ottimizzato rileva per tablet

README.md

@@ -1,466 +1,95 @@
-Progetto Container Docker ble-ai-localizer
-Ambiente: MN reslevis 192.168.1.3
-	  m1.MajorNet-x64.6.6.0-60.bin:03 October 2025
-	  server linux gentoo kernl: 6.6.74-gentoo-x86_64 Python default version 3.10.16
+# 🛰️ BLE AI Localizer - README
 
-cd /data/service/
+Progetto di localizzazione indoor basato su segnali Bluetooth (BLE) e Machine Learning (KNN).
+Ambiente: MN reslevis 192.168.1.3 | Python 3.10.16 | Docker 27.4.1.
 
-###########################################################################################
-Passo 1  Creazione struttura progetto (sul server)
-	mkdir -p ble-ai-localizer/{app,config,data,models}
-	cd /data/service/ble-ai-localizer
+---
 
-		touch Dockerfile requirements.txt entrypoint.sh docker-compose.yml
-		touch app/{__init__.py,main.py,settings.py,auth.py,mqtt_client.py,train.py,infer.py}
-		touch config/config.yaml config/secrets.yaml
+## 🛠️ Architettura e Struttura Progetto
+Il sistema è diviso tra un **Core Engine** (background) e una **Management Suite** (UI Streamlit).
 
-	Cosa hai ottenuto
-		app/ codice python (training + inference)
-		config/ configurazione (config.yaml versionabile + secrets.yaml non versionabile)
-		data/ dataset fingerprint (volume)
-		models/ modello salvato (volume)
+### File Operativi Principali
+* **`web_suite.py`**: Punto d'ingresso unico (Login, UI Management, MQTT Logging).
+* **`main.py`**: Orchestratore dei processi background (Inference & Training).
+* **`infer_mode.py`**: Motore di calcolo real-time che processa il traffico MQTT.
+* **`web_test_inference.py`**: Tool di validazione offline con mappa Folium interattiva.
+* **`web_status.py`**: Monitoraggio infrastruttura (Core, MQTT Broker, API, Disco).
 
-###########################################################################################
-Passo 2 .gitignore (consigliato subito)
-	Se usi git:
-cat > .gitignore << 'EOF'
-# secrets e artefatti
-config/secrets.yaml
-data/
-models/
+---
 
-# python
-__pycache__/
-*.pyc
-.venv/
+## 🚀 Note per l'Operatore: Campagna di Rilevazione
 
-# editor
-.vscode/
-.idea/
-EOF
+Per garantire la massima precisione del sistema, attenersi rigorosamente a queste linee guida durante la raccolta delle fingerprint.
 
-###########################################################################################
-Passo 3  Verifica prerequisiti Docker (Gentoo)
+### 1. Parametri Algoritmo (KNN)
+* **Valore di k**: Rappresenta il calcolo sulla retta ($k=2$) o sul piano ($k>2$) tra i beacon di training.
+* **Vincolo**: $k$ deve coincidere come minimo con il numero di beacon per stanza.
+* **Consiglio**: Se si catturano 5 misure per stanza, impostare **$k=3$**. Il parametro è globale.
 
-Esegui:
+### 2. Linee Guida per la Raccolta (Training)
+* **Distanza Pareti**: Rilevare i punti ad almeno **1m dalle pareti** (evitare angoli o pareti adiacenti).
+* **Simmetria**: Per piani simmetrici, far coincidere le misure di ogni piano nello stesso punto spaziale.
+* **Beacon di Test**: Durante la raccolta includere almeno un beacon di test per ogni potenza. Verrà escluso dal training ma usato per validare l'inferenza sul traffico registrato.
 
-docker --version
-docker compose version
+### 3. Configurazione Hardware Beacon
+* **Fase di Raccolta**: TX Power variabile (0, -4, -8, -12 dBm) | TX Interval: **200 ms**.
+* **Fase di Produzione**: Potenza ottimale risultata dai test | TX Interval: **1400 ms**.
 
+### 4. Parametri Temporali (`config.yaml`)
+* **Slot di raccolta**: 30 secondi per ogni punto di fingerprint.
+* **Finestra di Inferenza**: 7 secondi.
 
-Se docker compose non esiste, di solito su Gentoo:
+---
 
-o hai il plugin docker-cli-compose
+## 🖥️ Management Suite (Interfaccia Web)
 
-o usi docker-compose (legacy). In tal caso dimmelo e ti adatto i comandi.
+### Accesso
+* **URL**: `http://192.168.1.3:8501`.
+* **Credenziali**: Configurate nel `docker-compose.yml` (Default: `Admin` / `pwdadmin1`).
 
-Quando hai eseguito i comandi del Passo 1–3, nel prossimo step ti faccio creare:
+### Funzionalità InferTest (Validazione)
+Nella tab **🧪 InferTest** è possibile validare i modelli caricando file di test:
+* 🔵 **Celeste**: Punto di Test (posizione reale registrata).
+* 🟠 **Arancione**: Punto Predetto (posizione calcolata dal modello).
+* 🟡 **Linea Gialla**: Scostamento metrico (Errore Distanza).
+La mappa supporta lo zoom persistente e la navigazione Folium.
 
-requirements.txt minimale + pinned
+### MQTT Raw Logging
+Dalla Sidebar è possibile attivare la registrazione del traffico grezzo.
+* **Log Directory**: `/data/mqtt_raw/`.
+* **Comando interno**: `mosquitto_sub` filtrato su `publish_out` con parametri presi dinamicamente dal file `config.yaml`.
 
