Drop in a CSV, write one config file, and get a live-updating Web UI + REST API. FastAPI-based data monitoring for shop floors and small teams.

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  • Tags dashboard , monitoring , csv , fastapi , andon , spc , manufacturing
  • Requires: Python >=3.10
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Project description

Monitor App 🚀

PyPI version Python versions License: MIT

Drop in a CSV, write one config file, and get a live-updating Web UI + REST API.

Monitor App is a FastAPI-based data monitoring tool designed for shop floors, labs, and small teams. Define your tables and views declaratively in a single config.py (validated by Pydantic at startup), and Monitor App serves a real-time dashboard, full CRUD REST API with Swagger docs, threshold alerts, and an Andon-style wallboard — no frontend build, no Node.js, fully offline-capable.

日本語のドキュメントは README_ja.md を参照してください。

✨ Features

  • CSV → Web app in 3 commands — place CSV files, run the server, see your data
  • Type-safe declarative config — tables, views, styles, alerts in one Pydantic-validated config.py; typos are caught at startup with friendly messages (monitor-app check)
  • Live-updating views — Server-Sent Events push changes to the browser; polling fallback
  • Full REST API — CRUD per table, bulk ingest, automatic Swagger UI at /docs
  • SQLite / MySQL / PostgreSQL — switch with a single DATABASE_URL environment variable
  • Conditional cell styling — color-code values by thresholds, no code required
  • No frontend toolchain — vanilla JS + bundled assets; works on offline factory networks

🏭 Built for the shop floor (v2.1)

Feature What it does How to enable
Auto data ingest Watches csv/ for changes; POST /api/ingest/{table} for sensors/PLC/MES MONITOR_INGEST_WATCH / always on
Threshold alerts On-screen banner + browser notification, Webhook (Slack/Teams), e-mail; edge-triggered alerts=[AlertRule(...)]
Andon wallboard Full-screen rotating views with a green/amber/red status light at /kiosk kiosk=KioskConfig(...)
Trend / SPC charts Line & bar charts with UCL/LCL/target lines, out-of-control points highlighted ViewDef(chart=ChartDef(...))
Dashboard Home page shows mini-chart grid (columns=1–4) for chart-enabled views; non-chart views listed below dashboard=DashboardConfig(columns=2)
Group navigation Sidebar and view list support named groups; ViewDef(group="...") ViewDef(group=...)
Dark theme Toggle between light and dark; persisted in localStorage["monitor-theme"]; <html data-theme="dark"> theme-toggle button (☀️/🌙)
KPI cards Scalar SQL (counts, rates, OEE) as color-coded cards on the home page kpis={...}
Operator entry forms Touch-friendly forms generated from your table schema at /form/{table} TableDef(form=FormDef(...))
Audit log Who changed what and when, for every write MONITOR_AUDIT_ENABLED
Export Download any view as CSV or Excel /api/views/{view}/export

🚀 Installation

pip install monitor-app

With Excel export support:

pip install "monitor-app[xlsx]"

🔧 Quick start

# 1. Create a project (config.py + sample CSVs, ready to run)
monitor-app startproject myproject
cd myproject

# 2. Import CSVs and start the server
monitor-app runserver --import-csv

# 3. Open http://127.0.0.1:9990  (Swagger UI at /docs)

CLI commands

Command Description
monitor-app startproject <name> Generate a project skeleton
monitor-app runserver [--import-csv] [--reload] [--host H] [--port P] Start the web server
monitor-app import-csv [--keep] Import csv/ into the database (--keep appends)
monitor-app check Validate config.py without starting the server

📝 Configuration

Everything lives in one config.py. Mistakes are reported at startup, not at runtime:

from monitor_app import (
    AlertRule, CellStyle, ChartDef, FormDef, KioskConfig,
    KpiCard, MonitorConfig, TableDef, Threshold, ViewDef,
)

config = MonitorConfig(
    app_title="Line Monitor",
    refresh_interval_ms=2000,
    # CRUD tables — column types are inferred (or written explicitly)
    tables={
        "measurements": TableDef(
            columns={"id": "int", "ts": "str", "temp": "float"},
            form=FormDef(labels={"temp": "Temperature"}),   # operator entry form
        ),
    },
    # Display views — any SELECT, including JOINs and aggregation
    views={
        "temp_trend": ViewDef(
            query="SELECT ts, temp FROM measurements ORDER BY id",
            title="Temperature trend",
            chart=ChartDef(type="line", x="ts", y="temp", ucl=80, lcl=20),
            styles={"temp": CellStyle(
                greater_than=Threshold(value=80, **{"class": "bg-danger text-white"}),
            )},
        ),
    },
    # Threshold alerts with notifications
    alerts=[
        AlertRule(view="temp_trend", column="temp", op=">", value=80,
                  level="critical", notify=["webhook"]),
    ],
    # KPI cards and Andon wallboard
    kpis={"count": KpiCard(title="Samples", query="SELECT COUNT(*) FROM measurements")},
    kiosk=KioskConfig(rotate_seconds=15, theme="dark"),
)

