Habit Tracker (Python/Flet)

Designed and Created by Bahamut 0 - Ernest Hardison

A robust, high-performance habit tracking application built with Python and Flet. Optimized for desktop and mobile use, featuring fast local storage (SQLite), detailed habit tracking, and user personalization.

🗺️ Architecture Map

This diagram illustrates how the application components interact.

graph TD
    User((User))
    
    subgraph "Entry Point"
        Main[main.py]
    end

    subgraph "Views (UI Screens)"
        Home[views/home_view.py]
        Add[views/add_habit_view.py]
        Settings[views/settings_view.py]
        Profile[views/profile_view.py]
        Help[views/help_view.py]
    end

    subgraph "Components"
        HabitRow[components/habit_item.py]
    end

    subgraph "Data Layer"
        Models[models.py]
        DB[database.py]
        SQLite[(SQLite DB)]
    end

    %% Flows
    User --> Main
    Main --> Home
    
    %% Navigation
    Home -->|Click Add| Add
    Home -->|Click Settings| Settings
    Settings -->|Nav| Profile
    Settings -->|Nav| Help
    
    %% Data Flow
    Home -->|Reads/Writes| DB
    Add -->|Writes| DB
    Profile -->|Reads/Writes| DB
    Home -->|Uses| Models
    
    %% Component Usage
    Home -->|Renders List of| HabitRow
    HabitRow -->|Updates Progress| DB
    
    DB -->|Persists| SQLite

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📂 File Summaries & Functionality

Core Application

• main.py
  • Summary: The entry point of the application.
  • Functionality: Initializes the Flet page, sets the theme/title, and manages the routing logic (navigation) between different screens (/, /add, /settings, etc.).

Data Layer

• database.py
  • Summary: The persistence layer managing the SQLite database.
  • Functionality:
    • Creates tables (habits, logs, user_profile) on startup.
    • Handles all SQL queries: add_habit, log_habit, get_profile, etc.
    • Optimized with indexes for fast performance (verified < 2ms).
• models.py
  • Summary: Data definitions.
  • Functionality: Defines the Habit dataclass to ensure consistent data structures when passing habit information between the Database and the UI.

Views (Screens)

• views/home_view.py
  • Summary: The main dashboard.
  • Functionality:
    • Displays the list of habits.
    • Calculates and shows daily progress.
    • Provides access to "Add Habit" and "Settings".
    • Handles the "Exit" button logic.
• views/add_habit_view.py
  • Summary: Creation form.
  • Functionality:
    • Form to input Name, Type (Simple/Quantitative), Goal, and Dates.
    • Common Habits: Quick-add buttons for Water, Gym, etc.
• views/settings_view.py
  • Summary: Central settings hub.
  • Functionality: Menu navigation to Profile, Help, Updates, and (Stub) Auto Restore.
• views/profile_view.py
  • Summary: Personalization screen.
  • Functionality: Form to view and edit User Name, Birthdate, Phone, Email, and Goals.
• views/help_view.py
  • Summary: Educational screen.
  • Functionality: Static guide explaining how to use the app features.

Components

• components/habit_item.py
  • Summary: A reusable UI widget for a single habit.
  • Functionality:
    • Renders differently based on type (Checkbox for Simple vs Progress Bar for Quantitative).
    • Handles local click events (toggle/increment) and notifies the parent view to update the database.

Utilities

• perf_test.py
  • Summary: Performance verification script.
  • Functionality: Generates 1 year of dummy data (7,300+ logs) and measures query speed to ensure the app meets the < 2s requirement.
• CHANGELOG.md
  • Summary: Application history.
  • Functionality: Tracks all changes, new features, and bug fixes version by version.

🚀 How It Works

1. Startup: executing main.py starts the Flet app. database.py ensures the file habits.db exists.
2. Navigation: The app uses URL-based routing (e.g., page.go("/add")). main.py listens for route changes and swaps the visible view.
3. Data Flow:
   • Reads: Views call db.get_all_habits() or db.get_profile().
   • Writes: User actions (clicking checkmarks, saving forms) call methods like db.log_habit() or db.add_habit().
4. Performance: The app is architected with a "local-first" SQLite approach, ensuring it remains instant even with years of data.