-Dockerfile compatibile (python:3.10-slim)
+---
 
-docker-compose.yml con volumi (config/, data/, models/) e variabile MODE=train|infer
+## ⚙️ Gestione Docker (SysAdmin)
 
-Se vuoi andare avanti subito senza feedback, incolla qui l’output di:
-
-docker --version
-docker compose version
-
-docker compose version
-Docker version 27.4.1, build b9d17eaebb55b7652ce37ae5c7c52fcb34194956
-Docker Compose version v2.28.1
-
-Perfetto, sei a posto (Docker 27.4.1 + Compose v2.28.1).
-
-###########################################################################################
-Passo 4 — Popoliamo i file base (requirements, Dockerfile, entrypoint, compose)
-4.1 requirements.txt
-
-Metti dentro (pinned e minimale per il tuo caso):
-
-cat > requirements.txt << 'EOF'
-pandas==2.2.2
-numpy==1.26.4
-scikit-learn==1.5.1
-joblib==1.4.2
-
-paho-mqtt==2.1.0
-requests==2.32.3
-PyYAML==6.0.2
-
-fastapi==0.115.0
-uvicorn[standard]==0.30.6
-
-matplotlib==3.9.2
-seaborn==0.13.2
-EOF
-
-
-(Se poi decidiamo che plot non servono in produzione, li togliamo.)
-
-4.2 Dockerfile (Python “fissato”, non-root, robusto)
-cat > Dockerfile << 'EOF'
-FROM python:3.10-slim
-
-# dipendenze native utili a numpy/scikit (safe choice)
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential gcc g++ \
-    && rm -rf /var/lib/apt/lists/*
-
-WORKDIR /app
-
-COPY requirements.txt /app/requirements.txt
-RUN pip install --no-cache-dir -r /app/requirements.txt
-
-COPY app/ /app/app/
-COPY entrypoint.sh /app/entrypoint.sh
-RUN chmod +x /app/entrypoint.sh
-
-# utente non-root
-RUN useradd -m appuser
-USER appuser
-
-# cartelle dati/modelli (volumi)
-RUN mkdir -p /home/appuser/data /home/appuser/models
-ENV DATA_DIR=/home/appuser/data
-ENV MODELS_DIR=/home/appuser/models
-
-ENTRYPOINT ["/app/entrypoint.sh"]
-EOF
-
-4.3 entrypoint.sh (MODE=train|infer|api)
-cat > entrypoint.sh << 'EOF'
-#!/usr/bin/env bash
-set -euo pipefail
-
-MODE="${MODE:-infer}"
-
-case "$MODE" in
-  train)
-    exec python -m app.main train
-    ;;
-  infer)
-    exec python -m app.main infer
-    ;;
-  api)
-    exec uvicorn app.main:api --host 0.0.0.0 --port "${PORT:-8080}"
-    ;;
-  *)
-    echo "MODE non valido: $MODE (usa train|infer|api)"
-    exit 1
-    ;;
-esac
-EOF
-
-
-Poi rendilo eseguibile:
-
-chmod +x entrypoint.sh
-
-4.4 docker-compose.yml (volumi + config esterna)
-cat > docker-compose.yml << 'EOF'
-services:
-  ble-ai-localizer:
-    build: .
-    image: ble-ai-localizer:0.1.0
-    environment:
-      MODE: "infer"
-      CONFIG_FILE: "/app/config/config.yaml"
-      SECRETS_FILE: "/app/config/secrets.yaml"
-    volumes:
-      - ./config:/app/config:ro
-      - ./data:/data
-      - ./models:/models
-    restart: unless-stopped
-EOF
-
-4.5 config/config.yaml (scheletro)
-cat > config/config.yaml << 'EOF'
-mqtt:
-  host: "mosquitto"
-  port: 1883
-  topic: "ble/raw"
-
-api:
-  get_gateways_url: "https://APIHOST:5050/reslevis/getGateways"
-  verify_tls: false
-  refresh_seconds: 300
-
-oidc:
-  token_url: "https://KEYCLOAK/realms/REALM/protocol/openid-connect/token"
-  client_id: "Fastapi"
-  audience: "Fastapi"
-
-paths:
-  dataset: "/data/fingerprint.parquet"
-  model: "/models/model.joblib"
-
-ml:
-  # parametri generali; l'algoritmo è un dettaglio interno
-  method: "knn"
-  k: 7
-  weights: "distance"
-  metric: "euclidean"
-EOF
-
-4.6 config/secrets.yaml (placeholder, non versionare)
-cat > config/secrets.yaml << 'EOF'
-oidc:
-  client_secret: "CHANGE_ME"
-  username: "CHANGE_ME"
-  password: "CHANGE_ME"
-EOF
-
-###########################################################################################
-Passo 5 Metti un main minimale per verificare che il container parte
-app/main.py
-cat > app/main.py << 'EOF'
-from fastapi import FastAPI
-from .settings import load_settings
-
-api = FastAPI()
-
-@api.get("/health")
-def health():
-    return {"status": "ok"}
-
-def main():
-    import sys
-    settings = load_settings()
-    print("Settings loaded. Keys:", list(settings.keys()))
-
-    if len(sys.argv) < 2:
-        raise SystemExit("Usage: python -m app.main [train|infer]")
-
-    cmd = sys.argv[1].lower()
-    if cmd == "train":
-        print("TRAIN mode (placeholder)")
-    elif cmd == "infer":
-        print("INFER mode (placeholder)")
-    else:
-        raise SystemExit("Unknown command")
-
-if __name__ == "__main__":
-    main()
-EOF
-
-app/settings.py
-cat > app/settings.py << 'EOF'
-import os
-from pathlib import Path
-import yaml
-
-def _read_yaml(path: str) -> dict:
-    with open(path, "r", encoding="utf-8") as f:
-        return yaml.safe_load(f) or {}
-
-def deep_merge(a: dict, b: dict) -> dict:
-    out = dict(a or {})
-    for k, v in (b or {}).items():
-        if isinstance(v, dict) and isinstance(out.get(k), dict):
-            out[k] = deep_merge(out[k], v)
-        else:
-            out[k] = v
-    return out
-
-def load_settings() -> dict:
-    cfg_path = os.getenv("CONFIG_FILE", "/app/config/config.yaml")
-    settings = _read_yaml(cfg_path)
-
-    secrets_path = os.getenv("SECRETS_FILE", "")
-    if secrets_path and Path(secrets_path).exists():
-        secrets = _read_yaml(secrets_path)
-        settings = deep_merge(settings, secrets)
-
-    # fallback paths (coerenti con compose)
-    settings.setdefault("paths", {})
-    settings["paths"].setdefault("dataset", os.getenv("DATASET_PATH", "/data/fingerprint.parquet"))
-    settings["paths"].setdefault("model", os.getenv("MODEL_PATH", "/models/model.joblib"))
-
-    return settings
-EOF
-
-(Gli altri file per ora possono restare vuoti.)
-
-###########################################################################################
-Passo 6 Build & test smoke test
-	docker compose build
-
-
-###########################################################################################
-###  END prima creazione container                                        #################
-###########################################################################################
-
-
-
-
-###########################################################################################
-1) Come avviare SOLO ble-ai-localizer (senza impattare gli altri)
-
-	Vai nella directory del progetto:
-		cd /data/service/ble-ai-localizer
-
-	Avvio in background:
-		docker compose -p ble-ai-localizer up -d
-			Perché -p ble-ai-localizer?
-			Imposta esplicitamente il project name, così sei sicuro al 100% di nonagganciare per errore un altro compose.
-
-	Verifica stato (solo di questo progetto):
-		docker compose -p ble-ai-localizer ps
-
-	Log (solo di questo progetto):
-		docker compose -p ble-ai-localizer logs -f
-
-
-2) Come stoppare/riavviare SOLO ble-ai-localizer
-
-	Stop (non rimuove container):
-		docker compose -p ble-ai-localizer stop
-
-	Restart:
-		docker compose -p ble-ai-localizer restart
-
-	Stop + rimozione container/network del progetto (NON tocca volumi bind ./data, ./models):
-		docker compose -p ble-ai-localizer down
-
-3) Aggiornare solo il tuo container (codice o Dockerfile cambiato)
-
-	Ricostruisci l'immagine:
-		docker compose -p ble-ai-localizer build
-
-	Riavvia applicando l'immagine nuova:
-		docker compose -p ble-ai-localizer up -d
-
-	Se vuoi forzare rebuild+restart in un colpo:
-		docker compose -p ble-ai-localizer up -d --build
-
-
-4) Esportare l'immagine (backup o deploy su altro server)
-	Esempio export in tar (meglio gzippato):
-		docker save ble-ai-localizer:0.1.0 | gzip > ble-ai-localizer_0.1.0.tar.gz
-	Su altro server:
-		gzip -dc ble-ai-localizer_0.1.0.tar.gz | docker load
-
-#Debug
-	mosquitto_sub -v -h 192.168.1.101 -p 1883 -t '#' -V mqttv311  | grep publish_out
-
-	docker compose -p ble-ai-localizer exec -T ble-ai-localizer ls -l /data/config/
-
-	#Caso di gateway che non rileva nessun beacon
-	publish_out/ac233fc1dcd3 [{"timestamp":"2026-01-30T13:40:08.885Z","type":"Gateway","mac":"AC233FC1DCD3","nums":0}]
-
-
-#Verifica del modello in uso:
-#Ver 1
-docker compose -p ble-ai-localizer exec -T ble-ai-localizer python - <<'PY'
-import hashlib, joblib, os
-p="/data/model/model.joblib"
-b=open(p,"rb").read()
-print(f"FILE: {p}")
-print(f"sha256={hashlib.sha256(b).hexdigest()[:12]} size={len(b)} bytes")
-m=joblib.load(p)
-print("TYPE:", type(m))
-for k in ["version","nan_fill","k_floor","k_xy","weights","metric","floors"]:
-    print(f"{k}:", getattr(m,k,None))
-gws=getattr(m,"feature_gateways",[])
-print("gateways:", len(gws), "first:", gws[:5])
-regs=getattr(m,"xy_regs",{})
-print("xy_regs floors:", sorted(list(regs.keys())))
-PY
-service "ble-ai-localizer" is not running
-
-#Ver 2
-docker compose -p ble-ai-localizer exec -T ble-ai-localizer python - <<'PY'
-import joblib, pprint
-m = joblib.load("/data/model/model.joblib")
-keys = [
-  "created_at_utc","sklearn_version","numpy_version",
-  "gateways_order","nan_fill","k_floor","k_xy","weights","metric","floors"
-]
-pprint.pprint({k: m.get(k) for k in keys})
-PY
-
-#Esempio utilizzo server API:
-Ottenimento del token:
-TOKEN=$(
-  curl -k -s -X POST "https://10.251.0.30:10002/realms/API.Server.local/protocol/openid-connect/token" \
-    -H "Content-Type: application/x-www-form-urlencoded" \
-    -d "grant_type=password" \
-    -d "client_id=Fastapi" \
-    -d "client_secret=wojuoB7Z5xhlPFrF2lIxJSSdVHCApEgC" \
-    -d "username=core" \
-    -d "password=C0r3_us3r_Cr3d3nt14ls" \
-    -d "audience=Fastapi" \
-  | jq -r '.access_token'
-)
-
-Utilizzare il token in un API:
-
-curl -k -X 'GET' \
-  'https://10.251.0.30:5050/reslevis/getTrackers' \
-  -H 'accept: application/json' \
-  -H 'Authorization: Bearer $TOKEN'
-
-
-
-
-#Aggiornamento del software
-	cd /data/service/ble-ai-localizer
-	docker compose up -d --build
-	docker compose -p ble-ai-localizer build
-	docker compose -p ble-ai-localizer up -d --build
-	docker system prune
+####Aggiornamento del software
+```bash
+    cd /data/service/ble-ai-localizer
+    docker compose up -d --build
+    docker compose -p ble-ai-localizer build
+    docker compose -p ble-ai-localizer up -d --build
+    docker system prune
     docker rmi ble-ai-localizer:0.1.0
 
