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.dockerignore

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+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.venv/
+.env
+.git/
+.ipynb_checkpoints/

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+venv/
+ENV/
+*.egg-info/
+*.egg
+dist/
+build/
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+coverage.xml
+*.cover
+.hypothesis/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+
+# Logs
+*.log
+*.out
+*.err
+
+# Models and large files
+models/*.joblib
+models/*.json
+
+# Ignore sensitive files
+.env
+*.key
+*.pem

app.py

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+import sys
+import os
+import time
+import json
+import logging
+
+import joblib
+import numpy as np
+import pandas as pd
+
+from flask import Flask, request, jsonify
+from sklearn.pipeline import Pipeline
+
+from features.feature_builder import build_features
+from schemas.request_schema import PredictRequest
+
+
+# ======================
+# PATH SETUP
+# ======================
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, BASE_DIR)
+
+
+# ======================
+# APP INIT
+# ======================
+app = Flask(__name__)
+
+
+# ======================
+# LOGGING
+# ======================
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+
+# ======================
+# SECURITY CONFIG
+# ======================
+API_KEY = os.getenv("FRAUD_API_KEY")
+MAX_REQUEST_SIZE = 10_000  # 10 KB
+RATE_LIMIT = 30
+RATE_LIMIT_WINDOW = 60  # seconds
+
+rate_limit_store = {}
+
+
+def is_rate_limited(client_ip: str) -> bool:
+    now = time.time()
+
+    if client_ip not in rate_limit_store:
+        rate_limit_store[client_ip] = []
+
+    rate_limit_store[client_ip] = [
+        t for t in rate_limit_store[client_ip]
+        if now - t < RATE_LIMIT_WINDOW
+    ]
+
+    if len(rate_limit_store[client_ip]) >= RATE_LIMIT:
+        return True
+
+    rate_limit_store[client_ip].append(now)
+    return False
+
+
+# ======================
+# GLOBAL API KEY GUARD
+# ======================
+@app.before_request
+def check_api_key():
+    # Health endpoint is public
+    if request.path == "/health":
+        return
+
+    # Skip static files if any
+    if request.path.startswith("/static"):
+        return
+
+    if not API_KEY:
+        logging.error("FRAUD_API_KEY environment variable not set")
+        return jsonify({"error": "Server misconfigured"}), 500
+
+    client_key = request.headers.get("X-API-KEY")
+    if not client_key or client_key != API_KEY:
+        return jsonify({"error": "Unauthorized"}), 401
+
+
+# ======================
+# LOAD MODEL & PREPROCESSOR
+# ======================
+MODEL_PATH = os.path.join(BASE_DIR, "models", "ensemble_model_enhanced.joblib")
+PREPROCESSOR_PATH = os.path.join(BASE_DIR, "models", "preprocessor_enhanced.joblib")
+
+if not os.path.exists(MODEL_PATH):
+    raise FileNotFoundError(f"Model tidak ditemukan: {MODEL_PATH}")
+
+if not os.path.exists(PREPROCESSOR_PATH):
+    raise FileNotFoundError(f"Preprocessor tidak ditemukan: {PREPROCESSOR_PATH}")
+
+model = joblib.load(MODEL_PATH)
+preprocessor = joblib.load(PREPROCESSOR_PATH)
+
+pipeline_model = Pipeline([
