UCSD Makerspace Check-In System

Welcome to the UCSD Makerspace.
This repository contains the code for the RFID-based check-in station deployed at the makerspace. Students check in by tapping their UCSD ID at the station, which verifies their account and waiver status and logs their activity.

How it Works

The check-in station is a Python-based application with a main event loop running on its own thread. It integrates with several components to verify and log student check-ins:

• Traffic Light: A USB-connected device that shows green, yellow, or red lights indicating the check-in status.
• Local User Database: A cached JSON file storing known student records for quick lookup.
• Online User Database: The authoritative source of student records, accessed via Google Sheets.
• Online Waiver Database: A separate Google Sheet tracking which students have signed the required waiver.

1. Card Scan

The student taps their UCSD ID, and the RFID tag is read and queried to our local database.

2. Determine Student Status

• If the student is found locally:

  • If waiver is already signed locally: mark check-in as successful, light turns green, and activity is logged.
  • If no waiver is marked locally: query the waiver database online.
    • If waiver is found online: update the local record and proceed as above.
    • If waiver is still not found: light turns yellow and a QR code for waiver signature is displayed.

• If the student is not found locally:

  • If the student is found online: add to the local database and follow the same waiver checks as above.
  • If the student is not found online: begin the account creation workflow.

3. Account Creation Workflow

If no account is found locally or online:

• The system displays a screen informing the student that no account exists (NoAccNoWaiver.py).
• After a short delay, the student is prompted (NoAccNoWaiverSwipe.py) to scan the barcode on their ID or to enter their information manually.
• If barcode scan fails, the student can use the manual fill form (ManualFill.py) to enter their name, PID, and email.
• Once submitted, a new account is created, added to both the local and online databases, provisioned in Fabman.io, and the student is checked in. A confirmation screen (UserThank.py) is then shown.

4. Log Activity

All successful check-ins are enqueued and written asynchronously to the Google Sheets activity log. This avoids delays from network or API latency and deduplicates repeated scans within a short time window.

Status Indicators

Color	Meaning
Green	Check-in successful
Yellow	Waiver not yet signed
Red	Account not found

Features

Waiver Management

Automatically verifies waiver status locally and online. Updates the local database when waiver is found online.

Local and Online Sync

Uses a local JSON database for fast lookups. Refreshes records periodically or when missing information.

Fabman.io Integration

Each machine in the makerspace is controlled by Fabman.io. Students are provisioned Fabman accounts and access tokens via the fabman.py API client after completing required trainings or attending mandatory workshops. Non-technical staff members are able to use our Google Sheets database to grant access to students via Google Sheets checkboxes (enabled by Google Sheets scripts).

Asynchronous Logging Queue

All check-ins are enqueued and written asynchronously to the Google Sheets activity log. This prevents blocking the main loop and deduplicates repeated scans within a short time window, ensuring smooth operation even with network latency.

Error Handling

Displays errors and disables check-in if network connectivity or database access fails.

Project Structure

This repository is organized into the following main components:

• main.py
  Application entry point. Initializes the RFID reader, traffic light, GUI, and starts the main loop thread.

• core/handle_check_in.py
  Core check-in logic: verifies student account, waiver status, updates local database, and logs check-ins.

• core/checkin_queue.py
  Implements an asynchronous queue for logging check-ins to Google Sheets, to avoid blocking the main thread.

• core/UserRecord.py Class with various information extraction methods, student verifications, and helper functions to assist handling a check-in.

• core/new_row_check_in.py
  Handles activity logging and displaying the appropriate GUI status pages based on the check-in result.

• utils.py
  Utility functions for validation, waiver matching, timestamp formatting, and account creation workflow.

• fabman.py
  Client for interacting with the Fabman.io API to provision accounts and grant machine access.

• gui/
  Tkinter-based user interface, including:

  • AccNoWaiver.py, AccNoWaiverSwipe.py – Pages for users missing a waiver
  • NoAccNoWaiver.py, NoAccNoWaiverSwipe.py – Pages for users with no account
  • CheckInNoId.py – Allows manual check-in with PID
  • ManualFill.py – Manual account creation form
  • UserWelcome.py, UserThank.py – Success confirmation pages
  • traffic.py – Controls the USB-connected traffic light

• export_user_db.py
  Utility script to export the online student database to the local machine.

• get_info_from_pid.py
  Queries UCSD APIs for student information by PID.

Development

Requirements

• Python 3.8 or higher
• Google Sheets API credentials (configured in global_.sheets)
• Fabman API token saved in fabtoken.txt

Privacy and Credentials

This repository depends on several sensitive files and credentials that are not included in version control for privacy and security:

• assets/local_user_db.json
  Contains the local cache of student records. This is generated automatically at runtime or by running export_user_db.py and should not be manually edited.

• fabtoken.txt
  Contains the API token for Fabman.io. This file must be created manually on the deployment machine with your Fabman API key.

• Google Sheets API credentials
  Managed separately via OAuth and stored securely on the deployment machine.

These files are excluded from the repository via .gitignore and must be provisioned when setting up a new check-in station.

Running

First, make sure to install the required packages with pip install -r requirements.txt in the main directory

On a deployed machine, you can use the provided helper script to run the system and log output:

./run

# or:

python3 main.py [-v]