-Gestione Sart/Stop Container
-	cd /data/service/ble-ai-localizer
-	docker compose -p ble-ai-localizer up -d
-	docker compose -p ble-ai-localizer logs -f --tail=200 --timestamps
-	docker compose -p ble-ai-localizer stop
-	docker compose -p ble-ai-localizer restart
-	docker compose -p ble-ai-localizer down
-
-
-Accesso Web MajorNET ResLevis:
-	https://10.251.0.30/frontend/app_reslevis/app.html#home
-
-Accesso Web a Container ble-ai-localizer
-	URL: http://0.0.0.0:8501
-	http://192.168.1.3:8501/
-		username e password da file composer: docker-compose.yml
-      			UI_USER: "Admin"
-      			UI_PASSWORD: "pwdadmin1"  <-- facilitate per accesso
-			da mobile
-
-
-
-
-NOTE:
-- Il valore di k utilizzato rappresenta la retta (k=2) o il piano (k>2) tra i
-  beacon di trainig, in addestramento deve coincidere come minimo con il
-numero di beacon per stanza, ma è un parametro globale per cui va fatto
-rispettare per tutte le stanze,  per cui ad esempio se si decide di catturare 5
-misure per stanza k conviene metterlo a 3
-
-- almeno a 1m dalle pareti della stanza (evitare attaccato al muro su pareti
-  adiacenti)
-
-- per piani simemtrici le misure di ogni piano conviene farle conincidere
-
-- aggingere timestamp in fingerprint e registarre traffico mqtt raw (a
-  parita' di finestra di registrazione potranno essere rivalutati i valori
-  letti)
+###Gestione Sart/Stop Container
+```bash
+    cd /data/service/ble-ai-localizer
+    docker compose -p ble-ai-localizer up -d
+    docker compose -p ble-ai-localizer logs -f --tail=200 --timestamps
+    docker compose -p ble-ai-localizer stop
+    docker compose -p ble-ai-localizer restart
+    docker compose -p ble-ai-localizer down
 
