Gig Worker Earnings Tracker

A lightweight, AI-assisted(Vibe Coded) Flask web app for delivery analytics — built because spreadsheets were slowing me down.

Overview

When DoorDash became my side-gig, I tracked everything in Google Sheets.
But the formulas, sheets, and manual updates became chaotic… so one evening, out of boredom (and curiosity), I challenged myself:

“Can I replace the entire spreadsheet workflow with a clean local web app?”

Three nights later, I shipped DoorDash Earnings Tracker — a fully functioning Flask + SQLite analytics system, designed to log, analyze, visualize, and export earnings and expenses with zero cloud dependency.

Why I Built It

• I needed faster weekly insights without scrolling through formulas.

• I wanted beautiful charts that Sheets didn’t give me.

• I wanted local-only data: no cloud, no account, no risk.

• I wanted to experiment with a production-style Flask project, including authentication, CSRF, modals, systemd deployment, etc.

• And mostly…
  I was bored and wanted to see how fast I could ship a real app using my AI-assisted coding workflow.

Core Features

1. Dynamic Dashboard

The homepage shows everything instantly with clean KPIs and smart presets.

Date Presets: Week | Month | YTD | All-time
KPIs: Total earnings, Avg/hr, Avg per delivery, Expenses, Net, etc.
Visuals powered by Chart.js:

• Weekly earnings

• Earnings over time

• Rolling 4-week trend

• Category breakdown

• Delivery vs. hours ratio

2. Weekly Earnings Management

All weekly logs are editable via modal forms with full CRUD:

• Week number, start/end date

• Hours worked

• Deliveries count

• DoorDash Pay + Tips + Other Pay

• Out-of-pocket expenses

• Notes

• Soft-delete & restore support

• Filters, sorting, and pagination

3. Expense Tracking System

Separate tables for:

• Categories

• Individual expenses (with date, amount, notes)

• Optional receipt field

• Search, category filter, pagination

4. Analytics, Export & Backup Tools

• CSV Export: weekly.csv, expenses.csv, summary.csv

• Automatic backups to /backups

• Restore from UI (admin-only)

• Data import via import_data.py with:

  • duplicate detection

  • dry-run mode

  • truncate mode

5. Security & Local Auth

• PIN lock with hashing

• Session timeout

• CSRF tokens

• Local-only storage (SQLite)

• No internet dependency

Architecture

Backend: Flask + SQLAlchemy
Database: SQLite
Frontend: HTML, Jinja2, Tailwind-style minimal CSS, Chart.js
Auth: Local PIN hash + session timeout
Packaging: Bash scripts + systemd service template
Config: JSON-driven (currency symbol, date range presets, PIN hash)

File Structure

app.py                # routes, models, auth, csrf, exports
templates/
    base.html
    index.html        # dashboard
    weekly.html
    expenses.html
    admin.html
    login.html
static/
    script.js         # charts, filters, modals, theme toggle
    style.css
import_data.py        # CSV importer
config.json
dist/                 # packaged tarball

Screenshots

What I Learned

This wasn’t just a “replace my spreadsheet” project. It became a small production-grade system:

• Designing API endpoints for chart JSON

• Implementing a real CRUD experience with modals

• Managing soft deletes

• Packaging with bash automation

• Building CSV export logic correctly

• Handling CSRF in Flask

• Writing custom import scripts

• Turning quick ideas into polished tools

The “AI Vibe Coding” Workflow

This project was intentionally built fast using an experimental workflow using Codex and My Curiosity:

1. I described the app at a high level.

2. AI generated the first version of each module.

3. I refined the architecture and debugged local behavior.

4. Together, we iterated through UI, chart logic, and packaging.

5. Shipped from idea → working app in a very short cycle.

That speed and creativity became part of the fun.

Conclusion

DoorDash Earnings Tracker is a small but meaningful example of how quickly a well-structured idea can turn into a working product — especially when pairing human creativity with AI-accelerated development.

It replaced spreadsheets that I use completely.
It gave me cleaner insights.
And it proved to me that I can ship real tools, fast, while working on networking, labs, and everything else in my busy routine.