"""SafeEvac AI — cPanel/Passenger WSGI build. Framework-free WSGI application so the hackathon demo can run on a standard cPanel Python Application without a separate Streamlit process or exposed port. """ from __future__ import annotations import base64 import csv import html import io import json import os from functools import lru_cache from pathlib import Path from urllib.parse import parse_qs, urlencode import pandas as pd from sklearn.compose import ColumnTransformer from sklearn.ensemble import RandomForestClassifier from sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline from sklearn.preprocessing import OneHotEncoder, StandardScaler from safeevac_model import ( FEATURES, predict_with_uncertainty, recommended_interventions, risk_band, train_model, validate_scenario, ) APP_DIR = Path(__file__).resolve().parent TITANIC_PATH = APP_DIR / "legacy_titanic" / "train.csv" SECTORS = ["Office", "Care", "Education", "Hospitality", "Retail", "Logistics", "Industry"] HAZARDS = ["Fire", "Smoke", "Power outage", "Chemical", "Flooding"] DEFAULT_SCENARIO = { "sector": "Office", "hazard": "Fire", "occupant_count": 250, "floors": 4, "exits_total": 3, "blocked_exits": 0, "stair_width_m": 1.4, "avg_route_m": 55.0, "bhv_count": 6, "mobility_support_count": 5, "sensory_cognitive_support_count": 4, "visitor_pct": 20.0, "alarm_delay_s": 75, "trained_occupants_pct": 65.0, "communication_channels": 2, "night_shift": 0, "drill_recent": 1, } CSS = r""" :root{--bg:#070c12;--panel:#0d151e;--panel2:#111c27;--line:#203141;--text:#f5f8fb;--muted:#8ea0b2;--blue:#18b5ff;--blue2:#195cff;--green:#40d56b;--orange:#ff9f1a;--red:#ff4d3d;--shadow:0 18px 50px rgba(0,0,0,.35)} *{box-sizing:border-box}body{margin:0;background:radial-gradient(circle at 70% 10%,#102131 0,#070c12 38%,#05080c 100%);color:var(--text);font-family:Inter,ui-sans-serif,system-ui,-apple-system,Segoe UI,Roboto,Arial,sans-serif;min-height:100vh} a{color:inherit;text-decoration:none}.shell{display:grid;grid-template-columns:220px minmax(0,1fr);min-height:100vh}.sidebar{position:sticky;top:0;height:100vh;border-right:1px solid var(--line);background:linear-gradient(180deg,rgba(10,19,29,.98),rgba(6,11,17,.98));padding:26px 18px}.brand{display:flex;align-items:center;gap:10px;font-size:20px;font-weight:800;margin:0 8px 32px}.shield{width:31px;height:31px;border:1.5px solid #d8f4ff;border-radius:10px;display:grid;place-items:center;color:var(--blue);box-shadow:0 0 25px rgba(24,181,255,.14)}.nav{display:grid;gap:7px}.nav a{padding:12px 13px;border:1px solid transparent;border-radius:8px;color:#aebbc8;font-size:14px;display:flex;gap:10px;align-items:center}.nav a:hover,.nav a.active{color:white;background:linear-gradient(90deg,rgba(20,102,187,.45),rgba(12,58,101,.25));border-color:#174c75;box-shadow:inset 3px 0 0 var(--blue)}.nav .ico{width:18px;text-align:center;color:#c5d0da}.ai-pill{position:absolute;bottom:28px;left:26px;border:1px solid #22384b;border-radius:20px;padding:7px 12px;color:#bdd5e6;font-size:12px;background:#0b1721}.main{padding:28px 34px 60px;max-width:1500px;width:100%;margin:auto}.topbar{display:flex;justify-content:space-between;align-items:flex-start;margin-bottom:23px}.title h1{font-size:28px;margin:0 0 5px}.title p{margin:0;color:var(--muted);font-size:13px}.tools{display:flex;gap:8px}.tool{width:35px;height:35px;border:1px solid var(--line);border-radius:50%;display:grid;place-items:center;color:#a9bdcb;background:#0a131c}.grid{display:grid;gap:16px}.g2{grid-template-columns:repeat(2,minmax(0,1fr))}.g3{grid-template-columns:repeat(3,minmax(0,1fr))}.g4{grid-template-columns:repeat(4,minmax(0,1fr))}.card{background:linear-gradient(155deg,rgba(17,29,40,.95),rgba(9,17,24,.98));border:1px solid var(--line);border-radius:11px;padding:18px;box-shadow:var(--shadow)}.card h3{font-size:13px;margin:0 0 