-- Tempo di tx nel beacon 200 ms
-- potenze 0, -4 ,-8, -12
-- slot di raccolta 30s
 
-- time 1400
-- Inferenza a 7 sec
+###Accesso Web MajorNET ResLevis:
+```bash
+    https://10.251.0.30/frontend/app_reslevis/app.html#home
 
-- durante la raccolta occorre almeno un beacon di test per potenza che veiene escluso
-  nell'addestarmento ma usato per l'inferenza con il traffico registrato nella
-  fase di raccolta.
+ Accesso Web a Container ble-ai-localizer
+    URL: http://0.0.0.0:8501
+    http://192.168.1.3:8501/
+        username e password da file composer: docker-compose.yml
+                UI_USER: "Admin"
+                UI_PASSWORD: "pwdadmin1"  <-- facilitate per accesso da mobile
 
-- testare prima gw e mgtt se regge 200 ms

README.md.old

@@ -0,0 +1,495 @@
+Progetto Container Docker ble-ai-localizer
+Ambiente: MN reslevis 192.168.1.3
+	  m1.MajorNet-x64.6.6.0-60.bin:03 October 2025
+	  server linux gentoo kernl: 6.6.74-gentoo-x86_64 Python default version 3.10.16
+
+cd /data/service/
+
+###########################################################################################
+Passo 1  Creazione struttura progetto (sul server)
+	mkdir -p ble-ai-localizer/{app,config,data,models}
+	cd /data/service/ble-ai-localizer
+
+		touch Dockerfile requirements.txt entrypoint.sh docker-compose.yml
+		touch app/{__init__.py,main.py,settings.py,auth.py,mqtt_client.py,train.py,infer.py}
+		touch config/config.yaml config/secrets.yaml
+
+	Cosa hai ottenuto
+		app/ codice python (training + inference)
+		config/ configurazione (config.yaml versionabile + secrets.yaml non versionabile)
+		data/ dataset fingerprint (volume)
+		models/ modello salvato (volume)
+
+###########################################################################################
+Passo 2 .gitignore (consigliato subito)
+	Se usi git:
+cat > .gitignore << 'EOF'
+# secrets e artefatti
+config/secrets.yaml
+data/
+models/
+
+# python
+__pycache__/
+*.pyc
+.venv/
+
+# editor
+.vscode/
+.idea/
+EOF
+
+###########################################################################################
+Passo 3  Verifica prerequisiti Docker (Gentoo)
+
+Esegui:
+
+docker --version
+docker compose version
+
+
+Se docker compose non esiste, di solito su Gentoo:
+
+o hai il plugin docker-cli-compose
+
+o usi docker-compose (legacy). In tal caso dimmelo e ti adatto i comandi.
+
+Quando hai eseguito i comandi del Passo 1–3, nel prossimo step ti faccio creare:
+
+requirements.txt minimale + pinned
+
+Dockerfile compatibile (python:3.10-slim)
+
+docker-compose.yml con volumi (config/, data/, models/) e variabile MODE=train|infer
+
+Se vuoi andare avanti subito senza feedback, incolla qui l’output di:
+
+docker --version
+docker compose version
+
+docker compose version
+Docker version 27.4.1, build b9d17eaebb55b7652ce37ae5c7c52fcb34194956
+Docker Compose version v2.28.1
+
+Perfetto, sei a posto (Docker 27.4.1 + Compose v2.28.1).
+
+###########################################################################################
+Passo 4 — Popoliamo i file base (requirements, Dockerfile, entrypoint, compose)
+4.1 requirements.txt
+
+Metti dentro (pinned e minimale per il tuo caso):
+
+cat > requirements.txt << 'EOF'
+pandas==2.2.2
+numpy==1.26.4
+scikit-learn==1.5.1
+joblib==1.4.2
+
+paho-mqtt==2.1.0
+requests==2.32.3
+PyYAML==6.0.2
+
+fastapi==0.115.0
+uvicorn[standard]==0.30.6
+
+matplotlib==3.9.2
+seaborn==0.13.2
+EOF
+
+
+(Se poi decidiamo che plot non servono in produzione, li togliamo.)
+
+4.2 Dockerfile (Python “fissato”, non-root, robusto)
+cat > Dockerfile << 'EOF'
+FROM python:3.10-slim
+
+# dipendenze native utili a numpy/scikit (safe choice)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential gcc g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir -r /app/requirements.txt
+
+COPY app/ /app/app/
+COPY entrypoint.sh /app/entrypoint.sh
+RUN chmod +x /app/entrypoint.sh
+
+# utente non-root
+RUN useradd -m appuser
+USER appuser
+
+# cartelle dati/modelli (volumi)
+RUN mkdir -p /home/appuser/data /home/appuser/models
+ENV DATA_DIR=/home/appuser/data
+ENV MODELS_DIR=/home/appuser/models
+
+ENTRYPOINT ["/app/entrypoint.sh"]
+EOF
+
+4.3 entrypoint.sh (MODE=train|infer|api)
+cat > entrypoint.sh << 'EOF'
+#!/usr/bin/env bash
+set -euo pipefail
+
+MODE="${MODE:-infer}"
+
+case "$MODE" in
+  train)
+    exec python -m app.main train
+    ;;
+  infer)
+    exec python -m app.main infer
+    ;;
+  api)
+    exec uvicorn app.main:api --host 0.0.0.0 --port "${PORT:-8080}"
+    ;;
+  *)
+    echo "MODE non valido: $MODE (usa train|infer|api)"
+    exit 1
+    ;;
+esac
+EOF
+
+
+Poi rendilo eseguibile:
+
+chmod +x entrypoint.sh
+
+4.4 docker-compose.yml (volumi + config esterna)
+cat > docker-compose.yml << 'EOF'
+services:
+  ble-ai-localizer:
+    build: .
+    image: ble-ai-localizer:0.1.0
+    environment:
+      MODE: "infer"
+      CONFIG_FILE: "/app/config/config.yaml"
+      SECRETS_FILE: "/app/config/secrets.yaml"
+    volumes:
+      - ./config:/app/config:ro
+      - ./data:/data
+      - ./models:/models
+    restart: unless-stopped
+EOF
+
+4.5 config/config.yaml (scheletro)
+cat > config/config.yaml << 'EOF'
+mqtt:
+  host: "mosquitto"
+  port: 1883
+  topic: "ble/raw"
+
+api:
+  get_gateways_url: "https://APIHOST:5050/reslevis/getGateways"
+  verify_tls: false
+  refresh_seconds: 300
+
+oidc:
+  token_url: "https://KEYCLOAK/realms/REALM/protocol/openid-connect/token"
+  client_id: "Fastapi"
+  audience: "Fastapi"
+
+paths:
+  dataset: "/data/fingerprint.parquet"
+  model: "/models/model.joblib"
+
+ml:
+  # parametri generali; l'algoritmo è un dettaglio interno
+  method: "knn"
+  k: 7
+  weights: "distance"
+  metric: "euclidean"
+EOF
+
+4.6 config/secrets.yaml (placeholder, non versionare)
+cat > config/secrets.yaml << 'EOF'
+oidc:
+  client_secret: "CHANGE_ME"
+  username: "CHANGE_ME"
+  password: "CHANGE_ME"
+EOF
+
+###########################################################################################
+Passo 5 Metti un main minimale per verificare che il container parte
+app/main.py
+cat > app/main.py << 'EOF'
+from fastapi import FastAPI
+from .settings import load_settings
+
+api = FastAPI()
+
+@api.get("/health")
+def health():
+    return {"status": "ok"}
+
+def main():
+    import sys
+    settings = load_settings()
+    print("Settings loaded. Keys:", list(settings.keys()))
+
+    if len(sys.argv) < 2:
+        raise SystemExit("Usage: python -m app.main [train|infer]")
+
+    cmd = sys.argv[1].lower()
+    if cmd == "train":
+        print("TRAIN mode (placeholder)")
+    elif cmd == "infer":
+        print("INFER mode (placeholder)")
+    else:
+        raise SystemExit("Unknown command")
+
+if __name__ == "__main__":
+    main()
+EOF
+
+app/settings.py
+cat > app/settings.py << 'EOF'
+import os
+from pathlib import Path
+import yaml
+
+def _read_yaml(path: str) -> dict:
+    with open(path, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f) or {}
+
+def deep_merge(a: dict, b: dict) -> dict:
+    out = dict(a or {})
+    for k, v in (b or {}).items():
+        if isinstance(v, dict) and isinstance(out.get(k), dict):
+            out[k] = deep_merge(out[k], v)
