HabitSphere – Habit Tracking and Analytics System

HabitSphere is a full-stack habit tracking application for creating routines, recording daily progress, reviewing consistency, and generating data-driven reports. It is built as a responsive single-page application (SPA) with a framework-free Python JSON API and a MySQL database.

Key features

• Secure user registration, login, logout, and server-side sessions
• Create, view, edit, delete, search, filter, activate, and deactivate habits
• Daily habit tracking with completion counts, notes, target-aware completion, and a date selector
• Dashboard with active habits, progress, streaks, weekly activity, and current calendar week/month goals
• Habit analytics: completion percentage, current streak, longest streak, consistency score, success rate, and missed days
• Weekly and monthly Insight views
• Downloadable CSV and TXT reports generated from MySQL data
• Matplotlib bar, pie, weekly-progress, and monthly-trend charts
• JSON-managed application preferences: theme, default section, weekly/monthly goals, and export preference
• Logging and validation for important application operations

Technology stack

Layer	Technologies
Frontend	HTML, CSS, JavaScript
Backend	Python with the built-in http.server and a custom JSON API handler
Database	MySQL with mysql-connector-python
Analytics	Pandas, Python date/time handling
Visualisation	Matplotlib
Settings	JSON

No Flask, Django, React, Bootstrap, Tailwind CSS, or other web framework is used.

Project architecture

Browser SPA (index.html + static/css/style.css + static/js/app.js)
        │ fetch() JSON requests
        ▼
Python built-in HTTP server (app.py)
        ▼
Business logic, validation, analytics, reports, charts (habit_tracker.py)
        ▼
MySQL database + JSON settings + generated reports/charts/logs

The frontend remains on one HTML page. JavaScript shows and hides SPA sections without full page navigation.

MySQL database overview

The database name is habit_tracker. The schema in database/schema.sql contains these tables:

Table	Purpose
USERS	Registered account details and password hashes
HABITS	User habits, categories, goals, targets, start dates, and statuses
HABIT_COMPLETION	Dated completion counts and notes
HABIT_ANALYTICS	Persisted habit performance metrics
IMPROVEMENT_TIPS	Reserved by the approved schema for rule-based suggestions

ER diagram

USERS (user_id)
  └──< HABITS (habit_id, user_id)
         ├──< HABIT_COMPLETION (completion_id, habit_id)
         ├──< HABIT_ANALYTICS (analytics_id, habit_id)
         └──< IMPROVEMENT_TIPS (tip_id, habit_id)

USERS → HABITS and HABITS → child tables are enforced with foreign keys and cascade deletion rules in the schema.

Project folder structure

HabitSphere/
├── app.py                       # Framework-free HTTP server and JSON API routes
├── habit_tracker.py             # OOP services: DB, auth, habits, tracking, analytics, reports, charts
├── index.html                   # Single-page application markup
├── requirements.txt             # Pinned Python packages
├── database/
│   └── schema.sql               # MySQL database schema
├── json/
│   └── settings.json            # Application and database configuration
├── static/
│   ├── css/style.css            # Responsive SPA styling
│   ├── js/app.js                # SPA interaction and API requests
│   └── charts/                  # Matplotlib charts generated at runtime
├── logs/
│   └── app.log                  # Application logs
├── reports/
│   ├── csv/                     # Generated CSV reports
│   └── txt/                     # Generated TXT reports
└── venv/                        # Local virtual environment (created locally)

The current web application uses app.py, habit_tracker.py, MySQL, and json/settings.json.

How the application works

1. A user registers and signs in through the SPA.
2. The Python server creates a secure, short-lived server-side session and sends an HttpOnly cookie.
3. Habits and completions are stored in MySQL through JSON API requests.
4. The dashboard retrieves live MySQL totals, today's active habits, calendar-week/month goals, and seven-day activity.
5. Analytics calculate performance values from real completion records.
6. Reports and charts are generated from those analytics and completion records.

Analytics features

• Habit completion percentage
• Current and longest daily streak
• Consistency score
• Success rate
• Missed days
• Most and least consistent habits
• Week (7-day) and month (30-day) Insight periods

Reports and visualisation

The Reports page generates:

• Daily, weekly, and monthly CSV reports
• Daily, weekly, and monthly TXT reports
• Habit completion bar chart
• Completed-versus-missed pie chart
• Seven-day progress line chart
• Six-month trend line chart

Generated files are stored in reports/ and static/charts/.

Installation requirements

• Python 3.13 or compatible Python 3 release
• MySQL Server running locally
• A MySQL account permitted to create/use the habit_tracker database

Virtual environment setup

From the project root in PowerShell:

python -m venv venv
.\venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
pip install -r requirements.txt

If PowerShell blocks activation for the current terminal session:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned
.\venv\Scripts\Activate.ps1

MySQL database setup

1. Start your local MySQL Server.
2. Update the database object in json/settings.json with your MySQL host, port, username, password, and database name.
3. Initialise the schema once:

python -c "from habit_tracker import DatabaseManager; DatabaseManager().initialize_schema()"

The supplied schema creates the habit_tracker database and the required tables when they do not already exist.

Configure settings

Two settings areas are available:

• json/settings.json contains database/server configuration and default application preferences.
• The in-app Settings SPA page updates theme, default section, weekly goal, monthly goal, and default report export format immediately.

For local development, the default server address is configured as 127.0.0.1:8000.

Run the application

Activate the virtual environment, then run:

python app.py

Open the application in a browser:

http://127.0.0.1:8000

Keep the terminal open while using the application. Press Ctrl + C to stop the server.

Security considerations

• Passwords are stored with PBKDF2-SHA256 hashing and a unique random salt.
• Passwords are not stored as plain text.
• Sessions use random server-side tokens with an expiry period.
• Session cookies are sent with HttpOnly and SameSite=Lax attributes.
• API operations require an authenticated session where applicable.
• SQL parameters are passed through mysql-connector-python parameterised queries.

For production deployment, move database credentials out of settings.json, use HTTPS, set Secure cookies, and use persistent session storage.

Known limitations

• Sessions are held in server memory; restarting the server logs users out.
• JSON preferences are application-wide rather than per-user.
• The approved database schema stores completion dates but does not include a separate completion-time column.
• The project is configured for local development and does not include production deployment configuration.

Future enhancements

• Per-user preference storage
• Persistent session storage
• Scheduled reminders
• Rule-based improvement-tip generation using IMPROVEMENT_TIPS
• Calendar month view and historical completion heat map
• Account password reset workflow
• Production configuration with environment-based secrets and HTTPS

Author

SHAIK AKHIL AHMED

HabitSphere was developed as a Python full-stack habit tracking and analytics project.