+    ("preprocess", preprocessor),
+    ("classifier", model)
+])
+
+THRESHOLD = 0.6
+
+
+# ======================
+# HEALTH CHECK
+# ======================
+@app.route("/health", methods=["GET"])
+def health():
+    return jsonify({
+        "status": "ok",
+        "model_loaded": model is not None,
+        "timestamp": time.time()
+    })
+
+
+# ======================
+# PREDICT
+# ======================
+@app.route("/predict", methods=["POST"])
+def predict():
+    start_time = time.time()
+
+    # ---------- REQUEST SIZE ----------
+    if request.content_length and request.content_length > MAX_REQUEST_SIZE:
+        return jsonify({"error": "Request too large"}), 413
+
+    # ---------- RATE LIMIT ----------
+    client_ip = request.remote_addr or "unknown"
+    if is_rate_limited(client_ip):
+        return jsonify({"error": "Too many requests"}), 429
+
+    # ---------- PARSE & VALIDATE ----------
+    try:
+        payload = request.get_json()
+        req = PredictRequest(**payload)
+        data = req.model_dump()
+        logging.info("Request valid: %s", data)
+    except Exception as e:
+        return jsonify({
+            "error": "Invalid request schema",
+            "detail": str(e)
+        }), 422
+
+    # ---------- BUSINESS VALIDATION ----------
+    amount = data.get("amount", 0)
+    location = data.get("location", -1)
+
+    if amount <= 0 or amount > 100_000_000:
+        return jsonify({"error": "Invalid amount value"}), 400
+
+    if location < 0:
+        return jsonify({"error": "Invalid location value"}), 400
+
+    # ---------- FEATURE BUILD ----------
+    try:
+        X_df = build_features(data)
+        logging.info("Feature DF: %s", X_df.to_dict(orient="records"))
+    except Exception as e:
+        logging.error(f"Feature building error: {e}")
+        return jsonify({
+            "error": "Feature building error",
+            "detail": str(e)
+        }), 500
+
+    # ---------- PREDICT ----------
+    try:
+        X = preprocessor.transform(X_df)
+        fraud_prob = model.predict_proba(X)[0][1]
+    except Exception as e:
+        logging.error(f"Prediction error: {e}")
+        return jsonify({
+            "error": "Preprocessing or prediction error",
+            "detail": str(e)
+        }), 500
+
+    # ---------- DECISION ----------
+    is_fraud = fraud_prob >= THRESHOLD
+
+    if fraud_prob >= 0.85:
+        decision = "BLOCK"
+    elif fraud_prob >= THRESHOLD:
+        decision = "REVIEW"
+    else:
+        decision = "ALLOW"
+
+    latency_ms = round((time.time() - start_time) * 1000, 2)
+
+    # ---------- STRUCTURED LOG ----------
+    logging.info({
+        "event": "fraud_decision",
+        "fraud_probability": float(fraud_prob),
+        "decision": decision,
+        "threshold": THRESHOLD,
+        "latency_ms": latency_ms
+    })
+
+    return jsonify({
+        "fraud_probability": round(float(fraud_prob), 4),
+        "is_fraud": bool(is_fraud),
+        "decision": decision,
+        "latency_ms": latency_ms
+    })
+
+
+# ======================
+# RUN SERVER
+# ======================
+if __name__ == "__main__":
+    app.run(debug=True, port=5001)