+        else:
+            out[k] = v
+    return out
+
+def load_settings() -> dict:
+    cfg_path = os.getenv("CONFIG_FILE", "/app/config/config.yaml")
+    settings = _read_yaml(cfg_path)
+
+    secrets_path = os.getenv("SECRETS_FILE", "")
+    if secrets_path and Path(secrets_path).exists():
+        secrets = _read_yaml(secrets_path)
+        settings = deep_merge(settings, secrets)
+
+    # fallback paths (coerenti con compose)
+    settings.setdefault("paths", {})
+    settings["paths"].setdefault("dataset", os.getenv("DATASET_PATH", "/data/fingerprint.parquet"))
+    settings["paths"].setdefault("model", os.getenv("MODEL_PATH", "/models/model.joblib"))
+
+    return settings
+EOF
+
+(Gli altri file per ora possono restare vuoti.)
+
+###########################################################################################
+Passo 6 Build & test smoke test
+	docker compose build
+
+
+###########################################################################################
+###  END prima creazione container                                        #################
+###########################################################################################
+
+
+
+
+###########################################################################################
+1) Come avviare SOLO ble-ai-localizer (senza impattare gli altri)
+
+	Vai nella directory del progetto:
+		cd /data/service/ble-ai-localizer
+
+	Avvio in background:
+		docker compose -p ble-ai-localizer up -d
+			Perché -p ble-ai-localizer?
+			Imposta esplicitamente il project name, così sei sicuro al 100% di nonagganciare per errore un altro compose.
+
+	Verifica stato (solo di questo progetto):
+		docker compose -p ble-ai-localizer ps
+
+	Log (solo di questo progetto):
+		docker compose -p ble-ai-localizer logs -f
+
+
+2) Come stoppare/riavviare SOLO ble-ai-localizer
+
+	Stop (non rimuove container):
+		docker compose -p ble-ai-localizer stop
+
+	Restart:
+		docker compose -p ble-ai-localizer restart
+
+	Stop + rimozione container/network del progetto (NON tocca volumi bind ./data, ./models):
+		docker compose -p ble-ai-localizer down
+
+3) Aggiornare solo il tuo container (codice o Dockerfile cambiato)
+
+	Ricostruisci l'immagine:
+		docker compose -p ble-ai-localizer build
+
+	Riavvia applicando l'immagine nuova:
+		docker compose -p ble-ai-localizer up -d
+
+	Se vuoi forzare rebuild+restart in un colpo:
+		docker compose -p ble-ai-localizer up -d --build
+
+
+4) Esportare l'immagine (backup o deploy su altro server)
+	Esempio export in tar (meglio gzippato):
+		docker save ble-ai-localizer:0.1.0 | gzip > ble-ai-localizer_0.1.0.tar.gz
+	Su altro server:
+		gzip -dc ble-ai-localizer_0.1.0.tar.gz | docker load
+
+#Debug
+	mosquitto_sub -v -h 192.168.1.101 -p 1883 -t '#' -V mqttv311  | grep publish_out
+
+	docker compose -p ble-ai-localizer exec -T ble-ai-localizer ls -l /data/config/
+
+	#Caso di gateway che non rileva nessun beacon
+	publish_out/ac233fc1dcd3 [{"timestamp":"2026-01-30T13:40:08.885Z","type":"Gateway","mac":"AC233FC1DCD3","nums":0}]
+
+
+#Verifica del modello in uso:
+#Ver 1
+docker compose -p ble-ai-localizer exec -T ble-ai-localizer python - <<'PY'
+import hashlib, joblib, os
+p="/data/model/model.joblib"
+b=open(p,"rb").read()
+print(f"FILE: {p}")
+print(f"sha256={hashlib.sha256(b).hexdigest()[:12]} size={len(b)} bytes")
+m=joblib.load(p)
+print("TYPE:", type(m))
+for k in ["version","nan_fill","k_floor","k_xy","weights","metric","floors"]:
+    print(f"{k}:", getattr(m,k,None))
+gws=getattr(m,"feature_gateways",[])
+print("gateways:", len(gws), "first:", gws[:5])
+regs=getattr(m,"xy_regs",{})
+print("xy_regs floors:", sorted(list(regs.keys())))
+PY
+service "ble-ai-localizer" is not running
+
+#Ver 2
+docker compose -p ble-ai-localizer exec -T ble-ai-localizer python - <<'PY'
+import joblib, pprint
+m = joblib.load("/data/model/model.joblib")
+keys = [
+  "created_at_utc","sklearn_version","numpy_version",
+  "gateways_order","nan_fill","k_floor","k_xy","weights","metric","floors"
+]
+pprint.pprint({k: m.get(k) for k in keys})
+PY
+
+#Esempio utilizzo server API:
+Ottenimento del token:
+TOKEN=$(
+  curl -k -s -X POST "https://10.251.0.30:10002/realms/API.Server.local/protocol/openid-connect/token" \
+    -H "Content-Type: application/x-www-form-urlencoded" \
+    -d "grant_type=password" \
+    -d "client_id=Fastapi" \
+    -d "client_secret=wojuoB7Z5xhlPFrF2lIxJSSdVHCApEgC" \
+    -d "username=core" \
+    -d "password=C0r3_us3r_Cr3d3nt14ls" \
+    -d "audience=Fastapi" \
+  | jq -r '.access_token'
+)
+
+Utilizzare il token in un API:
+
+curl -k -X 'GET' \
+  'https://10.251.0.30:5050/reslevis/getTrackers' \
+  -H 'accept: application/json' \
+  -H 'Authorization: Bearer $TOKEN'
+
+
+
+
+#Aggiornamento del software
+	cd /data/service/ble-ai-localizer
+	docker compose up -d --build
+	docker compose -p ble-ai-localizer build
+	docker compose -p ble-ai-localizer up -d --build
+	docker system prune
+    docker rmi ble-ai-localizer:0.1.0
+
+Gestione Sart/Stop Container
+	cd /data/service/ble-ai-localizer
+	docker compose -p ble-ai-localizer up -d
+	docker compose -p ble-ai-localizer logs -f --tail=200 --timestamps
+	docker compose -p ble-ai-localizer stop
+	docker compose -p ble-ai-localizer restart
+	docker compose -p ble-ai-localizer down
+
+
+Accesso Web MajorNET ResLevis:
+	https://10.251.0.30/frontend/app_reslevis/app.html#home
+
+Accesso Web a Container ble-ai-localizer
+	URL: http://0.0.0.0:8501
+	http://192.168.1.3:8501/
+		username e password da file composer: docker-compose.yml
+      			UI_USER: "Admin"
+      			UI_PASSWORD: "pwdadmin1"  <-- facilitate per accesso
+			da mobile
+
+
+
+
+NOTE:
+- Il valore di k utilizzato rappresenta la retta (k=2) o il piano (k>2) tra i
+  beacon di trainig, in addestramento deve coincidere come minimo con il
+numero di beacon per stanza, ma è un parametro globale per cui va fatto
+rispettare per tutte le stanze,  per cui ad esempio se si decide di catturare 5
+misure per stanza k conviene metterlo a 3
+
+- almeno a 1m dalle pareti della stanza (evitare attaccato al muro su pareti
+  adiacenti)
+
+- per piani simemtrici le misure di ogni piano conviene farle conincidere
+
+- aggingere timestamp in fingerprint e registarre traffico mqtt raw (a
+  parita' di finestra di registrazione potranno essere rivalutati i valori
+  letti)
+
+- Tempo di tx nel beacon 200 ms
+- potenze 0, -4 ,-8, -12
+- slot di raccolta 30s
+
+- time 1400
+- Inferenza a 7 sec
+
+- durante la raccolta occorre almeno un beacon di test per potenza che veiene escluso
+  nell'addestarmento ma usato per l'inferenza con il traffico registrato nella
+  fase di raccolta.
+
+- testare prima gw e mgtt se regge 200 ms
+
+
+
+
+
+
+ORGANIZZAZIONE CAMPAGNA:
+
+
+N.4 piedistalli con a bordo n.4 becon configurati ognuno:
+ - Tempo di tx nel beacon 200 ms
+ - potenze 0, -4 ,-8, -12
+
+
+Organizzazione dei piedistalli:
+GBC-01: BC-00-21, BC-04-22, BC-08-23, BC-12-24
+
+GBC-02: BC-00-25, BC-04-26, BC-08-27, BC-12-28
+
+GBC-03: BC-00-36, BC-04-37, BC-08-38, BC-12-39
+
+GBC-14: BC-00-29, BC-04-30, BC-08-41, BC-12-42 
+
+
+
+
+
+
+

