+
+def calculate_error(z_real, x_real, y_real, z_pred, x_pred, y_pred):
+    z_err = abs(z_real - z_pred)
+    dist_err = math.sqrt((x_real - x_pred)**2 + (y_real - y_pred)**2)
+    return z_err, dist_err
+
+def show_test_inference(cfg):
+    st.subheader("🧪 Test Inferenza Offline")
+    
+    MODEL_DIR = Path("/data/model")
+    TEST_SAMPLES_DIR = Path("/data/train/testsamples")
+    MAPS_DIR = Path("/data/maps")
+
+    available_models = sorted([m.name for m in MODEL_DIR.glob("model_camp_*.joblib")], reverse=True)
+    test_files = sorted([f.name for f in TEST_SAMPLES_DIR.glob("*.csv")])
+
+    if not available_models or not test_files:
+        st.warning("Verifica la presenza di modelli e campioni di test.")
+        return
+
+    col1, col2 = st.columns(2)
+    selected_model = col1.selectbox("🎯 Seleziona Modello:", available_models)
+    selected_test = col2.selectbox("📄 Seleziona Fingerprint:", test_files)
+
+    if "test_results" not in st.session_state:
+        st.session_state.test_results = None
+
+    if st.button("🚀 ESEGUI TEST DI PRECISIONE", type="primary", use_container_width=True):
+        try:
+            m_pkg = joblib.load(MODEL_DIR / selected_model)
+            delim = cfg.get('paths', {}).get('csv_delimiter', ';')
+            df_test = pd.read_csv(TEST_SAMPLES_DIR / selected_test, sep=delim)
+            
+            row = df_test.iloc[0]
+            z_real, x_real, y_real = int(round(float(row['z']))), float(row['x']), float(row['y'])
+            
+            gws = m_pkg['gateways_order']
+            fill = m_pkg.get('nan_fill', -110.0)
+            X_in = np.array([[float(row.get(gw, fill)) for gw in gws]])
+            
+            z_pred = int(m_pkg['floor_clf'].predict(X_in)[0])
+            x_pred, y_pred = -1.0, -1.0
+            if z_pred in m_pkg['xy_by_floor']:
+                xy = m_pkg['xy_by_floor'][z_pred].predict(X_in)[0]
+                x_pred, y_pred = float(xy[0]), float(xy[1])
+
+            z_err, dist_err = calculate_error(z_real, x_real, y_real, z_pred, x_pred, y_pred)
+            
+            st.session_state.test_results = {
+                "z_real": z_real, "x_real": x_real, "y_real": y_real,
+                "z_pred": z_pred, "x_pred": x_pred, "y_pred": y_pred,
+                "z_err": z_err, "dist_err": dist_err,
+                "model_used": selected_model
+            }
+        except Exception as e:
+            st.error(f"Errore: {e}")
+
+    if st.session_state.test_results:
+        res = st.session_state.test_results
+        
+        # --- RIPRISTINO INFORMAZIONI COORDINATE ---
+        c_info1, c_info2 = st.columns(2)
+        with c_info1:
+            st.info(f"📍 **Test Reale** | Piano: {res['z_real']} | X: **{res['x_real']}** | Y: **{res['y_real']}**")
+        with c_info2:
+            st.success(f"🔮 **Predizione** | Piano: {res['z_pred']} | X: **{round(res['x_pred'],1)}** | Y: **{round(res['y_pred'],1)}**")
+
+        # --- LEGENDA AGGIORNATA CON NUOVI COLORI ---
+        st.markdown(f"""
+        <div style="background-color: #f8f9fa; padding: 12px; border-radius: 8px; border-left: 5px solid #00bcd4; margin: 10px 0;">
+            <h5 style="margin:0 0 8px 0;">📍 Legenda Mappa</h5>
+            <span style="color: #00bcd4; font-weight: bold;">● PUNTO DI TEST:</span> Posizione reale del rilievo.<br>
+            <span style="color: #ff9800; font-weight: bold;">● PUNTO PREDETTO:</span> Posizione calcolata dal modello AI.<br>
+            <span style="color: #ffeb3b; font-weight: bold;">--- LINEA GIALLA:</span> Scostamento di <b>{round(res['dist_err'], 2)} px</b>.
+        </div>
+        """, unsafe_allow_html=True)
+
+        img_filename = f"floor_{res['z_pred']}.png"
+        meta_filename = f"meta_{res['z_pred']}.json"
+        img_p = MAPS_DIR / img_filename
+        meta_p = MAPS_DIR / meta_filename
+
+        if img_p.exists() and meta_p.exists():
+            with open(meta_p, "r") as f: meta = json.load(f)
+            img_data, w, h = get_image_base64(img_p)
+            bounds = [[0, 0], [h, w]]
+            
+            m = folium.Map(location=[h/2, w/2], crs="Simple", tiles=None, zoom_start=0, attribution_control=False)
+            m.fit_bounds(bounds)
+            m.options.update({"minZoom": -5, "maxZoom": 5, "maxBounds": bounds, "zoomSnap": 0.25})
+            folium.raster_layers.ImageOverlay(image=img_data, bounds=bounds).add_to(m)
+
+            if meta.get("calibrated"):
+                def to_px(mx, my):
+                    px = (mx * meta["pixel_ratio"]) + meta["origin"][0]
+                    py = meta["origin"][1] - (my * meta["pixel_ratio"])
+                    return [py, px]
+
+                p_real = to_px(res['x_real'], res['y_real'])
+                p_pred = to_px(res['x_pred'], res['y_pred'])
+
+                # 🔵 PUNTO DI TEST (Celeste)
+                folium.CircleMarker(
+                    location=p_real, radius=11, color="#00838f", fill=True, 
+                    fill_color="#00bcd4", fill_opacity=0.85,
+                    tooltip="PUNTO DI TEST (REALE)"
+                ).add_to(m)
+                
+                # 🟠 PUNTO PREDETTO (Arancio)
+                if res['x_pred'] != -1.0:
+                    folium.CircleMarker(
+                        location=p_pred, radius=11, color="#e65100", fill=True, 
+                        fill_color="#ff9800", fill_opacity=0.85,
+                        tooltip=f"PREDIZIONE (Modello: {res['model_used']})"
+                    ).add_to(m)
+                    
+                    # Linea Errore
+                    folium.PolyLine(
+                        locations=[p_real, p_pred], color="#ffeb3b", 
+                        weight=4, opacity=0.7, dash_array='8'
+                    ).add_to(m)
+
+            st_folium(m, height=700, width=None, key=f"test_map_final", use_container_width=True)