14px;color:#d6e0e8}.metric-label{font-size:11px;text-transform:uppercase;letter-spacing:.07em;color:#93a7b8}.metric{font-size:48px;line-height:1;font-weight:750;margin:13px 0 8px}.metric small{font-size:18px;color:#c4d0d9;font-weight:500}.orange{color:var(--orange)}.red{color:var(--red)}.green{color:var(--green)}.blue{color:var(--blue)}.muted{color:var(--muted)}.status{font-weight:700;font-size:13px}.list{display:grid;gap:11px}.row{display:grid;grid-template-columns:1fr auto;gap:18px;align-items:center;font-size:13px}.dot{width:7px;height:7px;border-radius:50%;display:inline-block;margin-right:8px;background:var(--orange);box-shadow:0 0 9px currentColor}.badge{font-size:11px;font-weight:750}.bar{height:5px;background:#162430;border-radius:7px;overflow:hidden}.bar>span{display:block;height:100%;border-radius:7px;background:linear-gradient(90deg,var(--blue2),var(--blue));box-shadow:0 0 12px rgba(24,181,255,.7)}.bar.greenbar>span{background:linear-gradient(90deg,#178d43,var(--green))}.btn{appearance:none;border:1px solid #276fd8;border-radius:6px;background:linear-gradient(180deg,#174fae,#11377d);color:white;padding:10px 16px;font-weight:700;font-size:12px;cursor:pointer;box-shadow:0 0 18px rgba(32,104,255,.2)}.btn:hover{filter:brightness(1.12)}.btn.secondary{border-color:#2b3f51;background:#0d1924;color:#d8e5ef}.input{width:100%;border:1px solid #263a4a;background:#0b151e;color:#eef5fa;border-radius:5px;padding:9px 10px;outline:none}.input:focus{border-color:#1e8fd2;box-shadow:0 0 0 2px rgba(24,181,255,.1)}label{display:block;color:#9fb0be;font-size:11px;margin:0 0 5px}.field{margin-bottom:12px}.steps{display:grid;grid-template-columns:repeat(4,1fr);margin:7px 0 22px}.step{text-align:center;color:#71889a;font-size:11px;position:relative}.step:before{content:"";position:absolute;top:11px;left:-50%;right:50%;height:1px;background:#293b49}.step:first-child:before{display:none}.step span{position:relative;z-index:2;margin:auto auto 7px;width:24px;height:24px;border:1px solid #3a4a57;border-radius:50%;display:grid;place-items:center;background:#0c1620}.step.active{color:#a9ddff}.step.active span{border-color:#2f7bff;background:#1551b8;color:white;box-shadow:0 0 16px rgba(33,111,255,.45)}.table{width:100%;border-collapse:collapse;font-size:12px}.table th{text-align:left;color:#7f95a7;font-size:10px;text-transform:uppercase;letter-spacing:.05em;border-bottom:1px solid #22313d;padding:10px}.table td{padding:11px 10px;border-bottom:1px solid #172631;color:#d7e2ea}.table tr:last-child td{border-bottom:none}.recommend{display:flex;gap:9px;align-items:flex-start;font-size:12px;margin:9px 0;color:#d7e2ea}.check{color:var(--green);font-weight:900}.warning{border:1px solid #51371d;background:#19150e;border-radius:7px;padding:11px;color:#eebc72;font-size:12px}.okbox{border:1px solid #1c5830;background:#0d1b13;border-radius:7px;padding:11px;color:#95e8af;font-size:12px}.hero-score{display:flex;justify-content:center;align-items:center;min-height:190px}.ring{width:150px;height:150px;border-radius:50%;display:grid;place-items:center;background:conic-gradient(var(--blue) var(--pct),#16232e 0);position:relative;box-shadow:0 0 36px rgba(24,181,255,.14)}.ring:after{content:"";position:absolute;width:122px;height:122px;border-radius:50%;background:#0a141d}.ring strong{position:relative;z-index:1;font-size:36px}.ring strong small{font-size:13px;color:#91a5b4}.kpi{display:flex;justify-content:space-between;gap:10px;padding:10px 0;border-bottom:1px solid #1d2b37;font-size:12px}.kpi:last-child{border:0}.tabs{display:flex;gap:24px;border-bottom:1px solid #22313e;margin-bottom:18px}.tab{padding:0 0 10px;color:#8598a8;font-size:12px}.tab.active{color:var(--blue);border-bottom:2px solid var(--blue)}.hero{padding:26px;border:1px solid #1c3a50;border-radius:12px;background:radial-gradient(circle at 75% 35%,rgba(24,181,255,.11),transparent 35%),linear-gradient(135deg,#101d29,#09121a);overflow:hidden}.hero