Integrazione mappa per supporto dispositivi mobili

Dockerfile

@@ -1,33 +1,28 @@
 FROM python:3.10-slim
 ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
 
-# dipendenze native utili a numpy/scikit (safe choice)
+# Installiamo solo libgomp1 (necessaria per scikit-learn)
 RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential gcc g++ \
+    libgomp1 \
     && rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app
 
-COPY requirements.txt /app/requirements.txt
-RUN pip install --no-cache-dir -r /app/requirements.txt
-
-# Correzione nome pacchetto e upgrade pip
-RUN pip install --no-cache-dir --upgrade pip 
-RUN pip install streamlit==1.29.0 streamlit-drawable-canvas==0.9.3 PyYAML Pillow pandas
-RUN pip install --no-cache-dir folium streamlit-folium
-RUN pip install --no-cache-dir psutil 
+# Copia e installazione in un unico step per ridurre i layer
+COPY requirements.txt .
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
 
+# Copia del resto dell'applicazione
 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
+# Setup utente e permessi
+RUN useradd -m appuser && \
+    mkdir -p /home/appuser/data /home/appuser/models && \
+    chown -R appuser:appuser /home/appuser /app
 
+USER appuser
 ENTRYPOINT ["/app/entrypoint.sh"]

README.md

@@ -411,7 +411,7 @@ curl -k -X 'GET' \
 	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

app/leaflet_bridge.js

@@ -1,58 +0,0 @@
-// leaflet_bridge.js
-function initMap(config) {
-    const { imgUrl, width, height, meta, grid_size, dots } = config;
-
-    // Setup Coordinate XY (0,0 in alto a sinistra)
-    const map = L.map('map', {
-        crs: L.CRS.Simple,
-        minZoom: -2,
-        maxZoom: 4,
-        attributionControl: false
-    });
-
-    const bounds = [[-height, 0], [0, width]];
-    L.imageOverlay(imgUrl, bounds).addTo(map);
-    map.fitBounds(bounds);
-
-    // Gestione Griglia
-    let gridLayer = L.layerGroup();
-    if (meta.show_grid) {
-        const step = meta.grid_size || 100;
-        const S = meta.pixel_ratio || 1;
-        for (let x = 0; x <= width; x += (step * S)) {
-            L.polyline([[0, x], [-height, x]], {color: '#ccc', weight: 1, opacity: 0.5}).addTo(gridLayer);
-        }
-        for (let y = 0; y >= -height; y -= (step * S)) {
-            L.polyline([[y, 0], [y, width]], {color: '#ccc', weight: 1, opacity: 0.5}).addTo(gridLayer);
-        }
-        gridLayer.addTo(map);
-    }
-
-    // Visualizzazione punti esistenti (Verdi e Blu)
-    dots.forEach(dot => {
-        const color = dot.status === 'completed' ? '#228B22' : '#0000FF';
-        L.circleMarker([dot.y, dot.x], {
-            radius: config.dot_size / 5,
-            fillColor: color,
-            color: 'white',
-            weight: 2,
-            fillOpacity: 0.8
-        }).addTo(map).bindPopup(`X: ${dot.relX}, Y: ${dot.relY}`);
-    });
-
-    // Evento Click per rilievo
-    map.on('click', function(e) {
-        const raw_x = e.latlng.lng;
-        const raw_y = e.latlng.lat; // In Leaflet Simple, Y è negativa sotto l'origine
-
-        // Invio dati a Streamlit
-        window.parent.postMessage({
-            type: 'streamlit:setComponentValue',
-            value: {
-                x: raw_x,
-                y: raw_y,
-                timestamp: new Date().getTime()
-            }
-        }, '*');
-    });
-}

app/map_manager.py

@@ -1,49 +1,217 @@
+import os
+import json
+import streamlit as st
+import pandas as pd
+from PIL import Image
+from pathlib import Path
+import time
+import folium
+from streamlit_folium import st_folium
+import math
 import base64
 from io import BytesIO
 
-def get_image_base64(img):
+# --- CACHE IMMAGINE ---
+@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()
-    return f"data:image/png;base64,{img_str}"
+    img_str = base64.b64encode(buffered.getvalue()).decode("ascii")
+    return f"data:image/png;base64,{img_str}", w, h
 
-def show_mapper_v2(cfg):
-    # ... (caricamento meta e percorsi come nel tuo file originale) ...
-    
-    img_path = MAPS_DIR / f"{cfg['maps']['floor_prefix']}{floor_id}.png"
-    img = Image.open(img_path).convert("RGBA")
-    img_width, img_height = img.size
-    img_b64 = get_image_base64(img)
+# --- FUNZIONE SALVATAGGIO FISICO ---
+def force_save_json(path, data):
+    try:
+        with open(path, "w", encoding='utf-8') as f:
+            json.dump(data, f, indent=4)
+        return True
+    except Exception as e:
+        st.error(f"Errore scrittura disco: {e}")
+        return False
 