app/map_manager.py

@@ -32,22 +32,21 @@ def force_save_json(path, data):
         return False
 
 def show_mapper(cfg):
+    # Configurazione percorsi
     MAPS_DIR = Path(cfg['maps']['map_dir'])
-    
-    # Riferimenti directory per Addestramento e Test
     TRAIN_BASE = Path("/data/train")
+    JOBS_BASE = Path(cfg['collect_train']['jobs_dir'])
+
+    # Riferimenti directory per Addestramento e Test
     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"
+    BEACONS_FILE = cfg.get('paths', {}).get('beacons_csv', "/data/config/beacons.csv")
     GROUPS_FILE = "/data/config/beacongroup.csv"
     CSV_DELIM = cfg.get('paths', {}).get('csv_delimiter', ';')
 
@@ -58,7 +57,7 @@ def show_mapper(cfg):
     # --- 1. GESTIONE UPLOAD ---
     maps = sorted([f.replace(cfg['maps']['floor_prefix'], "").split('.')[0] 
                    for f in os.listdir(MAPS_DIR) if f.startswith(cfg['maps']['floor_prefix'])])
-    
+
     with st.expander("📂 Carica Nuova Planimetria", expanded=not maps):
         c_up1, c_up2 = st.columns([1, 2])
         new_f_id = c_up1.text_input("ID Piano (es. 0, -1):", key="new_f_id_v21")
@@ -115,7 +114,7 @@ def show_mapper(cfg):
     tool = st.session_state.get('map_tool', 'Rileva')
 