h2{font-size:31px;margin:5px 0 8px}.hero p{max-width:760px;color:#91a7b7;line-height:1.55;font-size:13px}.eyebrow{color:var(--blue);text-transform:uppercase;letter-spacing:.12em;font-size:10px;font-weight:800}.split{display:grid;grid-template-columns:1.2fr .8fr;gap:16px}.empty{padding:40px;text-align:center;color:#718798}.footer{margin-top:24px;color:#536979;font-size:10px;text-align:center}.donut{height:8px;border-radius:10px;background:#15242f;overflow:hidden}.donut span{display:block;height:100%;background:linear-gradient(90deg,var(--orange),var(--red))}.pill{display:inline-block;border:1px solid #294053;border-radius:20px;padding:5px 9px;font-size:10px;color:#adc2d2}.footer-actions{display:flex;justify-content:flex-end;gap:8px;margin-top:18px}.mobile-nav{display:none} @media(max-width:900px){.shell{display:block}.sidebar{display:none}.mobile-nav{display:flex;position:sticky;top:0;z-index:10;background:#09121a;border-bottom:1px solid var(--line);padding:10px;overflow:auto;gap:6px}.mobile-nav a{white-space:nowrap;border:1px solid #213545;border-radius:6px;padding:8px 10px;color:#adbfcb;font-size:11px}.main{padding:20px 14px 50px}.g2,.g3,.g4,.split{grid-template-columns:1fr}.topbar{margin-top:8px}.metric{font-size:40px}} """ NAV = [ ("/", "⌂", "Dashboard"), ("/scan", "▣", "Scenario Scan"), ("/results", "◎", "Resultaten"), ("/stress", "◌", "Stress Test"), ("/reports", "▤", "Rapporten"), ("/governance", "♢", "Gegevens & Governance"), ] def esc(value): return html.escape(str(value), quote=True) def response(start_response, body, status="200 OK", content_type="text/html; charset=utf-8", headers=None): raw = body.encode("utf-8") if isinstance(body, str) else body h = [("Content-Type", content_type), ("Content-Length", str(len(raw)))] if headers: h.extend(headers) start_response(status, h) return [raw] def parse_cookie(environ): cookie = environ.get("HTTP_COOKIE", "") out = {} for item in cookie.split(";"): if "=" in item: k, v = item.strip().split("=", 1) out[k] = v return out def scenario_cookie(scenario): payload = json.dumps(scenario, separators=(",", ":")).encode() return base64.urlsafe_b64encode(payload).decode() def load_scenario(environ): token = parse_cookie(environ).get("safeevac_scenario") if not token: return None try: return json.loads(base64.urlsafe_b64decode(token.encode()).decode()) except Exception: return None def scenario_df(s): return pd.DataFrame([s])[FEATURES] @lru_cache(maxsize=1) def get_model(): return train_model() @lru_cache(maxsize=1) def get_bias_metrics(): df = pd.read_csv(TITANIC_PATH) num = ["Pclass", "Age", "SibSp", "Parch", "Fare"] cat = ["Sex", "Embarked"] prep = ColumnTransformer([ ("num", Pipeline([("imp", SimpleImputer(strategy="median")), ("scale", StandardScaler())]), num), ("cat", Pipeline([("imp", SimpleImputer(strategy="most_frequent")), ("oh", OneHotEncoder(handle_unknown="ignore"))]), cat), ]) pipe = Pipeline([("prep", prep), ("rf", RandomForestClassifier(n_estimators=180, min_samples_leaf=2, random_state=42, n_jobs=1))]) pipe.fit(df[num + cat], df["Survived"]) sample = df[num + cat].dropna(subset=["Sex"]).sample(min(250, len(df)), random_state=42).copy() original = pipe.predict_proba(sample)[:, 1] flip = sample.copy(); flip["Sex"] = flip["Sex"].map({"male":"female", "female":"male"}) sex_pp = float((abs(original - pipe.predict_proba(flip)[:, 1]) * 100).mean()) cls = sample.copy(); cls["Pclass"] = cls["Pclass"].map({1:3,2:2,3:1}) class_pp = float((abs(original - pipe.predict_proba(cls)[:, 1]) * 100).mean()) return len(df), sex_pp, class_pp def nav_html(path): links = [] for href, icon, label in NAV: active = "active" if (path == href or (href != "/" and path.startswith(href))) else "" links.append(f'{icon}{esc(label)}') return "".join(links) def layout(path, title, subtitle, content): mobile = "".join(f'{esc(l)}' for h, _, l in NAV) return f""" {esc(title)} · SafeEvac AI
{mobile}

