learning-tracker-app 1.0.0

A graphical tool to track, visualize, and analyze your learning progress.

📈 Learning Progress Tracker

A premium, interactive desktop GUI application built in Python (using standard Tkinter) to map, visualize, track, and analyze your learning progress.

Create custom question-concept networks, track when you master items, see incremental snapshots of your growth, and automatically export gorgeous, interactive analytics dashboards.

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✨ Key Features

• 🧠 Interactive Mind Mapping: Drag and drop nodes, double-click to view details, and draw directed learning dependencies dynamically.
• 🎨 State-of-the-Art Visuals:
  • Difficulty-based node shapes (Circular for Easy, Rectangular for Medium, Triangular for Hard, Diamond for Challenging, and Star-shaped for Extreme).
  • Snapshot-based heatmaps (automatically ranges node colors from light red to light blue depending on when you created them relative to your learning journey).
  • Visual cues like bold checkmark badges on mastered nodes.
• ⏱️ Professional History Engine: Full multi-level Undo (Ctrl+Z) and Redo (Ctrl+Y) operations for all layout, connection, and data updates.
• 📊 Premium Interactive Dashboard: Generates Plotly-powered charts:
  • Pie chart of answered vs unanswered questions (overall and snapshot-specific).
  • Bar chart timeline of concept creation trends.
  • Scatter-line chart mapping node connectivity (in-degree and out-degree analysis).
  • Fully responsive HTML sidebar-driven multi-dashboard uniting all analytics.
• 📂 Clean File Persistence: Custom JSON save/load system allowing multiple distinct learning graphs.

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📂 Project Directory Structure

The application has been restructured using the modern Python src/ layout recommended for robust, conflict-free packaging and publishing:

LearningTrackerApp/
├── src/
│   └── learning_tracker/
│       ├── __init__.py           # Package exports & metadata
│       ├── main.py               # Application launcher and logger init
│       ├── models.py             # Dataclasses (QuestionNode, LearningGraph)
│       ├── storage.py            # JSON I/O and export directories
│       ├── utils.py              # Subgraph traversals & timestamp binning
│       ├── statistics_engine.py  # Plotly dashboards HTML generator
│       ├── gui_main.py           # Main window & toolbars orchestration
│       ├── gui_canvas.py         # Custom canvas with drag, shift-drag cues
│       └── gui_popups.py         # Popups for node details creation/edits
├── pyproject.toml                # Modern PEP 621 packaging metadata
├── LICENSE                       # MIT License
└── README.md                     # Comprehensive product guide

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🚀 Installation

You can install the package directly using standard pip tools once published.

Core GUI Only

To keep dependencies extremely light (GUI runs entirely on built-in standard library tools), run:

pip install learning-tracker-app

Full Analytical Dashboard Support (Recommended)

To enable generating interactive Plotly dashboards and data reports, install with the stats extras:

pip install learning-tracker-app[stats]

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🎮 Running the Application

After installing, run the app directly from your terminal using the custom CLI entry point:

learning-tracker

Alternatively, you can run the package module:

python -m learning_tracker

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🖱️ Quick Controls Cheat Sheet

Interaction	Action
Right-Click on empty canvas	Create a new standalone node
Shift + Left-Click + Drag from a node to empty canvas	Create a new node and draw a connection to it instantly
Shift + Left-Click + Drag from a node to another node	Draw a directed edge (learning dependency)
Left-Click on a node	Open detail editor (view dates, edit question/answers, set difficulty, mark completed)
Left-Click + Drag on a node	Reposition node on the canvas smoothly
Ctrl + Z / Ctrl + Y	Undo / Redo any action

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📦 Publishing to PyPI

Here is the quick guide to build and upload your package. Make sure you have build and twine installed:

pip install --upgrade build twine

1. Build the Distribution

Run the build script from the project root folder (where pyproject.toml resides):

python -m build

This generates .tar.gz (source distribution) and .whl (built distribution) packages in the dist/ directory.

2. Verify Your Build

Ensure package descriptions are formatted correctly and metadata matches PyPI rules:

twine check dist/*

3. Upload to TestPyPI (Recommended first step)

Verify everything looks correct on the test repository:

twine upload --repository testpypi dist/*

4. Upload to Production PyPI

Publish your app to the world!

twine upload dist/*

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📄 License

Distributed under the MIT License. See LICENSE for more details.