-    # Prepariamo la lista dei punti esistenti (Punto 6 delle specifiche)
-    dots_data = []
-    # Qui cicla sui tuoi file CSV e popola dots_data con {x, y, relX, relY, status}
-    
-    # Integrazione del componente HTML
-    # Carichiamo il JS dal file esterno
-    with open("leaflet_bridge.js", "r") as f:
-        js_code = f.read()
-
-    html_content = f"""
-    <link rel="stylesheet" href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css" />
-    <script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"></script>
-    <div id="map" style="height: 600px; width: 100%;"></div>
-    <script>
-        {js_code}
-        initMap({{
-            imgUrl: "{img_b64}",
-            width: {img_width},
-            height: {img_height},
-            meta: {json.dumps(meta)},
-            dots: {json.dumps(dots_data)},
-            dot_size: {dot_size}
-        }});
-    </script>
-    """
-    
-    # Il componente restituisce il valore di window.parent.postMessage
-    result = components.html(html_content, height=650)
+def show_mapper(cfg):
+    MAPS_DIR = Path(cfg['maps']['map_dir'])
+    SAMPLES_DIR = Path("/data/train/samples")
+    JOBS_BASE = Path(cfg['collect_train']['jobs_dir'])
+    PENDING_DIR = JOBS_BASE / "pending"
+    ERROR_DIR = JOBS_BASE / "error"
+    BEACONS_FILE = "/data/config/beacons.csv"
+    [p.mkdir(parents=True, exist_ok=True) for p in [MAPS_DIR, SAMPLES_DIR, PENDING_DIR, ERROR_DIR]]
+
+    # --- 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'])])
     
-    if result:
-        st.write(f"Posizione catturata: {result}")
-        # Qui inserisci la tua logica di salvataggio CSV che avevi nel punto 7
+    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")
+        up_file = c_up2.file_uploader("Immagine (PNG/JPG):", type=['png', 'jpg', 'jpeg'], key="up_v21")
+        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())
+                
+                m_path = MAPS_DIR / f"{cfg['maps']['meta_prefix']}{new_f_id}.json"
+                force_save_json(m_path, {"pixel_ratio": 1.0, "calibrated": False, "origin": [0, 0], "grid_size": 50})
+                st.session_state.current_floor = new_f_id
+                st.rerun()
+
+    if not maps: return
+
+    # --- 2. RIGA STATO ---
+    c_p, c_s, c_o = st.columns([2.5, 2, 2])
+    with c_p:
+        s1, s2 = st.columns([1, 1.2])
+        s1.markdown("<p style='padding-top:35px; font-weight:bold;'>Piano Attivo:</p>", unsafe_allow_html=True)
+        def_idx = maps.index(st.session_state.current_floor) if st.session_state.get("current_floor") in maps else 0
+        floor_id = s2.selectbox("", maps, index=def_idx, key="f_v21", label_visibility="collapsed")
+        if st.session_state.get("prev_floor") != floor_id:
+            st.session_state.cal_points = []
+            st.session_state.temp_lat, st.session_state.temp_lng = None, None
+            st.session_state.prev_floor = floor_id
+
+    meta_path = MAPS_DIR / f"{cfg['maps']['meta_prefix']}{floor_id}.json"
+    if meta_path.exists():
+        with open(meta_path, "r") as f: meta = json.load(f)
+    else:
+        meta = {"pixel_ratio": 1.0, "calibrated": False, "origin": [0, 0]}
+
+    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) ---
+    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")
+    st.markdown("---")
+
+    # --- 4. TOOLSET ---
+    t1, t2, t3 = st.columns(3)
+    if t1.button("📏 CALIBRA", use_container_width=True): st.session_state.map_tool = "Calibra"; st.session_state.cal_points = []
+    if t2.button("🎯 SET ORIGINE", use_container_width=True): st.session_state.map_tool = "Origine"
+    if t3.button("📡 RILEVA", use_container_width=True): st.session_state.map_tool = "Rileva"
+    tool = st.session_state.get('map_tool', 'Rileva')
+
+    # --- 5. MAPPA ---
+    col_map, col_ui = st.columns([3, 1])
+    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)
+        bounds = [[0, 0], [h, w]]
+        m = folium.Map(location=[h/2, w/2], crs="Simple", tiles=None, attribution_control=False)
+        m.fit_bounds(bounds)
+        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)
+
+        # GRIGLIA
+        if show_grid and meta["calibrated"] and meta["origin"] != [0, 0]:
+            px_step = grid_cm * meta["pixel_ratio"]
+            ox, oy = meta["origin"]
+            for x in sorted(list(range(int(ox), w, int(px_step))) + list(range(int(ox), 0, -int(px_step)))):
+                folium.PolyLine([[0, x], [h, x]], color="blue", weight=1, opacity=0.1).add_to(m)
+            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"):
+            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)
+                except: continue
+
+        draw_points(SAMPLES_DIR, "green", "circle")
+        draw_points(PENDING_DIR, "yellow", "diamond")
+        draw_points(ERROR_DIR, "red", "square")
+
+        # 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}")
+        click = out.get("last_clicked")
+        if click:
+            clat, clng = click["lat"], click["lng"]
+            if tool == "Calibra":
+                if len(st.session_state.get("cal_points", [])) < 2:
+                    st.session_state.cal_points.append([clat, clng]); st.rerun()
+            else:
+                if tool == "Rileva" and snap_on and meta["calibrated"]:
+                    px_step = grid_cm * meta["pixel_ratio"]
+                    st.session_state.temp_lng = meta["origin"][0] + round((clng - meta["origin"][0]) / px_step) * px_step
+                    st.session_state.temp_lat = meta["origin"][1] + round((clat - meta["origin"][1]) / px_step) * px_step
+                else:
+                    st.session_state.temp_lat, st.session_state.temp_lng = clat, clng
+                st.rerun()
+
+    # --- 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", [])
+            if len(pts) == 2:
+                dist_cm = st.number_input("Distanza reale (cm):", value=100.0)
+                if st.button("📏 SALVA SCALA", use_container_width=True, type="primary"):
+                    px_d = math.sqrt((pts[1][1]-pts[0][1])**2 + (pts[1][0]-pts[0][0])**2)
+                    meta.update({"pixel_ratio": px_d / dist_cm, "calibrated": True})
+                    if force_save_json(meta_path, meta): st.session_state.cal_points = []; st.rerun()
+        
+        elif st.session_state.get("temp_lat") is not None:
+            px_x, px_y = st.session_state.temp_lng, st.session_state.temp_lat
+            if tool == "Origine":
+                st.metric("X (px)", int(px_x)); st.metric("Y (px)", int(px_y))
+                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()