dockerfile

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+FROM python:3.11-slim
+
+# Environment safety
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+# Workdir
+WORKDIR /app
+
+# System deps (minimal)
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first (cache friendly)
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app code
+COPY . .
+
+# Expose port
+EXPOSE 5001
+
+# Run with gunicorn (PRODUCTION)
+CMD ["gunicorn", "-w", "2", "-b", "0.0.0.0:5001", "app:app"]

main.py

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+from fastapi import FastAPI
+
+app = FastAPI()
+
+@app.get("/")
+def root():
+    return {"status": "ok"}

note.md

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+# note
+
+🔖 **Ringkasan singkat (untuk dibaca besok)**
+
+- Project: API deteksi fraud berbasis Flask.
+- Status saat ini: semua unit & integration tests lulus (2 passed).
+- Model files: `ensemble_model_enhanced.joblib` (prioritas) / `ensemble_model.joblib` / `Ensemble_model.joblib`.
+- Preprocessor files: `preprocessor_enhanced.joblib` (prioritas) / `preprocessor.joblib` / `Preprocessor.joblib`.
+- Config: `anscombe.json` (cari case-insensitive).
+- Dependencies penting: `scikit-learn==1.6.1` (dipin), `imbalanced-learn` (untuk unpickle model), `requests` (untuk integrasi).
+
+---
+
+## Cara menjalankan server (cepat)
+
+1. Pastikan dependencies terpasang:
+```powershell
+& "C:\Users\CINDY\AppData\Local\Programs\Python\Python310\python.exe" -m pip install -r requirements.txt
+```
+2. Jalankan server Flask (foreground):
+```powershell
+& "C:\Users\CINDY\AppData\Local\Programs\Python\Python310\python.exe" app.py
+```
+Server akan tersedia di: `http://127.0.0.1:5000` (default). Tekan `Ctrl+C` untuk stop.
+
+3. Jalankan server di background dan simpan log:
+```powershell
+Start-Process -FilePath "C:\Users\CINDY\AppData\Local\Programs\Python\Python310\python.exe" -ArgumentList "app.py" -RedirectStandardOutput server_out.log -RedirectStandardError server_err.log -PassThru
+Get-Content server_out.log -Tail 50 -Wait
+```
+
+---
+
+## Cara menguji endpoint `/predict`
+
+- PowerShell:
+```powershell
+Invoke-RestMethod -Uri http://127.0.0.1:5000/predict -Method Post -ContentType 'application/json' -Body '{"features":[200,1,0,500]}'
+```
+- curl:
+```powershell
+curl -X POST http://127.0.0.1:5000/predict -H "Content-Type: application/json" -d "{\"features\":[200,1,0,500]}"
+```
+- Python one-liner:
+```powershell
+& "C:\Users\CINDY\AppData\Local\Programs\Python\Python310\python.exe" -c "import requests; print(requests.post('http://127.0.0.1:5000/predict', json={'features':[200,1,0,500]}).json())"
+```
+
+Respons contoh:
+```json
+{"fraud":0, "fraud_prediction":0, "probability":0.83}
+```
+
+---
+
+## Menguji frontend (quick smoke test)
+
+1. Pastikan server Flask berjalan (lihat bagian "Cara menjalankan server").
+2. Serving file frontend agar fetch berjalan tanpa masalah CORS dari file://, mis. jalankan dari folder `frontend`:
+```powershell
+cd frontend
+python -m http.server 8000
+# lalu buka http://localhost:8000/fraud_detection_frontend.html di browser
+```
+3. Periksa `API_BASE_URL` di `frontend/fraud_detection_frontend.html` — default saya set ke `http://localhost:5000` karena endpoint `/predict` ada di root.
+4. Form akan mengirimkan payload lengkap yang mencakup field yang diharapkan preprocessor (contoh payload yang berhasil diuji):
+```json
+{
+	"merchant_name":"Test",
+	"avg_amount_per_transaction":123.45,
+	"day_of_week":2,
+	"amount_deviation_from_location_mean":0,
+	"transaction_category":"retail",
+	"customer_no_transactions":0,
+	"customer_lat":null,
+	"transaction_type":"online",
+	"customer_place_name":null,
+	"merchant_id":null,
+	"location":"New York",
+	"customer_job":null,
+	"age":null,
+	"merchant_long":null,