app/web_training_data.py

@@ -1,72 +1,142 @@
 import streamlit as st
-import os
 import pandas as pd
-from pathlib import Path
+import os
+import json
+import shutil
 import time
-from datetime import datetime
+import joblib
+from pathlib import Path
+
+def get_campaigns_info(directory: Path):
+    """Raggruppa i file per prefisso campagna."""
+    if not directory.exists(): return {}
+    files = list(directory.glob("*.csv"))
+    campaigns = {}
+    for f in files:
+        parts = f.name.split('_')
+        if len(parts) >= 4:
+            camp_id = parts[0]
+            campaigns.setdefault(camp_id, []).append(str(f))
+    return campaigns
 
 def show_training_data_manager(cfg):
-    st.subheader("📂 Gestione Campioni Training")
+    st.subheader("🧠 Console Gestione Modelli e Rilievi")
     
-    try:
-        raw_path = cfg.get('collect_train', {}).get('samples_dir', '/data/train/samples')
-        samples_dir = Path(raw_path)
-    except Exception as e:
-        st.error(f"Errore config: {e}")
-        return
+    MODEL_DIR = Path("/data/model")
+    JOBS_DIR = Path("/data/train/train_jobs")
+    MODEL_DIR.mkdir(parents=True, exist_ok=True)
+    JOBS_DIR.mkdir(parents=True, exist_ok=True)
 
-    # --- 1. RECUPERO E FILTRO DATI ---
-    all_files = [f for f in os.listdir(samples_dir) if f.endswith('.csv')]
-    files_data = []
+    # --- 1. GESTIONE MODELLI E LINK SIMBOLICO ---
+    st.write("### 🎯 Modelli Generati ed Attivazione")
     
-    for file in all_files:
-        path = samples_dir / file
-        ts = os.path.getmtime(path)
-        files_data.append({
-            "File": file,
-            "Data": datetime.fromtimestamp(ts).strftime("%Y-%m-%d %H:%M"),
-            "ts": ts
-        })
+    available_models = sorted(
+        [m for m in MODEL_DIR.glob("model_camp_*.joblib")], 
+        key=os.path.getmtime, 
+        reverse=True
+    )
 