app/map_manager.py_v1

@@ -1,145 +0,0 @@
-import os
-import json
-import streamlit as st
-import pandas as pd
-from PIL import Image, ImageDraw
-from pathlib import Path
-from streamlit_drawable_canvas import st_canvas
-import time
-
-def show_mapper(cfg):
-    # --- 1. CONFIGURAZIONE PERCORSI ---
-    MAPS_DIR = Path(cfg['maps']['map_dir'])
-    SAMPLES_DIR = Path(cfg['train']['samples_dir'])
-    PENDING_DIR = Path(cfg['collect_train']['jobs_dir']) / "pending"
-    BEACONS_FILE = "/data/config/beacons.csv"
-
-    [p.mkdir(parents=True, exist_ok=True) for p in [MAPS_DIR, SAMPLES_DIR, PENDING_DIR]]
-
-    def load_map_metadata(f_id):
-        meta_path = MAPS_DIR / f"{cfg['maps']['meta_prefix']}{f_id}.json"
-        defaults = {"pixel_ratio": 1.0, "calibrated": False, "origin": [0, 0], "grid_size": 50}
-        if meta_path.exists():
-            with open(meta_path, "r") as f: return {**defaults, **json.load(f)}
-        return defaults
-
-    st.subheader("🗺️ Gestione Mappatura e Rilevamento")
-
-    # Selezione Piano
-    maps = sorted([f.replace(cfg['maps']['floor_prefix'], "").split('.')[0] for f in os.listdir(MAPS_DIR) if f.startswith(cfg['maps']['floor_prefix'])])
-    if not maps: st.error("Nessuna mappa trovata."); return
-    floor_id = st.selectbox("Seleziona Piano (Z):", maps)
-    meta = load_map_metadata(floor_id)
-
-    # --- 2. STATO SISTEMA ---
-    c_s1, c_s2 = st.columns(2)
-    with c_s1:
-        st.info(f"📏 Scala: {'✅' if meta['calibrated'] else '❌'} ({meta['pixel_ratio']:.4f} px/cm)")
-    with c_s2:
-        st.info(f"🎯 Origine: {'✅' if meta['origin'] != [0,0] else '❌'} (X:{meta['origin'][0]}, Y:{meta['origin'][1]})")
-
-    # --- 3. CONTROLLI VISUALIZZAZIONE ---
-    with st.expander("🎨 Opzioni Visualizzazione", expanded=True):
-        c_v1, c_v2, c_v3 = st.columns(3)
-        zoom = c_v1.slider("🔍 Zoom Mappa", 0.1, 4.0, 1.0, 0.1)
-        dot_size = c_v2.slider("🔵/🟢 Dimensione Punti", 10, 100, 40)
-        mode = c_v3.radio("Modalità Interazione:", ["🖐️ NAVIGA", "🎯 AZIONE"], horizontal=True)
-
-    # --- 4. SELEZIONE STRUMENTO ---
-    t1, t2, t3 = st.columns(3)
-    if t1.button("📏 IMPOSTA SCALA", use_container_width=True): st.session_state.map_tool = "Calibra"
-    if t2.button("🎯 SET ORIGINE", use_container_width=True): st.session_state.map_tool = "Origine"
-    
-    is_ready = meta.get("calibrated", False) and meta["origin"] != [0, 0]
-    if t3.button("📡 RILEVA", use_container_width=True, disabled=not is_ready): st.session_state.map_tool = "Rileva"
-
-    tool = st.session_state.get('map_tool', 'Rileva')
-    st.write(f"Strumento attivo: **{tool}**")
-
-    # --- 5. PREPARAZIONE IMMAGINE E STORICO ---
-    img_path = MAPS_DIR / f"{cfg['maps']['floor_prefix']}{floor_id}.png"
-    if not img_path.exists(): img_path = MAPS_DIR / f"{cfg['maps']['floor_prefix']}{floor_id}.jpg"
-    img = Image.open(img_path).convert("RGBA")
-    draw = ImageDraw.Draw(img)
-
-    if is_ready:
-        samples_files = list(SAMPLES_DIR.glob(f"{floor_id}_*.csv"))
-        pending_files = list(PENDING_DIR.glob(f"{floor_id}_*.csv"))
-        
-        def draw_points(files, color):
-            for f in files:
-                try:
-                    p = f.stem.split("_")
-                    px = (int(p[1]) * meta["pixel_ratio"]) + meta["origin"][0]
-                    py = (int(p[2]) * meta["pixel_ratio"]) + meta["origin"][1]
-                    r = dot_size // 2