+	"amount_per_city_pop":0,
+	"customer_long":null,
+	"distance_customer_merchant":0,
+	"transactions_per_customer_ratio":0,
+	"customer_city_population":0,
+	"merchant_lat":null,
+	"customer_no_payments":0,
+	"customer_no_orders":0,
+	"payments_per_order_ratio":0,
+	"hour_of_day":12,
+	"amount":123.45,
+	"customer_zip_code":null,
+	"mean_amount_by_location":0,
+	"fraud_rate_by_location":0,
+	"customer_gender":null
+}
+```
+
+Hasil uji lokal (contoh): server merespons 200 dan body JSON seperti `{"fraud":0,"fraud_prediction":0,"probability":0.766...}`
+
+---
+
+## Menjadikan Frontend ke Aplikasi Mobile (ringkasan & langkah cepat)
+
+Jika ingin agar frontend bisa diakses seperti aplikasi mobile, ada 2 jalur praktis yang saya rekomendasikan:
+
+- **PWA (Progressive Web App)** — tercepat dan paling mudah dicoba.
+	- Buat `manifest.json` (name, short_name, icons, start_url, display: standalone).
+	- Tambah `sw.js` (service worker) untuk caching (app shell) dan memungkinkan akses offline terbatas.
+	- Pastikan serve via HTTPS (Chrome/Edge mewajibkan HTTPS untuk installable PWA). Untuk development, pakai `ngrok http 8000` atau host sementara.
+	- Pengguna bisa pilih "Add to Home screen" di browser Android/Chrome.
+	- Cocok untuk prototipe dan distribusi cepat tanpa perlu build native.
+
+- **Capacitor (WebView wrapper → Android/iOS native app)** — bila mau jadi APK/IPA.
+	- Butuh Node.js dan Android SDK (untuk Android) atau Xcode (untuk iOS).
+	- Langkah ringkas:
+		```powershell
+		# di folder project root
+		npm init -y
+		npm install @capacitor/core @capacitor/cli
+		npx cap init my-app com.example.myapp
+		# Copy output static (letakkan file html ke folder `www/` atau build pipeline)
+		npx cap add android
+		npx cap copy android
+		npx cap open android
+		```
+	- Untuk dev lokal saat backend di localhost, gunakan `ngrok` (atau host backend) supaya device nyata bisa reach API.
+
+### Checklist penting sebelum rilis
+- Pastikan backend reachable dari device (hosted / ngrok untuk testing).
+- Gunakan HTTPS untuk API (Play Store & browser modern membatasi HTTP).
+- Pastikan CORS/Origin sudah diatur (PWA: origin penting; WebView biasanya tidak terkena CORS sama cara).
+- Otentikasi & keamanan: pakai token, jangan hard-code credentials di kode klien.
+- Ukuran & performa: minimalkan asset (gambar, script) dan aktifkan caching service worker untuk PWA.
+- Untuk Play Store: sign APK dengan key, perhatikan kebijakan privasi & permission.
+
+### Contoh resource cepat (starter files)
+- `manifest.json` (minimal):
+```json
+{
+	"name": "Fraud Detection AI",
+	"short_name": "FraudAI",
+	"start_url": "/fraud_detection_frontend.html",
+	"display": "standalone",
+	"icons": [ { "src": "/icons/icon-192.png", "sizes": "192x192", "type": "image/png" } ]
+}
+```
+- `sw.js` (very small cache-first):
+```js
+const CACHE_NAME = 'fraud-ai-v1';
+const ASSETS = ['/', '/fraud_detection_frontend.html', '/styles.css'];
+self.addEventListener('install', e => {
+	e.waitUntil(caches.open(CACHE_NAME).then(cache => cache.addAll(ASSETS)));
+});
+self.addEventListener('fetch', e => {
+	e.respondWith(caches.match(e.request).then(r => r || fetch(e.request)));
+});
+```
+
+---
+
+## Implementasi PWA & Capacitor (yang sudah saya tambahkan)
+
+- Sudah saya tambahkan ke repo:
+	- `frontend/manifest.json`, `frontend/sw.js`, `frontend/icons/icon.svg`
+	- `frontend/README_PWA.md` (cara test & catatan)
+	- Perubahan pada `frontend/fraud_detection_frontend.html` (link `manifest.json`, register `sw.js`)
+	- `mobile/README_CAPACITOR.md` berisi langkah awal untuk membungkus aplikasi dengan Capacitor
+
+Jika Anda ingin saya lanjut scaffold project Capacitor (init + add android, script npm), saya bisa kerjakan — beri tahu saya, dan saya akan buat branch terpisah karena itu menambahkan file Node.js/platform yang besar.