-    df = pd.DataFrame(files_data).sort_values("ts", ascending=False)
+    if available_models:
+        # Visualizzazione stato attuale link simbolico
+        target_path = MODEL_DIR / "model.joblib"
+        if target_path.is_symlink():
+            try:
+                current_link = os.readlink(target_path)
+                st.info(f"🔗 **Modello operativo attuale:** `{os.path.basename(current_link)}`")
+            except Exception:
+                st.warning("⚠️ Impossibile leggere il link simbolico.")
+        
+        # Tabella modelli esistenti
+        m_list = []
+        for m in available_models:
+            m_list.append({
+                "Nome Modello": m.name,
+                "Data": time.strftime('%d/%m/%Y %H:%M', time.localtime(m.stat().st_mtime)),
+                "Size": f"{round(m.stat().st_size / 1024, 1)} KB"
+            })
+        st.dataframe(pd.DataFrame(m_list), use_container_width=True, hide_index=True)
 
-    # Barra di ricerca
-    search = st.text_input("🔍 Cerca coordinata:", "").strip().lower()
-    if search:
-        df = df[df['File'].str.lower().str.contains(search)]
+        selected_model = st.selectbox("Scegli il modello da analizzare o attivare:", [m.name for m in available_models])
+        
+        # --- NUOVA SEZIONE: ANALISI TECNICA ---
+        with st.expander("🔍 Ispezione Metadati Modello", expanded=False):
+            try:
+                m_data = joblib.load(MODEL_DIR / selected_model)
+                st.write(f"**Data Creazione:** {m_data.get('created_at', 'N/D')}")
+                st.write(f"**Gateway allenati:** {len(m_data.get('gateways_order', []))}")
+                st.code(", ".join(m_data.get('gateways_order', [])))
+                
+                floors = list(m_data.get('xy_by_floor', {}).keys())
+                st.write(f"**Piani mappati nel regressore:** {floors}")
+                
+                if not floors or len(m_data.get('gateways_order', [])) == 0:
+                    st.error("⚠️ Attenzione: Il modello sembra non contenere dati validi.")
+            except Exception as e:
+                st.error(f"Errore caricamento per analisi: {e}")
 
-    # --- 2. VISUALIZZAZIONE COMPATTA (SENZA TREMOLIO) ---
-    # Usiamo le colonne per restringere la tabella al centro
-    col_tab, col_empty = st.columns([3, 1]) 
-    
-    with col_tab:
-        # st.table non ha scrollbar e non ha interazioni JS che causano tremolio
-        st.table(df[["File", "Data"]].head(20)) 
+        if st.button("🔗 IMPOSTA COME OPERATIVO (Symlink)", type="primary", use_container_width=True):
+            try:
+                if target_path.exists() or target_path.is_symlink():
+                    target_path.unlink()
+                current_dir = os.getcwd()
+                os.chdir(str(MODEL_DIR))
+                os.symlink(selected_model, "model.joblib")
+                os.chdir(current_dir)
+                st.success(f"Link aggiornato con successo a {selected_model}")
+                time.sleep(1)
+                st.rerun()
+            except Exception as e:
+                st.error(f"Errore: {e}")
+    else:
+        st.info("Nessun modello disponibile.")
 
     st.divider()
 
-    # --- 3. AZIONI ---
-    st.markdown("### 🛠️ Azioni")
-    selected = st.selectbox("Seleziona file per operare:", df["File"].tolist())
+    # --- 2. ISPEZIONE RILIEVI E NUOVO ADDESTRAMENTO ---
+    st.write("### 🚀 Analisi Dati e Nuovo Addestramento")
     