-                    draw.ellipse([px-r, py-r, px+r, py+r], fill=color, outline="white", width=2)
-                except: continue
-
-        draw_points(samples_files, "#228B22") # Verde: Completati
-        draw_points(pending_files, "#0000FF") # Blu: In corso
-
-    # --- 6. CANVAS ---
-    d_mode = "transform" if mode == "🖐️ NAVIGA" else ("line" if tool == "Calibra" else "point")
-    
-    canvas_result = st_canvas(
-        background_image=img,
-        height=int(img.size[1] * zoom),
-        width=int(img.size[0] * zoom),
-        drawing_mode=d_mode,
-        display_toolbar=True,
-        update_streamlit=True,
-        key=f"canvas_v10_{floor_id}_{zoom}_{mode}_{tool}",
-    )
-
-    # --- 7. LOGICA AZIONI E SALVATAGGIO CSV ---
-    if mode == "🎯 AZIONE" and canvas_result.json_data and canvas_result.json_data["objects"]:
-        last = canvas_result.json_data["objects"][-1]
-        raw_x, raw_y = last["left"] / zoom, last["top"] / zoom
-
-        if tool == "Calibra" and last["type"] == "line":
-            px_dist = ((last["x2"] - last["x1"])**2 + (last["y2"] - last["y1"])**2)**0.5 / zoom
-            dist_cm = st.number_input("Distanza reale (cm):", value=100.0)
-            if st.button("APPLICA SCALA"):
-                meta.update({"pixel_ratio": px_dist / dist_cm, "calibrated": True})
-                with open(MAPS_DIR / f"{cfg['maps']['meta_prefix']}{floor_id}.json", "w") as f:
-                    json.dump(meta, f)
-                st.rerun()
-
-        elif tool == "Origine":
-            if st.button(f"Fissa Origine qui: {int(raw_x)}, {int(raw_y)}"):
-                meta["origin"] = [int(raw_x), int(raw_y)]
-                with open(MAPS_DIR / f"{cfg['maps']['meta_prefix']}{floor_id}.json", "w") as f:
-                    json.dump(meta, f)
-                st.rerun()
-
-        elif tool == "Rileva" and is_ready:
-            dx = (raw_x - meta["origin"][0]) / meta["pixel_ratio"]
-            dy = (raw_y - meta["origin"][1]) / meta["pixel_ratio"]
-            grid = meta.get("grid_size", 50)
-            sx, sy = int(round(dx/grid)*grid), int(round(dy/grid)*grid)
-            
-            st.write(f"### 📍 Punto: {sx}cm, {sy}cm")
-            
-            if os.path.exists(BEACONS_FILE):
-                b_df = pd.read_csv(BEACONS_FILE, sep=";")
-                sel_b = st.selectbox("Seleziona Beacon:", b_df.apply(lambda x: f"{x['BeaconName']} | {x['MAC']}", axis=1))
-                if st.button("🚀 REGISTRA LETTURA", type="primary", use_container_width=True):
-                    b_name, b_mac = sel_b.split(" | ")
-                    fname = f"{floor_id}_{sx}_{sy}.csv"
-                    
-                    # --- FIX SINTASSI CSV ---
-                    # Formato: Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
-                    data_out = {
-                        "Position": f"P_{sx}_{sy}",
-                        "Floor": floor_id,
-                        "RoomName": "Area_Rilevazione",
-                        "X": sx,
-                        "Y": sy,
-                        "Z": 0,
-                        "BeaconName": b_name,
-                        "MAC": b_mac
-                    }
-                    pd.DataFrame([data_out]).to_csv(PENDING_DIR / fname, index=False, sep=";")
-                    st.toast(f"Punto Blu registrato!")
-                    time.sleep(1); st.rerun()