     # --- 5. MAPPA ---
-    col_map, col_ui = st.columns([3, 1])
+    col_map, col_ui = st.columns([3, 1.2])
     with col_map:
         img_p = next((MAPS_DIR / f"{cfg['maps']['floor_prefix']}{floor_id}{e}" for e in ['.png','.jpg','.jpeg'] if (MAPS_DIR / f"{cfg['maps']['floor_prefix']}{floor_id}{e}").exists()))
         img_data, w, h = get_image_base64(img_p)
@@ -137,41 +136,30 @@ 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 (OTTIMIZZATO TRAMITE NOME FILE) ---
+        # --- DISEGNO PUNTI STORICI ---
         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
-                    
+                    fz = parts[-3]
+                    fx = float(parts[-2].replace(',', '.'))
+                    fy = float(parts[-1].replace(',', '.'))
                     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)
+                        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"):
@@ -185,9 +173,9 @@ def show_mapper(cfg):
                         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, 
+                            location=[px_y, px_x], number_of_sides=4,
                             rotation=45 if shape=="diamond" else 0,
-                            radius=m_size, color=color, weight=2, 
+                            radius=m_size, color=color, weight=2,
                             dash_array=dash, fill=True
                         ).add_to(m)
                 except: continue
@@ -222,7 +210,7 @@ def show_mapper(cfg):
     # --- 6. LOGICA UI ---
     with col_ui:
         st.write(f"### Tool: **{tool}**")
-        
+
         if tool == "Calibra":
             pts = st.session_state.get("cal_points", [])
             if len(pts) == 2:
@@ -244,49 +232,75 @@ def show_mapper(cfg):
                 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)
+                
+                # --- OTTIMIZZAZIONE SPAZIO: X e Y SU STESSA RIGA ---
+                c_c1, c_c2 = st.columns(2)
+                c_c1.metric("X (cm)", sx)
+                c_c2.metric("Y (cm)", sy)
+
+                # Divisore compresso con margine ridotto
+                st.markdown("<hr style='margin: 0.5em 0;'>", unsafe_allow_html=True)
 
-                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'])]
+                # --- STILE CSS PER OTTIMIZZAZIONE TABLET (Tapping) ---
+                st.markdown("""
+                <style>
+                [data-testid="stRadio"] > div {
+                    gap: 1.0rem;
+                }
+                [data-testid="stRadio"] label {
+                    font-size: 19px !important;
+                    padding: 5px 0px;
+                }
+                [data-testid="stRadio"] div[role="radiogroup"] > div {
+                    background-color: #f0f2f6;
+                    padding: 4px 12px;
+                    border-radius: 8px;
+                    margin-bottom: 3px;
+                }
+                /* Riduzione whitespace globale colonna UI */
+                .element-container { margin-bottom: 0.3rem !important; }
+                </style>
+                """, unsafe_allow_html=True)
+
                 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']]
+                    list_groups = [f"Gruppo: {n}" for n in g_df['BeaconGroupName']]
+                else:
+                    list_groups = []
 
-                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)
+                if not list_groups:
+                    st.warning("⚠️ Configura i Gruppi.")
+                else:
+                    # Selezione target via radio per evitare tastiera
+                    sel_target = st.radio("🎯 Selezione Target (Solo Gruppi):", list_groups, key="sel_target_v24_touch")
+
+                    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: "):
+                        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()}
+
+                        job_rows = []
                         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()
+
+                        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"))

app/web_beacon.py

@@ -1,8 +1,8 @@
 import streamlit as st
 import pandas as pd
 import os
+import time  # RISOLVE: NameError: name 'time' is not defined
 from pathlib import Path
-# CORREZIONE: rimosso il punto per evitare ImportError
 from normalize import norm_mac
 
 def show_beacon_manager(cfg):
@@ -13,7 +13,10 @@ def show_beacon_manager(cfg):
     group_csv = "/data/config/beacongroup.csv"
     csv_delim = paths.get("csv_delimiter", ";")
 
-    # --- CARICAMENTO DATI ---
+    # Inizializzazione session_state per il reset dei campi dopo il salva
+    if "reset_trigger" not in st.session_state:
+        st.session_state.reset_trigger = False
+
     def load_df(path, cols):
         if os.path.exists(path):
             try:
@@ -27,84 +30,90 @@ def show_beacon_manager(cfg):
     df_beacons = load_df(beacon_csv, ["BeaconName", "MAC"])
     df_groups = load_df(group_csv, ["BeaconGroupName", "GroupMAC"])
 
-    # --- UI: TABS INTERNI ---
     sub_tab1, sub_tab2 = st.tabs(["Individuali", "Gruppi"])
 
     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, 
-            key="editor_beacons",
-            hide_index=True
+            df_beacons, num_rows="dynamic", use_container_width=True, 
+            key="editor_beacons", hide_index=True
         )
-        
         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.success("Anagrafica salvata!")
                 st.rerun()
             except Exception as e:
-                st.error(f"Errore durante il salvataggio: {e}")
+                st.error(f"Errore: {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)
+            st.write("#### Gruppi Attivi (Vista Operatore)")
+            mac_to_name = dict(zip(df_beacons['MAC'], df_beacons['BeaconName']))
+            
+            display_data = []
+            for _, row in df_groups.iterrows():
+                group_name = row['BeaconGroupName']
+                macs = str(row['GroupMAC']).split(',')
+                for m in macs:
+                    m = m.strip()
+                    display_data.append({
+                        "Gruppo": group_name, 
+                        "BeaconName": mac_to_name.get(m, "Sconosciuto"),
+                        "MAC": m
+                    })
+            
+            # --- LOGICA PER UNIRE LE CELLE (Excel Style) ---
+            # Trasformiamo Gruppo e BeaconName in Indici per raggrupparli visivamente
+            df_display = pd.DataFrame(display_data)
+            df_grouped = df_display.set_index(["Gruppo", "BeaconName"])
+            st.dataframe(df_grouped, use_container_width=True)
+
+            # RIMOZIONE SELETTIVA GRUPPO
+            st.divider()
+            col_del1, col_del2 = st.columns([3, 1])
+            group_to_del = col_del1.selectbox("Seleziona gruppo da rimuovere:", df_groups["BeaconGroupName"].unique())
+            if col_del2.button("🗑️ Rimuovi Gruppo", use_container_width=True):
+                df_groups = df_groups[df_groups["BeaconGroupName"] != group_to_del]
+                df_groups.to_csv(group_csv, index=False, sep=csv_delim)
+                st.warning(f"Gruppo '{group_to_del}' rimosso.")
+                st.rerun()
         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)]
+        # Form per l'inserimento con reset automatico
+        with st.form("form_nuovo_gruppo", clear_on_submit=True):
+            gn = st.text_input("Nome Gruppo (es. GBC-01)")
             