-    if selected:
-        file_path = samples_dir / selected
-        c1, c2, c3 = st.columns([1,1,1])
-        
-        with c1:
-            if st.button("👁️ Dettagli", use_container_width=True):
-                content = pd.read_csv(file_path, sep=";")
-                st.markdown(f"**Dati di `{selected}`**")
-                st.table(content.T.reset_index().rename(columns={'index': 'AP', 0: 'RSSI'}))
-        
-        with c2:
-            with open(file_path, "rb") as f:
-                st.download_button("📥 Scarica", f, file_name=selected, use_container_width=True)
-        
-        with c3:
-            if st.button("🗑️ Elimina", use_container_width=True, type="primary"):
-                os.remove(file_path)
-                st.rerun()
+    train_cfg = cfg.get("train", {})
+    samples_dir = Path(train_cfg.get("samples_dir", "/data/train/samples"))
+    campaigns_map = get_campaigns_info(samples_dir)
+    
+    if not campaigns_map:
+        st.info("Nessun dato di rilievo trovato."); return
+
+    camp_list = sorted(campaigns_map.keys())
+    selected_camp = st.radio("Seleziona Campagna:", camp_list, horizontal=True)
+
+    current_files = campaigns_map[selected_camp]
+    files_df = pd.DataFrame([
+        {"File": os.path.basename(f), "Size": f"{round(os.path.getsize(f)/1024, 1)} KB", "Path": f} 
+        for f in current_files
+    ]).sort_values("File")
+    
+    st.dataframe(files_df[["File", "Size"]], use_container_width=True, hide_index=True)
+
+    sel_file = st.selectbox("Ispeziona contenuto file CSV:", ["-- seleziona --"] + files_df["File"].tolist())
+    if sel_file != "-- seleziona --":
+        f_path = files_df[files_df["File"] == sel_file]["Path"].iloc[0]
+        try:
+            delim = cfg.get('paths', {}).get('csv_delimiter', ';')
+            st.dataframe(pd.read_csv(f_path, sep=delim), use_container_width=True)
+        except Exception as e:
+            st.error(f"Errore lettura: {e}")
 
-    # Stats discrete in sidebar
-    st.sidebar.caption(f"Totale campioni: {len(all_files)}")
+    # Bottone di Addestramento
+    pending_jobs = list(JOBS_DIR.glob("*.lock"))
+    if pending_jobs:
+        st.warning(f"⏳ Addestramento in corso: {pending_jobs[0].name}")
+    else:
+        if st.button(f"🏋️ AVVIA ADDESTRAMENTO CAMPAGNA {selected_camp}", type="primary", use_container_width=True):
+            job_data = {
+                "campaign": selected_camp,
+                "knn": train_cfg.get("knn", {}),
+                "nan_fill": train_cfg.get("nan_fill", -110),
+                "gateways_csv": train_cfg.get("gateways_csv", "/data/config/gateway.csv"),
+                "timestamp": time.time()
+            }
+            with open(JOBS_DIR / f"train_{selected_camp}.lock", "w") as f:
+                json.dump(job_data, f)
+            st.success("Richiesta inviata al Core.")
+            time.sleep(1)
+            st.rerun()

config/config.yaml

@@ -60,7 +60,7 @@ infer:
   refresh_seconds: 10
   rssi_max: -25
   rssi_min: -110
-  window_seconds: 5
+  window_seconds: 7
   xy_round: 0
 maps:
   default_dot_size: 20
@@ -74,7 +74,7 @@ ml:
   method: knn
   metric: euclidean
   weights: distance
-mode: collect_train
+mode: all
 mqtt:
   ca_file: ''
   client_id: ble-ai-localizer
@@ -93,6 +93,7 @@ paths:
   csv_delimiter: ;
   dataset: /data/fingerprint.parquet
   gateways_csv: /data/config/gateway.csv
+  mqtt_raw_dir: /data/mqtt_raw/
 train:
   backup_existing_model: true
   gateways_csv: /data/config/gateway.csv