app/map_manager.py_v2

@@ -1,49 +0,0 @@
-import base64
-from io import BytesIO
-
-def get_image_base64(img):
-    buffered = BytesIO()
-    img.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode()
-    return f"data:image/png;base64,{img_str}"
-
-def show_mapper_v2(cfg):
-    # ... (caricamento meta e percorsi come nel tuo file originale) ...
-    
-    img_path = MAPS_DIR / f"{cfg['maps']['floor_prefix']}{floor_id}.png"
-    img = Image.open(img_path).convert("RGBA")
-    img_width, img_height = img.size
-    img_b64 = get_image_base64(img)
-
-    # Prepariamo la lista dei punti esistenti (Punto 6 delle specifiche)
-    dots_data = []
-    # Qui cicla sui tuoi file CSV e popola dots_data con {x, y, relX, relY, status}
-    
-    # Integrazione del componente HTML
-    # Carichiamo il JS dal file esterno
-    with open("leaflet_bridge.js", "r") as f:
-        js_code = f.read()
-
-    html_content = f"""
-    <link rel="stylesheet" href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css" />
-    <script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"></script>
-    <div id="map" style="height: 600px; width: 100%;"></div>
-    <script>
-        {js_code}
-        initMap({{
-            imgUrl: "{img_b64}",
-            width: {img_width},
-            height: {img_height},
-            meta: {json.dumps(meta)},
-            dots: {json.dumps(dots_data)},
-            dot_size: {dot_size}
-        }});
-    </script>
-    """
-    
-    # Il componente restituisce il valore di window.parent.postMessage
-    result = components.html(html_content, height=650)
-    
-    if result:
-        st.write(f"Posizione catturata: {result}")
-        # Qui inserisci la tua logica di salvataggio CSV che avevi nel punto 7

app/web_inference.py

@@ -1,100 +1,102 @@
 import streamlit as st
 import pandas as pd
+import os
 import json
-from PIL import Image, ImageDraw, ImageFont
+import folium
+from streamlit_folium import st_folium
 from pathlib import Path
-import time
-import os
+from PIL import Image
+import base64
+from io import BytesIO
+
+# --- 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 show_inference_page(cfg):
-    # Definizione percorsi
-    # Assumendo che il file sia in /app/app/web_inference.py, 
-    # cerchiamo il font nella stessa cartella 'app'
-    BASE_DIR = Path(__file__).parent
+    st.subheader("📡 Monitoraggio Beacon Real-Time")
+    
+    # --- CONFIGURAZIONE PERCORSI ---
     MAPS_DIR = Path(cfg['maps']['map_dir'])
-    INFER_FILE = Path(cfg['infer']['output_dir']) / cfg['infer']['out_file']
+    INFER_FILE = Path("/data/infer/infer.csv")
     
-    # Percorso del font locale
-    FONT_PATH = BASE_DIR / "DejaVuSans-Bold.ttf"
-
-    st.subheader("🤖 Monitoraggio Inferenza Live")
+    # --- 1. SELEZIONE E STATO (RIGA COMPATTA) ---
+    maps = sorted([f.replace(cfg['maps']['floor_prefix'], "").split('.')[0] 
+                   for f in os.listdir(MAPS_DIR) if f.startswith(cfg['maps']['floor_prefix'])])
+    
+    if not maps:
+        st.warning("Nessuna mappa configurata.")
+        return
 
-    # --- CONTROLLI NEL TAB ---
-    with st.expander("🎨 Opzioni e Controllo", expanded=True):
-        col_ctrl1, col_ctrl2, col_ctrl3, col_ctrl4 = st.columns(4)
-        with col_ctrl1:
-            dot_size = st.slider("Dimensione Marker", 10, 100, 45, key="inf_dot")
-        with col_ctrl2:
-            refresh_rate = st.select_slider("Refresh (s)", options=[2, 5, 10, 30], value=5, key="inf_ref")
-        with col_ctrl3:
-            show_labels = st.checkbox("Mostra MAC", value=True, key="inf_show_mac")
-        with col_ctrl4:
-            # Monitoraggio disattivato di default per evitare login fantasma
-            auto_refresh = st.toggle("🔄 Monitoraggio Attivo", value=False, key="inf_auto")
+    # Lettura dati per conteggio
+    df_infer = pd.DataFrame()
+    if INFER_FILE.exists():
+        df_infer = pd.read_csv(INFER_FILE, sep=";")
 
-    floor_id = st.number_input("Piano (Z)", value=0, min_value=0, step=1, key="inf_z")
+    # Layout riga 1
+    c_piano, c_count, c_size = st.columns([3, 2, 2])
     
+    with c_piano:
+        sub1, sub2 = st.columns([1, 1.2])
+        sub1.markdown("<p style='padding-top:35px; font-weight:bold; font-size:15px;'>Piano Visualizzato:</p>", unsafe_allow_html=True)
+        floor_id = sub2.selectbox("", maps, key="inf_floor_v24", label_visibility="collapsed")
+    
+    # Filtro dati per il piano scelto
+    df_active = df_infer[(df_infer['z'].astype(str) == str(floor_id)) & (df_infer['x'] != -1)] if not df_infer.empty else pd.DataFrame()
+
+    with c_count:
+        st.info(f"📡 Beacon Attivi: **{len(df_active)}**\n(Totali nel file: {len(df_infer)})")
+
+    with c_size:
+        # Slider per dimensione pallini (come nel mapper)
+        m_size = st.slider("Dimensione Beacon:", 5, 20, 8, key="inf_msize_v24")
+
     # Caricamento Metadati
     meta_path = MAPS_DIR / f"{cfg['maps']['meta_prefix']}{floor_id}.json"
-    meta = {"pixel_ratio": 1.0, "origin": [0, 0]}
-    if meta_path.exists():
-        with open(meta_path, "r") as f: 
-            meta.update(json.load(f))
+    if not meta_path.exists(): return
+    with open(meta_path, "r") as f: meta = json.load(f)
+
+    # --- 2. RENDERING MAPPA ---
+    st.markdown("---")
+    img_p = next((MAPS_DIR / f"{cfg['maps']['floor_prefix']}{floor_id}{e}" for e in ['.png','.jpg'] if (MAPS_DIR / f"{cfg['maps']['floor_prefix']}{floor_id}{e}").exists()))
+    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, attribution_control=False)
+    m.fit_bounds(bounds)
+    m.options.update({"minZoom": -6, "maxZoom": 6, "zoomSnap": 0.25, "maxBounds": bounds, "maxBoundsViscosity": 1.0})
+    folium.raster_layers.ImageOverlay(image=img_data, bounds=bounds).add_to(m)
 