Environment-specific values (never in config.py) go in .env:

MONITOR_DATABASE_URL=postgresql+pg8000://user:pass@db:5432/monitor   # default: SQLite
MONITOR_API_KEY=change-me            # enables X-API-Key auth on writes
MONITOR_INGEST_WATCH=true            # auto-import csv/ on change
MONITOR_AUDIT_ENABLED=true           # record change history
MONITOR_WEBHOOK_URL=https://hooks.slack.com/services/...

🌐 Endpoints

Path Description
/ Home: dashboard with mini-charts, KPI cards, and view list
/table/{view} Live table / chart page
/kiosk Andon wallboard (full screen, auto-rotating)
/form/{table} Operator entry form
/docs, /redoc OpenAPI documentation
/api/tables/{table} CRUD (GET/POST/PUT/DELETE)
/api/ingest/{table} Bulk ingest for sensors/PLC
/api/views/{view} View data (+ /stream for SSE, /export for CSV/Excel)
/api/kpis, /api/alerts, /api/audit, /api/schema, /api/health KPIs, active alerts, audit log, schema, health

🔌 External tool integrations (v2.2)

Connect procdiag, netdiag, and network-checker to Monitor App with a few lines — no changes to the external tools required (read-only, pull-based).

from monitor_app.integrations import procdiag, netdiag, network_checker

config = procdiag.attach(config, db_path="/opt/procdiag/data/procdiag.db")
config = netdiag.attach(config)
config = network_checker.attach(config, hosts=["plc-01", "gateway"])

Each attach() call merges tables, views, KPIs, and alert rules into your existing config. Run monitor-app sync-sources to ingest on demand, or set MONITOR_INGEST_WATCH=1 for continuous polling.

Full reference: docs/integrations_v2_2.md

🔄 Migrating from v0.x

v2 was rebuilt on FastAPI + Pydantic. The legacy dict-style config.py (ALLOWED_TABLES / VIEW_TABLES / TABLE_CELL_STYLES) still loads through a compatibility shim, but new projects should use MonitorConfig. Key changes:

  • python app.py runserver → monitor-app runserver (the app no longer lives in your project)
  • DB settings (DB_TYPE, passwords) → MONITOR_DATABASE_URL environment variable
  • API paths: /api/<table> → /api/tables/{table}, /api/view/<v> → /api/views/{view}

Details: redesign notes · v2.1 feature design · v2.2 integrations · v2.3 UI redesign

🐳 Docker

docker build -t monitor-app -f docker/Dockerfile .
docker run -v $(pwd)/myproject:/project -p 9990:9990 monitor-app

🛠 Development

git clone https://github.com/mikawa-bushi/monitor-app.git
cd monitor-app
poetry install
poetry run pytest

Pre-push security & quality checks

A pre-commit config scans for secrets and enforces formatting locally, before code leaves your machine. Enable it once per clone:

poetry run pre-commit install          # installs the commit + pre-push hooks
poetry run pre-commit run --all-files  # optional: run across the repo now
  • gitleaks — blocks hard-coded secrets (PyPI/AWS/GCP tokens, GitHub PATs, private keys)
  • detect-private-key / check-added-large-files / check-merge-conflict, plus black / ruff (pinned to the CI versions)
  • check-commit-identity (pre-push) — rejects commits whose author/committer e-mail is off the allowlist, keeping work/other identities out of history (scripts/check_commit_identity.py; extend via the MONITOR_ALLOWED_EMAILS env var)
  • check-ai-traces (commit + pre-push) — flags traces of AI-agent tools (config dirs, file content, commit metadata) so they don't leak into the repo. Tool patterns are data-driven in scripts/check_ai_traces.py (extend the TOOLS list or MONITOR_AI_TRACE_EXTRA; ignore paths via .ai-trace-ignore)

Audit the whole working tree and full git history on demand:

python3 scripts/check_ai_traces.py --all   # exit 0 = no traces found

⚠️ Local hooks can be skipped with git commit/push --no-verify. For an unbypassable gate, also make gitleaks a required GitHub Actions check.

Contributions are welcome — see CONTRIBUTING.md.

📜 License

MIT

Project details

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  • License Expression: MIT
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  • Author: hardwork9047
  • Tags dashboard , monitoring , csv , fastapi , andon , spc , manufacturing
  • Requires: Python >=3.10
  • Provides-Extra: xlsx
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