config/config.yaml_v1

@@ -0,0 +1,113 @@
+api:
+  audience: Fastapi
+  client_id: Fastapi
+  get_beacons_url: https://10.251.0.30:5050/reslevis/getTrackers
+  get_gateways_url: https://10.251.0.30:5050/reslevis/getGateways
+  refresh_seconds: 30
+  timeout_s: 10.0
+  token_url: https://10.251.0.30:10002/realms/API.Server.local/protocol/openid-connect/token
+  verify_tls: false
+collect_train:
+  aggregate: median
+  gateway_ready_check_before_job: true
+  gateway_ready_log_seconds: 10
+  gateway_ready_max_age_seconds: 30
+  iqr_k: 1.5
+  job_glob: '*.csv'
+  jobs_dir: /data/train/jobs
+  mad_z: 3.5
+  max_stddev: 8
+  min_non_nan: 3
+  min_samples_per_gateway: 5
+  outlier_method: mad
+  poll_seconds: 2
+  rssi_decimals: 3
+  rssi_max: -25
+  rssi_min: -110
+  samples_dir: /data/train/samples
+  window_seconds: 30
+debug:
+  collect_train_log_every_seconds: 10
+  collect_train_log_first_seen: true
+  collect_train_log_samples: true
+  csv: true
+  dump_beacons_api: true
+  dump_gateways_api: true
+  enabled: true
+  log_each_mqtt_message: false
+  log_inference_detail: true
+  log_mqtt_raw: false
+  monitor_online: true
+  offline_after_seconds_beacons: 240
+  offline_after_seconds_gateways: 90
+  online_check_seconds: 30
+  show_library_warnings: false
+  target_mac: ''
+  timezone: Europe/Rome
+  train_stats_top_k: 8
+  train_verbose: true
+infer:
+  aggregate: median
+  gateways_csv: /data/config/gateway.csv
+  include_mac: true
+  mad_z: 3.5
+  model_path: /data/model/model.joblib
+  nan_fill: -110
+  out_file: infer.csv
+  outlier_method: mad
+  output_dir: /data/infer
+  output_format: csv
+  refresh_seconds: 10
+  rssi_max: -25
+  rssi_min: -110
+  window_seconds: 5
+  xy_round: 0
+maps:
+  default_dot_size: 20
+  default_grid_size: 100
+  floor_prefix: floor_
+  map_dir: /data/maps
+  meta_prefix: meta_
+  show_grid_default: true
+ml:
+  k: 5
+  method: knn
+  metric: euclidean
+  weights: distance
+mode: infer
+mqtt:
+  ca_file: ''
+  client_id: ble-ai-localizer
+  host: 192.168.1.101
+  keepalive: 60
+  password: ''
+  port: 1883
+  protocol: mqttv311
+  qos: 0
+  tls: false
+  topic: publish_out/#
+  username: ''
+  verify_tls: false
+paths:
+  beacons_csv: /data/config/beacons.csv
+  csv_delimiter: ;
+  dataset: /data/fingerprint.parquet
+  gateways_csv: /data/config/gateway.csv
+train:
+  backup_existing_model: true
+  gateways_csv: /data/config/gateway.csv
+  knn:
+    cv:
+      enabled: false
+      folds: 5
+      k_max: 15
+      k_min: 3
+    k: 5
+    metric: euclidean
+    weights: distance
+  model_path: /data/model/model.joblib
+  nan_fill: -110
+  sample_glob: '*.csv'
+  samples_dir: /data/train/samples
+ui:
+  font_path: /app/DejaVuSans-Bold.ttf

config/config.yaml_v2

@@ -0,0 +1,68 @@
+api:
+  audience: Fastapi
+  client_id: Fastapi
+  get_beacons_url: https://10.251.0.30:5050/reslevis/getTrackers
+  get_gateways_url: https://10.251.0.30:5050/reslevis/getGateways
+  refresh_seconds: 30
+  timeout_s: 10.0
+  token_url: https://10.251.0.30:10002/realms/API.Server.local/protocol/openid-connect/token
+  verify_tls: false
+collect_train:
+  aggregate: median
+  gateway_ready_check_before_job: true
+  gateway_ready_log_seconds: 10
+  gateway_ready_max_age_seconds: 30
+  iqr_k: 1.5
+  job_glob: '*.csv'
+  jobs_dir: /data/train/jobs
+  mad_z: 3.5
+  max_stddev: 8
+  min_non_nan: 3
+  min_samples_per_gateway: 5
+  outlier_method: mad
+  poll_seconds: 2
+  rssi_decimals: 3
+  rssi_max: -25
+  rssi_min: -110
+  samples_dir: /data/train/samples
+  window_seconds: 30
+debug:
+  collect_train_log_every_seconds: 10
+  collect_train_log_first_seen: true
+infer:
+  aggregate: median
+  gateways_csv: /data/config/gateway.csv
+  include_mac: true
+  mad_z: 3.5
+  model_path: /data/model/model.joblib
+  nan_fill: -110
+  out_file: infer.csv
+  outlier_method: mad
+  output_dir: /data/infer
+  output_format: csv
+  refresh_seconds: 10
+  rssi_max: -25
+  rssi_min: -110
+  window_seconds: 5
+  xy_round: 0
+maps:
+  default_dot_size: 20
+  default_grid_size: 100
+  floor_prefix: floor_
+  map_dir: /data/maps
+  meta_prefix: meta_
+  show_grid_default: true
+ml:
+  k: 5
+  method: knn
+  metric: euclidean
+  weights: distance
+mode: all
+mqtt:
+  ca_file: ''
+  client_id: ble-ai-localizer
+  host: 192.168.1.101
+  keepalive: 60
+  password: ''
+  port: 1883
+  protocol: mqtt