-    try:
-        if INFER_FILE.exists():
-            df = pd.read_csv(INFER_FILE, sep=";")
-            df_active = df[(df['z'] == floor_id) & (df['x'] != -1) & (df['y'] != -1)]
-            
-            img_path = MAPS_DIR / f"{cfg['maps']['floor_prefix']}{floor_id}.png"
-            if img_path.exists():
-                img = Image.open(img_path).convert("RGBA")
-                draw = ImageDraw.Draw(img)
+    # Disegno Beacon
+    if meta["calibrated"] and meta["origin"] != [0,0]:
+        # Origine
+        folium.CircleMarker(location=[meta["origin"][1], meta["origin"][0]], radius=4, color="black", fill=True).add_to(m)
 
-                # --- GESTIONE FONT XL ---
-                # Dimensione proporzionale al pallino (1.5x)
-                dynamic_font_size = int(dot_size * 1.5) 
-                
-                font = None
-                if FONT_PATH.exists():
-                    try:
-                        # Tenta il caricamento del file TTF locale
-                        font = ImageFont.truetype(str(FONT_PATH), dynamic_font_size)
-                    except Exception as e:
-                        # Se il file è corrotto (unknown file format), mostra errore e usa fallback
-                        st.error(f"Errore caricamento font: {e}. Controlla il file DejaVuSans-Bold.ttf")
-                
-                if font is None:
-                    font = ImageFont.load_default()
+        for _, row in df_active.iterrows():
+            px_x = (row['x'] * meta["pixel_ratio"]) + meta["origin"][0]
+            px_y = meta["origin"][1] - (row['y'] * meta["pixel_ratio"])
+            mac_label = str(row['mac'])[-5:]
 
-                for _, row in df_active.iterrows():
-                    px_x = (row['x'] * meta["pixel_ratio"]) + meta["origin"][0]
-                    px_y = (row['y'] * meta["pixel_ratio"]) + meta["origin"][1]
+            folium.CircleMarker(
+                location=[px_y, px_x], radius=m_size, color="blue", 
+                fill=True, fill_color="cyan", fill_opacity=0.8
+            ).add_to(m)
 
-                    r = dot_size
-                    # Disegno Marker
-                    draw.ellipse([px_x-(r+5), px_y-(r+5), px_x+(r+5), px_y+(r+5)], fill="black")
-                    draw.ellipse([px_x-r, px_y-r, px_x+r, px_y+r], fill="#00E5FF")
-                    
-                    if show_labels:
-                        label = f"{str(row['mac'])[-5:]}"
-                        # Posizionamento dinamico a destra del pallino
-                        text_x = px_x + r + 20
-                        text_y = px_y - (dynamic_font_size / 2)
-                        
-                        # Outline per leggibilità (bordo nero spesso 3px)
-                        for dx, dy in [(-3,-3), (3,-3), (-3,3), (3,3), (0,-3), (0,3), (-3,0), (2,0)]:
-                            draw.text((text_x + dx, text_y + dy), label, font=font, fill="black")
-                        
-                        # Testo Giallo
-                        draw.text((text_x, text_y), label, font=font, fill="#FFFF00")
+            folium.Marker(
+                location=[px_y, px_x],
+                icon=folium.DivIcon(html=f"""<div style="font-family: sans-serif; color: black; font-weight: bold; font-size: {int(m_size*1.2)}pt; width: 80px; transform: translate({m_size+2}px, -{m_size+2}px);">{mac_label}</div>""")
+            ).add_to(m)
 
-                st.image(img, use_column_width=True)
-                st.metric("Dispositivi Online", len(df_active))
-    except Exception as e:
-        st.error(f"Errore generale: {e}")
+    st_folium(m, height=700, width=None, key=f"inf_map_v24_{floor_id}", use_container_width=True)
 
