NetScript

A lightweight Python utility for running and monitoring scripts (.py, .sh, or any shell command) from a live web dashboard. Built on Flask with no external dependencies beyond the standard library and Flask itself.

features

• start and stop scripts from a browser UI
• live log streaming via server-sent events — no page reloads
• per-line log colorization (errors, warnings, info)
• status polling every 2 seconds with in-place card updates
• clean process teardown: SIGTERM with a 5s timeout, SIGKILL fallback
• works with Python scripts, shell scripts, or any arbitrary command

install

pip install netscript

usage

from pathlib import Path
from process_manager import ProcessManager, ScriptSpec

scripts = {
    "pipeline": ScriptSpec(
        Path("jobs/data_pipeline.py"),
        ["python", "jobs/data_pipeline.py"]
    ),
    "server": ScriptSpec(
        Path("jobs/api_server.py"),
        ["python", "jobs/api_server.py", "--port", "8080"]
    ),
    "backup": ScriptSpec(
        Path("jobs/backup.sh"),
        ["bash", "jobs/backup.sh"]
    ),
}

manager = ProcessManager(job_root=Path("jobs"), scripts=scripts)
app = manager.create_flask_app()

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

Then open http://localhost:5000 in your browser.

dynamic commands

If the command needs to be constructed at runtime (e.g. pulling config from a file or environment), pass a callable instead of a list:

import os

def build_cmd():
    return ["python", "worker.py", "--env", os.environ["APP_ENV"]]

scripts = {
    "worker": ScriptSpec(Path("worker.py"), build_cmd)
}

The callable is invoked each time the script is started.

log files

Each script writes stdout and stderr to a .log file next to the script file. For a script at jobs/data_pipeline.py, the log is written to jobs/data_pipeline.log. Logs persist across restarts and can be tailed directly from the terminal:

tail -f jobs/data_pipeline.log

api endpoints

The Flask app exposes a small REST API alongside the dashboard UI.

Example status response:

{
  "pipeline": { "label": "data_pipeline.py", "status": "running", "pid": 12345 },
  "server":   { "label": "api_server.py",    "status": "stopped", "pid": null }
}

notes

• processes that daemonize themselves (fork and detach) will not be tracked correctly — this tool is intended for foreground processes
• the dashboard does not persist state across server restarts; any running processes will be orphaned if the Flask server exits
• for always-on services, consider systemd instead; this tool is best suited for on-demand or development workloads