config/config.yaml_v3

@@ -0,0 +1,114 @@
+api:
+  audience: Fastapi
+  client_id: Fastapi
+  get_beacons_url: https://10.251.0.30:5050/reslevis/getTrackers
+  get_gateways_url: https://10.251.0.30:5050/reslevis/getGateways
+  refresh_seconds: 30
+  timeout_s: 10.0
+  token_url: https://10.251.0.30:10002/realms/API.Server.local/protocol/openid-connect/token
+  verify_tls: false
+collect_train:
+  aggregate: median
+  gateway_ready_check_before_job: true
+  gateway_ready_log_seconds: 10
+  gateway_ready_max_age_seconds: 30
+  iqr_k: 1.5
+  job_glob: '*.csv'
+  jobs_dir: /data/train/jobs
+  mad_z: 3.5
+  max_stddev: 8
+  min_non_nan: 3
+  min_samples_per_gateway: 5
+  outlier_method: mad
+  poll_seconds: 2
+  rssi_decimals: 3
+  rssi_max: -25
+  rssi_min: -110
+  samples_dir: /data/train/samples
+  window_seconds: 30
+debug:
+  collect_train_log_every_seconds: 10
+  collect_train_log_first_seen: true
+  collect_train_log_samples: true
+  csv: true
+  dump_beacons_api: true
+  dump_gateways_api: true
+  enabled: true
+  log_each_mqtt_message: false
+  log_inference_detail: true
+  log_mqtt_raw: false
+  monitor_online: true
+  offline_after_seconds_beacons: 240
+  offline_after_seconds_gateways: 90
+  online_check_seconds: 30
+  show_library_warnings: false
+  target_mac: ''
+  timezone: Europe/Rome
+  train_stats_top_k: 8
+  train_verbose: true
+infer:
+  aggregate: median
+  gateways_csv: /data/config/gateway.csv
+  include_mac: true
+  mad_z: 3.5
+  model_path: /data/model/model.joblib
+  nan_fill: -110
+  out_file: infer.csv
+  outlier_method: mad
+  output_dir: /data/infer
+  output_format: csv
+  refresh_seconds: 10
+  rssi_max: -25
+  rssi_min: -110
+  window_seconds: 5
+  xy_round: 0
+maps:
+  default_dot_size: 20
+  default_grid_size: 100
+  floor_prefix: floor_
+  map_dir: /data/maps
+  meta_prefix: meta_
+  show_grid_default: true
+ml:
+  k: 5
+  method: knn
+  metric: euclidean
+  weights: distance
+mode: all
+mqtt:
+  ca_file: ''
+  client_id: ble-ai-localizer
+  host: 192.168.1.101
+  keepalive: 60
+  password: ''
+  port: 1883
+  protocol: mqttv311
+  qos: 0
+  tls: false
+  topic: publish_out/#
+  username: ''
+  verify_tls: false
+paths:
+  beacons_csv: /data/config/beacons.csv
+  csv_delimiter: ;
+  dataset: /data/fingerprint.parquet
+  gateways_csv: /data/config/gateway.csv
+  mqtt_raw_dir: /data/mqtt_raw/
+train:
+  backup_existing_model: true
+  gateways_csv: /data/config/gateway.csv
+  knn:
+    cv:
+      enabled: false
+      folds: 5
+      k_max: 15
+      k_min: 3
+    k: 5
+    metric: euclidean
+    weights: distance
+  model_path: /data/model/model.joblib
+  nan_fill: -110
+  sample_glob: '*.csv'
+  samples_dir: /data/train/samples
+ui:
+  font_path: /app/DejaVuSans-Bold.ttf

data/.web_state

@@ -1 +0,0 @@
-running

data/config/beacongroup.csv

@@ -0,0 +1,2 @@
+BeaconGroupName;GroupMAC
+GBC-00;C3:00:00:57:B9:E6,C3:00:00:57:B9:D4,C3:00:00:57:B9:E8,C3:00:00:57:B9:F1

data/config/beacons.csv

@@ -1,6 +1,7 @@
 BeaconName;MAC
-BC-21;C3:00:00:57:B9:E6
-BC-22;C3:00:00:57:B9:D4
-BC-23;C3:00:00:57:B9:E8
-BC-24;C3:00:00:57:B9:F1
-BC-25;C3:00:00:57:B9:E7
+BC-00-21;C3:00:00:57:B9:E6
+BC-04-22;C3:00:00:57:B9:D4
+BC-08-23;C3:00:00:57:B9:E8
+BC-12-24;C3:00:00:57:B9:F1
+BC-00-25;C3:00:00:57:B9:E7
+BC-04-26;C3:00:00:57:B9:E9