-    # --- REFRESH CONDIZIONALE ---
-    if auto_refresh:
-        time.sleep(refresh_rate)
-        st.rerun()
+    # --- 3. TABELLA RIEPILOGO COMPLETA ---
+    if not df_infer.empty:
+        st.subheader("Dettaglio Dispositivi (Tutti i Piani)")
+        # Aggiunta colonna Z per chiarezza
+        st.dataframe(df_infer[['mac', 'z', 'x', 'y']], use_container_width=True)

app/web_suite.py

@@ -27,7 +27,7 @@ st.set_page_config(
     menu_items={
         'Get Help': None,
         'Report a bug': None,
-        'About': "# ▒~_~[▒▒~O BLE AI Localizer - Suite\nSistema professionale di posizionamento BLE."
+        'About': "🛰️BLE AI Localizer - Suite\nSistema professionale di posizionamento BLE."
     }
 )
 
@@ -44,6 +44,9 @@ st.markdown("""
     </style>
     """, unsafe_allow_html=True)
 
+if "main_login_user" not in st.session_state:
+    st.session_state["main_login_user"] = ""
+
 # --- LOGIN (Tua versione originale) ---
 if "password_correct" not in st.session_state:
     st.session_state["password_correct"] = False

config/config.yaml

@@ -63,18 +63,18 @@ infer:
   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_
-  default_grid_size: 100       # Dimensione griglia predefinita in cm
-  default_dot_size: 20         # Dimensione predefinita dei marker
-  show_grid_default: true      # Stato iniziale della griglia
+  show_grid_default: true
 ml:
   k: 5
   method: knn
   metric: euclidean
   weights: distance
-mode: infer
+mode: collect_train
 mqtt:
   ca_file: ''
   client_id: ble-ai-localizer

data/infer/infer.csv

@@ -1,13 +1,13 @@
 mac;z;x;y
-C8:3F:8F:17:DB:35;1;1061;1290
+C8:3F:8F:17:DB:35;1;1058;1294
 C3:00:00:39:47:DF;-1;-1;-1
-C3:00:00:39:47:C4;1;1386;1501
+C3:00:00:39:47:C4;1;1304;1478
 C3:00:00:39:47:E2;-1;-1;-1
-C7:AE:56:1E:38:B7;1;1311;1501
+C7:AE:56:1E:38:B7;1;1323;1499
 E0:1F:9A:7A:47:D2;-1;-1;-1
-C3:00:00:57:B9:D9;1;1060;1380
-C3:00:00:57:B9:DB;1;1056;1383
-C3:00:00:57:B9:F4;1;1061;1292
-C3:00:00:57:B9:DC;1;1060;1294
-C3:00:00:57:B9:DD;1;1216;1444
-C3:00:00:57:B9:DF;1;1059;1296
+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

data/maps.bck/meta_0.json

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

data/maps.bck/meta_1.json

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

data/maps.old/meta_0.json

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

data/maps.old/meta_1.json

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

data/maps/meta_-1.json

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

data/maps/meta_0.json

@@ -1 +1,9 @@
-{"pixel_ratio": 1.574668658056015, "calibrated": true, "origin": [1193, 1031], "grid_size": 50, "show_grid": true}
+{
+    "pixel_ratio": 1.584773230620443,
+    "calibrated": true,
+    "origin": [
+        1247,
+        3791
+    ],
+    "grid_size": 50
+}

data/maps/meta_1.json

@@ -1 +1,9 @@
-{"pixel_ratio": 1.5919341046277666, "calibrated": true, "origin": [1251, 1143], "grid_size": 50}
+{
+    "pixel_ratio": 1.5924129560451799,
+    "calibrated": true,
+    "origin": [
+        1297,
+        3587
+    ],
+    "grid_size": 50
+}

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

@@ -0,0 +1,2 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-21_-1_1050_250;-1;BC-21;1050;250;-1;BC-21;C3:00:00:57:B9:E6

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

@@ -0,0 +1,2 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-21_0_195_1425;0;BC-21;195;1425;0;BC-21;C3:00:00:57:B9:E6

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

@@ -0,0 +1,2 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-21_0_2114_796;0;BC-21;2114;796;0;BC-21;C3:00:00:57:B9:E6

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

@@ -0,0 +1,2 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+P_2115_697;0;BC-21;2115;697;0;BC-21;C3:00:00:57:B9:E6

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

@@ -0,0 +1,2 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-21_0_2568_946;0;BC-21;2568;946;0;BC-21;C3:00:00:57:B9:E6

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

@@ -0,0 +1,2 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-21_0_400_100;0;BC-21;400;100;0;BC-21;C3:00:00:57:B9:E6

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

@@ -0,0 +1,2 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-21_0_619_122;0;BC-21;619;122;0;BC-21;C3:00:00:57:B9:E6

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

@@ -0,0 +1,2 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-21_1_1050_500;1;BC-21;1050;500;1;BC-21;C3:00:00:57:B9:E6

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

@@ -0,0 +1,2 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-21_1_950_750;1;BC-21;950;750;1;BC-21;C3:00:00:57:B9:E6

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

@@ -0,0 +1,2 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-22_-1_1650_1100;-1;BC-22;1650;1100;-1;BC-22;C3:00:00:57:B9:D4

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

@@ -0,0 +1,2 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-22_-1_170_1418;-1;BC-22;170;1418;-1;BC-22;C3:00:00:57:B9:D4

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

@@ -0,0 +1,2 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-22_0_534_999;0;BC-22;534;999;0;BC-22;C3:00:00:57:B9:D4