{esc(title)}

{esc(subtitle)}

?◔●
{content}
SafeEvac AI prototype · Decision support only · Geen automatische persoonsprioritering
""" def analyze(s): model, metrics = get_model() df = scenario_df(s) issues = validate_scenario(df) if issues: return {"issues": issues, "metrics": metrics} risk, p10, p90 = predict_with_uncertainty(model, df) readiness = max(0.0, min(100.0, 100.0 - risk)) actions = recommended_interventions(model, df, risk) return {"risk": risk, "readiness": readiness, "p10": p10, "p90": p90, "band": risk_band(risk), "actions": actions, "metrics": metrics} def dashboard(environ): s = load_scenario(environ) or DEFAULT_SCENARIO a = analyze(s) readiness = a.get("readiness", 68) risk = a.get("risk", 62) actions = a.get("actions", [])[:5] risks = [] if s["blocked_exits"]: risks.append(("Beperkte uitgangscapaciteit", "Hoog", "red")) support = s["mobility_support_count"] + s["sensory_cognitive_support_count"] if support > s["bhv_count"]: risks.append(("Assistentiebehoefte vs. capaciteit", "Middel", "orange")) if s["alarm_delay_s"] > 60: risks.append(("Lange alarm-/responstijd", "Middel", "orange")) if not s["drill_recent"]: risks.append(("Geen recente oefening", "Laag", "orange")) if s["communication_channels"] < 3: risks.append(("Communicatiekanalen beperkt", "Laag", "orange")) risks = (risks + [("Bezoekers kennen de locatie beperkt", "Laag", "orange")])[:5] risk_rows = "".join(f'
{esc(x)}{esc(b)}
' for x,b,c in risks) improvement = sum(max(0, x["risk_reduction"]) for x in actions[:3]) if actions else 0 content = f"""
Responsible AI · Emergency Preparedness

Zie kwetsbaarheden vóór een calamiteit.

SafeEvac stress-test evacuatieplannen met gebouw-, bezettings- en responsdata. De AI adviseert waar je plan versterkt kan worden; een veiligheidsprofessional blijft eindverantwoordelijk.

Nieuwe scenario scanStress test
Evacuation Readiness Score
{readiness:.0f}/100
{'Goed' if readiness>=70 else 'Matig' if readiness>=45 else 'Laag'}

Laatste opgeslagen browser-scenario

Nieuwe scan
{readiness:.0f}/100

Top risico's

{risk_rows}

Verbeterpotentieel

+{improvement:.0f} pt

indicatieve verbetering uit de sterkste model-hefbomen

Operationeel risico

{risk:.0f}/100

Geen certificeringsscore; bedoeld voor scenario- en managementondersteuning.