+
+
+---
+
+## Menjalankan tests
+
+- Jalankan semua tests (unit + integration):
+```powershell
+& "C:\Users\CINDY\AppData\Local\Programs\Python\Python310\python.exe" -m pytest -q
+```
+- Hanya unit tests (skip integration):
+```powershell
+& "C:\Users\CINDY\AppData\Local\Programs\Python\Python310\python.exe" -m pytest -q -m "not integration"
+```
+- Hanya integration tests:
+```powershell
+& "C:\Users\CINDY\AppData\Local\Programs\Python\Python310\python.exe" -m pytest -q -m integration
+```
+
+> Catatan: integration test men-start server otomatis pada port `5001`.
+
+---
+
+## Debugging cepat (jika error)
+
+- Jika mendapat `Connection refused`: server tidak jalan atau port diblokir. Cek log `server_out.log` / `server_err.log` dan jalankan server di foreground untuk melihat traceback.
+- Jika mendapat `500 Internal Server Error`: buka `server_err.log`, salin traceback, lalu perbaiki (bisa minta saya terjemahkan/diagnosa).
+
+Perintah cek log:
+```powershell
+Get-Content server_err.log -Tail 200
+```
+
+---
+
+## Hal lain yang sudah saya lakukan
+- Menambahkan file `tests/test_integration.py` dan `scripts/run_integration_debug.py` (debug helper).
+- Menambahkan `.vscode/settings.json` untuk pytest discovery.
+- Membuat `pytest.ini` dengan marker `integration`.
+- Membuat perubahan di `app/model.py` agar lebih fleksibel memuat model/preprocessor dan meng-handle input fitur yang berbeda panjang.
+
+---
+
+## Rencana selanjutnya (opsional)
+- Commit & push perubahan jika Anda mau (saya bisa bantu buat pesan commit). 
+- Tambahkan CI (GitHub Actions) agar tests jalan otomatis di push/PR.
+- Buatkan `DEVELOPMENT_NOTES.md` yang lebih panjang (jika Anda mau dokumentasi lebih lengkap).
+
+---
+
+Jika mau, saya bisa langsung commit file `note.md` ini (atau buat branch & PR). Mau saya commit sekarang? ✅
+
+---
+
+## Uji Stabilitas / Load testing
+
+Kalau Anda mengalami kesulitan membuat uji stabilitas, berikut langkah praktis yang bisa dilakukan secara lokal untuk mengecek bagaimana backend `predict` berperilaku di bawah beban.
+
+1) Jalankan server dengan server WSGI produksi
+- Windows: gunakan `waitress` (sederhana dan cocok di Windows)
+```powershell
+python -m pip install waitress
+waitress-serve --listen=0.0.0.0:5000 app:app
+```
+- Linux/macOS: gunakan `gunicorn`
+```bash
+python -m pip install gunicorn
+gunicorn -w 4 -b 0.0.0.0:5000 app:app
+```
+
+2) Jalankan Locust (sudah saya siapkan file `load_tests/locustfile.py`)
+```powershell
+python -m pip install locust
+locust -f load_tests/locustfile.py --host=http://localhost:5000
+# buka http://localhost:8089 untuk mengatur jumlah users dan spawn rate
+```
+
+3) Panduan uji bertahap
+- Mulai kecil: 5 users, spawn rate 1/s selama 30s. Naikkan perlahan (20 → 50 → 100) sambil memonitor CPU/RAM dan error rate.
+- Perhatikan latency p50/p95/p99 dan request failures di Locust UI.
+
+4) Jika Anda ingin menguji dari perangkat mobile (Capacitor) atau perangkat nyata:
+- Pastikan backend dapat dijangkau dari device — gunakan `ngrok http 5000` untuk membuat HTTPS tunnel, lalu gunakan URL ngrok sebagai `API_BASE_URL` di frontend.
+
+5) Interpretasi singkat
+- Error rate tinggi: kemungkinan server kehabisan worker/connection atau unhandled exceptions. Cek `server_err.log`.
+- Latency tinggi: periksa penggunaan CPU (model inference mungkin bottleneck). Solusi: batching, memindahkan inference ke worker terpisah, atau skalakan server (multiple instances/load balancer).
+
+6) File & tool yang saya tambahkan
+- `load_tests/locustfile.py` — contoh script Locust untuk POST `/predict`.
+
+Kalau mau, saya siap bantu menjalankan uji ini bersama (saya pandu perintah di terminal Anda), atau scaffold runner otomatis di cloud (mis. k6 script + GitHub Actions) untuk uji berkelanjutan.