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

@@ -0,0 +1,2 @@
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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/P_1356_1256.csv

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data/train/jobs/done/P_2142_616.csv

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data/train/jobs/done/P_2491_543.csv

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data/train/samples/-1_1050_250.csv

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data/train/samples/-1_1650_1100.csv

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data/train/samples/-1_170_1418.csv

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data/train/samples/0_1356_1256.csv

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data/train/samples/0_1580_180.csv

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data/train/samples/0_1600_450.csv

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data/train/samples/0_195_1425.csv

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data/train/samples/0_2114_796.csv

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data/train/samples/0_2115_697.csv

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data/train/samples/0_2142_616.csv

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data/train/samples/0_2491_543.csv

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data/train/samples/0_2568_946.csv

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data/train/samples/0_400_100.csv

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data/train/samples/0_534_999.csv

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data/train/samples/0_562_239.csv

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data/train/samples/0_619_122.csv

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data/train/samples/1_1050_400.csv

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data/train/samples/1_1050_500.csv

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data/train/samples/1_130_1386.csv

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data/train/samples/1_1450_600.csv

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+C3:00:00:57:B9:E7;1450;600;1;-83.500;nan;-83.000;nan;nan;nan;nan;nan;-79.000;-48.000;-71.000;-75.000;-77.000;-77.000;-83.000;-80.000

data/train/samples/1_1800_600.csv

@@ -0,0 +1,2 @@
+mac;x;y;z;AC:23:3F:C1:DD:3C;AC:23:3F:C1:DD:49;AC:23:3F:C1:DC:EE;AC:23:3F:C1:DD:40;AC:23:3F:C1:DD:51;AC:23:3F:C1:DD:48;AC:23:3F:C1:DD:50;AC:23:3F:C1:DC:D3;AC:23:3F:C1:DD:55;AC:23:3F:C1:DC:D1;AC:23:3F:C1:DC:CB;AC:23:3F:C1:DC:D2;AC:23:3F:C1:DD:31;AC:23:3F:C1:DD:4B;AC:23:3F:C1:DD:4E;AC:23:3F:C1:DC:CD
+C3:00:00:57:B9:E8;1800;600;1;-80.000;-82.000;-83.000;nan;nan;nan;nan;nan;-76.000;-59.000;-71.000;-64.000;-76.500;-79.000;-84.500;-72.000

data/train/samples/1_1900_1400.csv

@@ -0,0 +1,2 @@
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+C3:00:00:57:B9:F1;1900;1400;1;nan;nan;nan;nan;nan;nan;nan;nan;-77.000;nan;-69.000;-65.000;-71.000;-77.000;-82.000;-81.000

data/train/samples/1_2200_1000.csv

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+C3:00:00:57:B9:F1;2200;1000;1;nan;nan;nan;nan;nan;nan;nan;nan;-77.000;nan;-69.000;-65.000;-71.000;-78.000;-82.000;-81.000

data/train/samples/1_2400_300.csv

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+C3:00:00:57:B9:E7;2400;300;1;-83.000;nan;-83.000;nan;nan;nan;nan;nan;-78.000;-48.000;-71.000;-76.500;-77.000;-77.000;-83.000;-80.000

data/train/samples/1_2500_1300.csv

@@ -0,0 +1,2 @@
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+C3:00:00:57:B9:E7;2500;1300;1;-84.000;nan;-83.000;nan;nan;nan;nan;nan;-79.000;-48.000;-71.000;-77.000;-77.000;-77.500;-83.000;-80.000

data/train/samples/1_2750_200.csv

@@ -0,0 +1,2 @@
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+C3:00:00:57:B9:E6;2750;200;1;-72.000;-83.000;-76.000;nan;nan;nan;nan;nan;-80.000;-51.000;-74.500;-64.000;-70.000;-78.000;nan;-76.000

data/train/samples/1_950_750.csv

@@ -0,0 +1,2 @@
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docker-compose.yml

@@ -3,6 +3,7 @@ services:
     build: .
     image: ble-ai-localizer:0.1.0
     environment:
+      PYTHONWARNINGS: "ignore:Unverified HTTPS request"
       CONFIG: "/config/config.yaml"        # Nome usato da main.py
       SECRETS_FILE: "/config/secrets.yaml"
       UI_USER: "Admin"

requirements.txt

@@ -1,14 +1,25 @@
-pandas==2.2.2
+# --- CORE AI & DATA ---
 numpy==1.26.4
 scikit-learn==1.5.1
 joblib==1.4.2
+pandas==2.2.2
+pyarrow==15.0.0
 
+# --- COMUNICAZIONE & SISTEMA ---
 paho-mqtt==2.1.0
 requests==2.32.3
 PyYAML==6.0.2
+psutil==5.9.8
+pathlib==1.0.1
 
+# --- WEB & MAPPE ---
+streamlit==1.49.1
+pillow==10.2.0
 fastapi==0.115.0
 uvicorn[standard]==0.30.6
+folium==0.15.1
+streamlit-folium==0.26.1
 
+# --- VISUALIZZAZIONE ---
 matplotlib==3.9.2
 seaborn==0.13.2