""" return layout("/", "Dashboard", "Overzicht van evacuatiegereedheid", content) def option_list(values, selected): return "".join(f'' for v in values) def number_field(name, label, value, step="1", minv="0", maxv="10000"): return f'
' def scan_page(environ, method, post): s = load_scenario(environ) or DEFAULT_SCENARIO.copy() if method == "POST": try: s = { "sector": post.get("sector", ["Office"])[0], "hazard": post.get("hazard", ["Fire"])[0], "occupant_count": int(post["occupant_count"][0]), "floors": int(post["floors"][0]), "exits_total": int(post["exits_total"][0]), "blocked_exits": int(post["blocked_exits"][0]), "stair_width_m": float(post["stair_width_m"][0]), "avg_route_m": float(post["avg_route_m"][0]), "bhv_count": int(post["bhv_count"][0]), "mobility_support_count": int(post["mobility_support_count"][0]), "sensory_cognitive_support_count": int(post["sensory_cognitive_support_count"][0]), "visitor_pct": float(post["visitor_pct"][0]), "alarm_delay_s": int(post["alarm_delay_s"][0]), "trained_occupants_pct": float(post["trained_occupants_pct"][0]), "communication_channels": int(post["communication_channels"][0]), "night_shift": 1 if "night_shift" in post else 0, "drill_recent": 1 if "drill_recent" in post else 0, } issues = validate_scenario(scenario_df(s)) if not issues: token = scenario_cookie(s) return None, [("Location", "/results"), ("Set-Cookie", f"safeevac_scenario={token}; Path=/; SameSite=Lax")] error = " ".join(issues) except Exception as exc: error = f"Controleer de ingevoerde waarden: {exc}" else: error = "" content = f"""
1Basisinfo
2Bezetting & Personen
3Hulpbronnen
4Incident & Omgeving
{f'
{esc(error)}
' if error else ''}

Basisinformatie

{number_field('floors','Aantal verdiepingen',s['floors'],minv='1',maxv='50')} {number_field('occupant_count','Aantal aanwezige personen',s['occupant_count'],minv='1')} {number_field('exits_total','Aantal uitgangen',s['exits_total'],minv='1',maxv='30')} {number_field('blocked_exits','Geblokkeerde uitgangen',s['blocked_exits'],minv='0',maxv='29')} {number_field('stair_width_m','Gemiddelde trap-/egressbreedte (m)',s['stair_width_m'],step='0.1',minv='0.5',maxv='5')} {number_field('avg_route_m','Gemiddelde evacuatieroute (m)',s['avg_route_m'],step='5',minv='5',maxv='500')}

Mensen, ondersteuning & respons

{number_field('bhv_count','Beschikbare BHV/responders',s['bhv_count'],minv='1',maxv='500')} {number_field('mobility_support_count','Mobiliteitsondersteuning nodig',s['mobility_support_count'],minv='0')} {number_field('sensory_cognitive_support_count','Extra communicatie/begeleiding nodig',s['sensory_cognitive_support_count'],minv='0')} {number_field('visitor_pct','Bezoekers / onbekend met locatie (%)',s['visitor_pct'],step='1',minv='0',maxv='100')} {number_field('trained_occupants_pct','Bekend met evacuatieprocedure (%)',s['trained_occupants_pct'],step='1',minv='0',maxv='100')} {number_field('alarm_delay_s','Alarm + responsvertraging (sec)',s['alarm_delay_s'],step='5',minv='0',maxv='600')} {number_field('communication_channels','Redundante communicatiekanalen',s['communication_channels'],minv='1',maxv='5')}
""" return layout("/scan", "Nieuwe scenario scan", "Voer de kernwaarden van gebouw en situatie in", content), None def results_page(environ): s = load_scenario(environ) if not s: return layout("/results", "Scan resultaten", "Nog geen scenario beschikbaar", "
Start eerst een Scenario Scan.
") a = analyze(s) if a.get("issues"): return layout("/results", "Scan resultaten", "Validatieprobleem", f"
{esc(' '.join(a['issues']))}
") risk, readiness = a["risk"], a["readiness"] action_rows = "".join(f'
{i}. {esc(x["action"])}+{x["risk_reduction"]:.1f}
' for i,x in enumerate(a["actions"][:5],1)) or '

Geen directe model-hefbomen gevonden.