data/infer/infer.csv

@@ -1,13 +1,13 @@
 mac;z;x;y
-C8:3F:8F:17:DB:35;1;1058;1294
+C8:3F:8F:17:DB:35;-1;-1;-1
 C3:00:00:39:47:DF;-1;-1;-1
-C3:00:00:39:47:C4;1;1304;1478
+C3:00:00:39:47:C4;-1;1917;650
 C3:00:00:39:47:E2;-1;-1;-1
-C7:AE:56:1E:38:B7;1;1323;1499
+C7:AE:56:1E:38:B7;-1;1917;650
 E0:1F:9A:7A:47:D2;-1;-1;-1
-C3:00:00:57:B9:D9;1;1058;1382
-C3:00:00:57:B9:DB;1;1060;1293
-C3:00:00:57:B9:F4;1;1059;1296
-C3:00:00:57:B9:DC;1;1060;1291
-C3:00:00:57:B9:DD;1;1203;1452
-C3:00:00:57:B9:DF;1;1061;1294
+C3:00:00:57:B9:D9;-1;1917;650
+C3:00:00:57:B9:DB;-1;1917;650
+C3:00:00:57:B9:F4;-1;1917;650
+C3:00:00:57:B9:DC;-1;1917;650
+C3:00:00:57:B9:DD;-1;1917;650
+C3:00:00:57:B9:DF;-1;1917;650

data/maps.bck/meta_0.json

@@ -1 +0,0 @@
-{"pixel_ratio": 1.5843674344166205, "calibrated": true, "origin": [1296, 3744], "grid_size": 50, "show_grid": true}

data/maps.bck/meta_1.json

@@ -1 +0,0 @@
-{"pixel_ratio": 1.5919341046277666, "calibrated": true, "origin": [1251, 1143], "grid_size": 50}

data/maps.old/meta_0.json

@@ -1,9 +0,0 @@
-{
-    "pixel_ratio": 1.584476207254857,
-    "calibrated": true,
-    "origin": [
-        1248,
-        3790
-    ],
-    "grid_size": 50
-}

data/maps.old/meta_1.json

@@ -1 +0,0 @@
-{"pixel_ratio": 1.5924732396126005, "calibrated": true, "origin": [1297, 3586], "grid_size": 50}

data/model/model.joblib

@@ -0,0 +1 @@
+model_camp_00_20260209_004706.joblib

data/mqtt_raw/mqtt_raw_20260209_023636.log

@@ -0,0 +1,313 @@
+publish_out/ac233fc1dd49 [{"timestamp":"2026-02-09T00:36:36.583Z","type":"Gateway","mac":"AC233FC1DD49","nums":1},{"timestamp":"2026-02-09T00:36:36.944Z","mac":"C300003947C4","rssi":-82,"rawData":"0201060303AAFE1116AAFE20000C2A100002C32FFD152F8478"}]
+publish_out/ac233fc1dd40 [{"timestamp":"2026-02-09T00:36:36.561Z","type":"Gateway","mac":"AC233FC1DD40","nums":2},{"timestamp":"2026-02-09T00:36:36.649Z","mac":"C3000057B9F5","rssi":-81,"rawData":"0201060303AAFE1116AAFE20000C2410000206F1D707BA77D0"},{"timestamp":"2026-02-09T00:36:37.048Z","mac":"C3000057B9DC","rssi":-75,"rawData":"0201060303AAFE1116AAFE20000C27100001F0BC260776BB62"}]
+publish_out/ac233fc1dd51 [{"timestamp":"2026-02-09T00:36:36.654Z","type":"Gateway","mac":"AC233FC1DD51","nums":5},{"timestamp":"2026-02-09T00:36:36.849Z","mac":"C7AE561E38B7","rssi":-82,"rawData":"02010617FF00020000FF0000FF0000FF0000000000000000052A0D03095336"},{"timestamp":"2026-02-09T00:36:36.865Z","mac":"C3000057B9F4","rssi":-72,"rawData":"0201060303AAFE1116AAFE20000C2A100002089BA307BE4374"},{"timestamp":"2026-02-09T00:36:37.090Z","mac":"C3000057B9DB","rssi":-74,"rawData":"0201060303AAFE1116AAFE20000C2410000204664807B0D432"},{"timestamp":"2026-02-09T00:36:37.154Z","mac":"C7AE561E38B7","rssi":-82,"rawData":"02010617FF000100000000000000000000C30600006F4C0000460003095336"},{"timestamp":"2026-02-09T00:36:37.459Z","mac":"C7AE561E38B7","rssi":-82,"rawData":"02010617FF000100000000000000000000C30600006F4C0000460003095336"}]
+publish_out/ac233fc1dd3c [{"timestamp":"2026-02-09T00:36:36.747Z","type":"Gateway","mac":"AC233FC1DD3C","nums":6},{"timestamp":"2026-02-09T00:36:36.944Z","mac":"C300003947C4","rssi":-77,"rawData":"0201060303AAFE1116AAFE20000C2A100002C32FFD152F8478"},{"timestamp":"2026-02-09T00:36:36.983Z","mac":"C3000057B9D4","rssi":-77,"rawData":"0201060303AAFE1116AAFE20000C540F0002000BA206BB32CA"},{"timestamp":"2026-02-09T00:36:37.370Z","mac":"E017085443A7","rssi":-75,"rawData":"0201060C16E0FE2001000000000100000A094D4B20427574746F6E"},{"timestamp":"2026-02-09T00:36:37.391Z","mac":"C3000057B9E8","rssi":-81,"rawData":"0201060303AAFE1116AAFE20000C30110001ECF6F406F29E22"},{"timestamp":"2026-02-09T00:36:37.463Z","mac":"504C97555B00","rssi":-81,"rawData":"020106020A09110712A24D2EFE14488E93D2173CFFE00000051206001000"},{"timestamp":"2026-02-09T00:36:37.667Z","mac":"504C97555B00","rssi":-76,"rawData":"020106020A09110712A24D2EFE14488E93D2173CFFE00000051206001000"}]
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data/train/jobs/done/0_1550_850.csv