pytest.ini

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+[pytest]
+markers =
+    integration: mark a test as an integration test (requires services)
+testpaths = tests

requirements.txt

@@ -1,3 +1,11 @@
-altair
-pandas
-streamlit
+python-dotenv
+flask
+flask-cors
+gunicorn
+
+numpy
+pandas
+joblib
+scikit-learn==1.6.1
+imbalanced-learn
+requests

server_err.log

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+WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
+ * Running on http://127.0.0.1:5000
+Press CTRL+C to quit
+ * Restarting with stat
+ * Debugger is active!
+ * Debugger PIN: 362-990-034
+127.0.0.1 - - [16/Dec/2025 01:27:49] "POST /predict HTTP/1.1" 200 -
+127.0.0.1 - - [16/Dec/2025 01:34:34] "GET / HTTP/1.1" 404 -
+127.0.0.1 - - [16/Dec/2025 01:34:36] "GET /favicon.ico HTTP/1.1" 404 -
+ * Detected change in 'C:\\Users\\CINDY\\OneDrive\\Desktop\\fraud_detection_api\\app\\model.py', reloading
+ * Restarting with stat
+ * Debugger is active!
+ * Debugger PIN: 362-990-034
+127.0.0.1 - - [16/Dec/2025 01:45:01] "POST /api/predict HTTP/1.1" 404 -
+127.0.0.1 - - [16/Dec/2025 01:45:15] "GET / HTTP/1.1" 404 -
+127.0.0.1 - - [16/Dec/2025 01:46:04] "POST /predict HTTP/1.1" 500 -
+127.0.0.1 - - [16/Dec/2025 01:47:03] "POST /predict HTTP/1.1" 500 -
+127.0.0.1 - - [16/Dec/2025 01:47:28] "POST /predict HTTP/1.1" 500 -
+127.0.0.1 - - [16/Dec/2025 01:48:59] "POST /predict HTTP/1.1" 200 -
+ * Detected change in 'C:\\Users\\CINDY\\OneDrive\\Desktop\\fraud_detection_api\\scripts\\run_integration_debug.py', reloading
+ * Restarting with stat
+ * Debugger is active!
+ * Debugger PIN: 362-990-034
+ * Detected change in 'C:\\Users\\CINDY\\OneDrive\\Desktop\\fraud_detection_api\\tests\\test_api.py', reloading
+ * Restarting with stat
+ * Debugger is active!
+ * Debugger PIN: 362-990-034
+ * Detected change in 'C:\\Users\\CINDY\\OneDrive\\Desktop\\fraud_detection_api\\app.py', reloading
+ * Restarting with stat
+ * Debugger is active!
+ * Debugger PIN: 362-990-034
+ * Detected change in 'C:\\Users\\CINDY\\OneDrive\\Desktop\\fraud_detection_api\\app\\__init__.py', reloading
+ * Restarting with stat
+ * Debugger is active!
+ * Debugger PIN: 362-990-034
+ * Detected change in 'C:\\Users\\CINDY\\OneDrive\\Desktop\\fraud_detection_api\\app\\routes.py', reloading
+ * Restarting with stat
+ * Debugger is active!
+ * Debugger PIN: 362-990-034

server_err_pwa.log

@@ -0,0 +1,23 @@
+::1 - - [16/Dec/2025 17:47:47] "GET /manifest.json HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 17:47:55] "GET /sw.js HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:02:21] "GET /fraud_detection_frontend.html HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:02:22] "GET /manifest.json HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:02:22] "GET /icons/icon.svg HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:16:13] "GET / HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:16:14] code 404, message File not found
+::1 - - [16/Dec/2025 18:16:14] "GET /favicon.ico HTTP/1.1" 404 -
+::1 - - [16/Dec/2025 18:16:19] "GET / HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:16:21] "GET / HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:16:57] "GET /manifest.json HTTP/1.1" 304 -
+::1 - - [16/Dec/2025 18:16:57] "GET /icons/icon.svg HTTP/1.1" 304 -
+::1 - - [16/Dec/2025 18:20:52] "GET / HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:21:25] "GET / HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:21:41] "GET / HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:21:44] "GET / HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:21:52] "GET / HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:23:17] "GET / HTTP/1.1" 200 -
+::1 - - [16/Dec/2025 18:23:17] "GET /manifest.json HTTP/1.1" 304 -
+::1 - - [16/Dec/2025 18:23:17] "GET /icons/icon.svg HTTP/1.1" 304 -
+::1 - - [16/Dec/2025 18:31:25] "GET / HTTP/1.1" 304 -
+::1 - - [16/Dec/2025 18:31:25] "GET /manifest.json HTTP/1.1" 304 -
+::1 - - [16/Dec/2025 18:31:25] "GET /icons/icon.svg HTTP/1.1" 304 -

server_out.log

@@ -0,0 +1,2 @@
+ * Serving Flask app 'app'
+ * Debug mode: on