' recs = "".join(f'
●{esc(x["action"])}
' for x in a["actions"][:3]) total_support=s['mobility_support_count']+s['sensory_cognitive_support_count'] content=f"""
Evacuation Readiness Score
{readiness:.0f}/100
{'Goed' if readiness>=70 else 'Matig' if readiness>=45 else 'Laag'}
Operationeel risico
{risk:.0f}/100
{esc(a['band'])}
Onzekerheid (modelspreiding)
{a['p10']:.0f}–{a['p90']:.0f}
P10–P90 bomen

Belangrijkste context

Sector / scenario{esc(s['sector'])} · {esc(s['hazard'])}
Bezetting{s['occupant_count']}
Uitgangen{s['exits_total']-s['blocked_exits']} bruikbaar / {s['exits_total']} totaal
Extra ondersteuningsvragen{total_support}
BHV/responders{s['bhv_count']}

Grootste verbeteringen (impact)

{action_rows}

Aanbevolen acties

{recs}
Human-in-the-loop: SafeEvac adviseert over capaciteit en kwetsbaarheden; de AI bepaalt nooit wie hulp krijgt of wie eerst geëvacueerd wordt.
Stress testRapport downloaden
""" return layout("/results", "Scan resultaten", f"{s['sector']} · {s['hazard']}", content) def stress_page(environ): s=load_scenario(environ) if not s: return layout("/stress","Stress test","Bekijk wat er verandert bij verstoringen","
Start eerst een Scenario Scan.
") model,_=get_model(); df=scenario_df(s); base=float(model.predict(df[FEATURES])[0]) cases=[ ("Extra uitgang geblokkeerd","blocked_exits",min(s['exits_total']-1,s['blocked_exits']+1)), ("Alarmvertraging +90 sec","alarm_delay_s",min(600,s['alarm_delay_s']+90)), ("Communicatie uitval","communication_channels",1), ("Nachtbezetting","night_shift",1), ("Geen recente oefening","drill_recent",0), ] results=[] for label,feature,value in cases: alt=df.copy(); alt.loc[0,feature]=value; val=float(model.predict(alt[FEATURES])[0]); results.append((label,val,val-base)) worst=max(results,key=lambda x:x[2]) rows="".join(f'{esc(l)}5 else "orange" if d>0 else "green"}">{v:.1f}{d:+.1f}' for l,v,d in sorted(results,key=lambda x:x[2],reverse=True)) pts=[base]+[x[1] for x in results]; maxv=max(100,max(pts)); coords=[] for i,v in enumerate(pts): x=20+i*(360/(len(pts)-1)); y=150-(v/maxv)*120; coords.append(f"{x:.0f},{y:.0f}") content=f"""

Stressfactoren

{rows}
ScenarioRisicoΔ

Resultaat zwaarste test

Nieuwe score
{worst[1]:.0f}/100
Verandering
{worst[2]:+.0f} pt

Score verloop

{''.join(f'' for p in coords)}
Impact: {esc(worst[0])} heeft in dit model de grootste negatieve invloed. Gebruik dit om een oefening of managementbesluit voor te bereiden, niet als realtime automatische evacuatiebesturing.
""" return layout("/stress","Stress test","Bekijk hoe je score verandert bij verschillende situaties",content) def reports_page(environ): s=load_scenario(environ) if s: a=analyze(s); score=f"{a.get('readiness',0):.0f}"; risk=f"{a.get('risk',0):.0f}" current=f"Laatste SafeEvac scan{esc(s['sector'])} · {esc(s['hazard'])}Browser-sessie{score}Download" else: current="Nog geen scenario in deze browser." content=f"""