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data/train/jobs/done/1_1050_1450.csv

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data/train/jobs/done/1_1250_600.csv

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data/train/jobs/done/1_1600_450.csv

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data/train/jobs/done/BC-21_-1_1050_250.csv

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data/train/jobs/done/BC-21_0_195_1425.csv

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data/train/jobs/done/BC-21_0_2114_796.csv

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data/train/jobs/done/BC-21_0_2115_697.csv

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data/train/jobs/done/BC-21_0_2568_946.csv

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data/train/jobs/done/BC-21_0_400_100.csv

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data/train/jobs/done/BC-21_0_619_122.csv

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data/train/jobs/done/BC-21_1_1050_500.csv

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data/train/jobs/done/BC-21_1_950_750.csv

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data/train/jobs/done/BC-22_-1_1650_1100.csv

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data/train/jobs/done/BC-22_-1_170_1418.csv

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data/train/jobs/done/BC-22_0_534_999.csv

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data/train/jobs/done/BC-23_0_1580_180.csv

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data/train/jobs/done/BC-23_1_130_1386.csv

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data/train/jobs/done/BC-23_1_1800_600.csv

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data/train/jobs/done/BC-24_1_1900_1400.csv

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data/train/jobs/done/BC-24_1_2200_1000.csv

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data/train/jobs/done/BC-25_0_562_239.csv

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data/train/jobs/done/BC-25_1_1450_600.csv

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data/train/jobs/done/BC-25_1_2400_300.csv

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data/train/jobs/done/BC-25_1_2500_1300.csv

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data/train/jobs/done/GBC-00_-1_1291_655.csv

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data/train/jobs/done/GBC-00_-1_1417_1081.csv

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data/train/jobs/done/GBC-00_-1_152_1379.csv

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data/train/jobs/done/GBC-00_-1_1687_221.csv

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data/train/jobs/done/GBC-00_-1_2122_693.csv

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data/train/jobs/done/GBC-00_-1_2547_235.csv

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data/train/jobs/done/GBC-00_-1_2642_1057.csv

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data/train/jobs/done/GBC-00_0_2925_1485.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_2925_1485;0;BC-00-21;2925;1485;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_2925_1485;0;BC-04-22;2925;1485;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_2925_1485;0;BC-08-23;2925;1485;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_2925_1485;0;BC-12-24;2925;1485;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-00_0_70_1485.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_70_1485;0;BC-00-21;70;1485;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_70_1485;0;BC-04-22;70;1485;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_70_1485;0;BC-08-23;70;1485;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_70_1485;0;BC-12-24;70;1485;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-00_0_98_58.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_98_58;0;BC-00-21;98;58;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_98_58;0;BC-04-22;98;58;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_98_58;0;BC-08-23;98;58;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_98_58;0;BC-12-24;98;58;0;BC-12-24;C3:00:00:57:B9:F1