-            selected_options = st.multiselect("Seleziona i Beacon da includere:", options)
+            options = [f"{n} ({m})" for n, m in zip(df_beacons["BeaconName"], df_beacons["MAC"])]
+            selected_options = st.multiselect("Seleziona i Beacon:", options)
             
-            submit = st.form_submit_button("💾 Salva Gruppo")
+            submit = st.form_submit_button("💾 Salva Gruppo", type="primary")
             
             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
+                if gn and selected_options:
                     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.success(f"Gruppo '{gn}' salvato!")
+                    time.sleep(1) 
                     st.rerun()
+                else:
+                    st.error("Dati incompleti: inserire Nome e selezionare i Beacon.")
 
     return df_beacons, df_groups

config/config.yaml_v1

@@ -1,113 +0,0 @@
-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

@@ -1,68 +0,0 @@
-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

@@ -1,114 +0,0 @@
-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/config/beacongroup.csv

@@ -1,2 +1,5 @@
 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
+GBC-01;C3:00:00:57:B9:E6,C3:00:00:57:B9:D4,C3:00:00:57:B9:E8,C3:00:00:57:B9:F1
+GBC-02;C3:00:00:57:B9:E7,C3:00:00:57:B9:D6,C3:00:00:57:B9:D7,C3:00:00:57:B9:F6
+GBC-03;C3:00:00:57:B9:DC,C3:00:00:57:B9:DD,C3:00:00:57:B9:E3,C3:00:00:57:B9:DF
+GBC-04;C3:00:00:57:B9:F2,C3:00:00:57:B9:D3,C3:00:00:39:47:DF,C3:00:00:39:47:E2

data/config/beacons.csv

@@ -4,4 +4,14 @@ 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
+BC-04-26;C3:00:00:57:B9:D6
+BC-08-27;C3:00:00:57:B9:D7
+BC-12-28;C3:00:00:57:B9:F6
+BC-00-36;C3:00:00:57:B9:DC
+BC-04-37;C3:00:00:57:B9:DD
+BC-08-38;C3:00:00:57:B9:E3
+BC-12-39;C3:00:00:57:B9:DF
+BC-00-29;C3:00:00:57:B9:F2
+BC-04-30;C3:00:00:57:B9:D3
+BC-08-41;C3:00:00:39:47:DF
+BC-12-42;C3:00:00:39:47:E2

data/maps/meta_-1.json

@@ -1,9 +0,0 @@
-{
-    "pixel_ratio": 1.5920551746472242,
-    "calibrated": true,
-    "origin": [
-        1297,
-        3587
-    ],
-    "grid_size": 50
-}

data/maps/meta_0.json

@@ -1,9 +0,0 @@
-{
-    "pixel_ratio": 1.584773230620443,
-    "calibrated": true,
-    "origin": [
-        1247,
-        3791
-    ],
-    "grid_size": 50
-}

data/maps/meta_1.json

@@ -1,9 +0,0 @@
-{
-    "pixel_ratio": 1.5924129560451799,
-    "calibrated": true,
-    "origin": [
-        1297,
-        3587
-    ],
-    "grid_size": 50
-}

data/model/model.joblib

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

data/mqtt_raw/mqtt_raw_20260209_023636.log

@@ -1,313 +0,0 @@
-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"}]
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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/samples/00_-1_1417_1081.csv

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data/train/samples/00_-1_152_1379.csv

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data/train/samples/00_-1_1687_221.csv

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data/train/samples/00_-1_2547_235.csv

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data/train/samples/00_-1_2642_1057.csv

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data/train/samples/00_0_1105_707.csv

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data/train/samples/00_0_2026_707.csv

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data/train/samples/00_0_2897_72.csv

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data/train/samples/00_0_2925_1485.csv

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data/train/samples/00_0_70_1485.csv

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data/train/samples/00_0_98_58.csv

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data/train/samples/00_1_105_67.csv

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data/train/samples/00_1_130_1414.csv

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data/train/samples/00_1_1502_611.csv

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data/train/samples/00_1_2766_157.csv

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data/train/samples/00_1_2837_1393.csv

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data/train/samples/04_-1_1291_655.csv

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data/train/samples/04_-1_1417_1081.csv

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data/train/samples/04_-1_152_1379.csv

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data/train/samples/04_-1_1687_221.csv

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data/train/samples/04_-1_2122_693.csv

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data/train/samples/04_-1_2547_235.csv

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data/train/samples/04_-1_2642_1057.csv

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data/train/samples/04_0_1105_707.csv

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data/train/samples/04_0_2026_707.csv

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data/train/samples/04_0_2897_72.csv

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data/train/samples/04_0_2925_1485.csv

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data/train/samples/04_0_70_1485.csv

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data/train/samples/04_0_98_58.csv

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data/train/samples/04_1_105_67.csv

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data/train/samples/04_1_130_1414.csv

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data/train/samples/04_1_1502_611.csv

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data/train/samples/04_1_2766_157.csv

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data/train/samples/04_1_2837_1393.csv

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data/train/samples/08_-1_1291_655.csv

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data/train/samples/08_-1_1417_1081.csv

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data/train/samples/08_-1_152_1379.csv

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data/train/samples/08_-1_1687_221.csv

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data/train/samples/08_-1_2547_235.csv

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data/train/samples/08_-1_2642_1057.csv

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data/train/samples/08_0_1105_707.csv

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data/train/samples/08_0_2026_707.csv

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data/train/samples/08_0_2897_72.csv

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data/train/samples/08_0_2925_1485.csv

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data/train/samples/08_0_70_1485.csv

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data/train/samples/08_0_98_58.csv

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data/train/samples/08_1_105_67.csv

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data/train/samples/08_1_130_1414.csv

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data/train/samples/08_1_1502_611.csv

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data/train/samples/08_1_2766_157.csv

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data/train/samples/08_1_2837_1393.csv

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data/train/samples/12_-1_1291_655.csv

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data/train/samples/12_-1_1417_1081.csv

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data/train/samples/12_-1_152_1379.csv

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data/train/samples/12_-1_1687_221.csv

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data/train/samples/12_-1_2122_693.csv

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data/train/samples/12_-1_2547_235.csv

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