Gegenereerde rapporten

Nieuw rapport
{current}
Rapport naamScenarioDatumScore

Rapport bevat

▤

Scenario overzicht

◎

Risico analyse

◇

Aanbevelingen

◌

Stress test context

""" return layout("/reports","Rapporten","Overzicht en export",content) def governance_page(environ): _,metrics=get_model(); n,sex_pp,class_pp=get_bias_metrics() used=[("Gebouwkenmerken","verdiepingen, uitgangen, route, egressbreedte"),("Bezetting","aantallen en bezoekerspercentage"),("Hulpbronnen","BHV, communicatie, alarmrespons"),("Inclusieve planning","geaggregeerde ondersteuningsbehoefte"),("Context","sector, hazard, nachtorganisatie")] excluded=["Geslacht","Leeftijd per persoon","Gezondheidsdiagnose","Nationaliteit","Inkomen / sociale klasse","Andere persoonskenmerken"] used_html="".join(f'
▧ {esc(a)}{esc(b)}
' for a,b in used) ex_html="".join(f'
×{esc(x)}
' for x in excluded) content=f"""
Data gebruikModel informatieEthiek & BiasBeperkingen

Gebruikte data (geen individuele persoonsprofielen)

{used_html}
Prototype trainingsdata6.000 scenario's

Bewust uitgesloten

{ex_html}
Model MAE
{metrics.mae:.1f}

risicopunten op synthetische hold-out data

R²
{metrics.r2:.2f}

prototype fit, geen real-world certificering

Titanic bias demo
{sex_pp:.1f} pp

gemiddelde gevoeligheid bij sex-flip; klasse-flip {class_pp:.1f} pp over {n} historische rijen

✓

Ons commitment

SafeEvac ondersteunt besluitvorming. Het systeem maakt nooit onderscheid tussen personen en bepaalt niet wie hulp krijgt. Voor productie zijn echte ontruimingsoefeningen, gebouwdata en onafhankelijke safety-validatie nodig.

""" return layout("/governance","Gegevens & Governance","Transparantie over data, model en ethiek",content) def report_download(environ, start_response): s=load_scenario(environ) if not s: return response(start_response,"Geen scenario beschikbaar.","404 Not Found","text/plain; charset=utf-8") a=analyze(s); out=io.StringIO(); w=csv.writer(out) w.writerow(["SafeEvac AI Evacuation Readiness Report"]); w.writerow([]) w.writerow(["Readiness score",f"{a['readiness']:.1f}/100"]); w.writerow(["Operational risk",f"{a['risk']:.1f}/100"]); w.writerow(["Risk band",a['band']]); w.writerow(["Model spread P10-P90",f"{a['p10']:.1f}-{a['p90']:.1f}"]); w.writerow([]) w.writerow(["Scenario field","Value"]) for k,v in s.items(): w.writerow([k,v]) w.writerow([]); w.writerow(["Recommended intervention","Estimated risk reduction"]) for x in a['actions'][:5]: w.writerow([x['action'],f"{x['risk_reduction']:.1f}"]) w.writerow([]); w.writerow(["Governance note","Decision support only; no automatic person ranking or evacuation priority."]) data=out.getvalue().encode('utf-8-sig') return response(start_response,data,content_type='text/csv; charset=utf-8',headers=[('Content-Disposition','attachment; filename="safeevac_report.csv"')]) def application(environ, start_response): path=environ.get('PATH_INFO','/') or '/'; method=environ.get('REQUEST_METHOD','GET').upper() try: if path=='/': return response(start_response,dashboard(environ)) if path=='/scan': post={} if method=='POST': try: length=int(environ.get('CONTENT_LENGTH') or 0) except ValueError: length=0 body=environ['wsgi.input'].read(length).decode('utf-8','replace'); post=parse_qs(body,keep_blank_values=True) page, headers=scan_page(environ,method,post) if headers: return response(start_response,b'',status='303 See Other',content_type='text/plain; charset=utf-8',headers=headers) return response(start_response,page) if path=='/results': return response(start_response,results_page(environ)) if path=='/stress': return response(start_response,stress_page(environ)) if path=='/reports': return response(start_response,reports_page(environ)) if path=='/governance': return response(start_response,governance_page(environ)) if path=='/download-report': return report_download(environ,start_response) return response(start_response,layout(path,'Niet gevonden','404',"
Deze pagina bestaat niet. Terug naar dashboard.
"),'404 Not Found') except Exception as exc: body=layout(path,'Applicatiefout','SafeEvac kon deze aanvraag niet verwerken',f"
Fout: {esc(exc)}

Controleer of de Python packages uit requirements.txt zijn geïnstalleerd en de data-map aanwezig is.
") return response(start_response,body,'500 Internal Server Error')