| | --- |
| | license: mit |
| | --- |
| | Here's a comprehensive Hugging Face Model Card for your Interactive PyQt5 A* Algorithm Game: |
| |
|
| | ```markdown |
| | --- |
| | language: |
| | - en |
| | tags: |
| | - game |
| | - pathfinding |
| | - algorithm |
| | - a-star |
| | - pyqt5 |
| | - visualization |
| | - educational |
| | - interactive |
| | - python |
| | widget: |
| | - video_path: demo.mp4 |
| | example_title: A* Algorithm Demo |
| | --- |
| |
|
| | # Interactive A* Pathfinding Algorithm Game |
| |
|
| | ## Model Overview |
| |
|
| | An interactive educational game that visually demonstrates the A* pathfinding algorithm using PyQt5. This application provides a hands-on way to understand how one of the most popular pathfinding algorithms works through real-time visualization. |
| |
|
| | ## What is A* Algorithm? |
| |
|
| | A* (A-Star) is a graph traversal and path search algorithm that finds the shortest path between nodes. It combines the strengths of Dijkstra's Algorithm (guaranteed shortest path) and Greedy Best-First Search (efficiency) by using both: |
| |
|
| | - **g(n)**: Actual cost from start node to current node |
| | - **h(n)**: Heuristic estimated cost from current node to goal |
| | - **f(n) = g(n) + h(n)**: Total estimated cost |
| |
|
| | ## Features |
| |
|
| | ### 🎮 Interactive Gameplay |
| | - Set custom start and end positions |
| | - Place and remove obstacles in real-time |
| | - Visualize algorithm execution step-by-step |
| | - Adjustable animation speed |
| |
|
| | ### 🎨 Visual Elements |
| | - **Green**: Start node |
| | - **Red**: End node |
| | - **Gray**: Obstacles/walls |
| | - **Yellow**: Open set (nodes being considered) |
| | - **Light Red**: Closed set (evaluated nodes) |
| | - **Blue**: Final optimal path |
| |
|
| | ### ⚙️ Technical Features |
| | - 20×20 grid system with 8-directional movement |
| | - Real-time cost display (g, h, f values) |
| | - Manhattan distance heuristic |
| | - Pause/Resume functionality |
| | - Reset and clear options |
| |
|
| | ## Quick Start |
| |
|
| | ### Installation |
| | ```bash |
| | pip install PyQt5 |
| | ``` |
| |
|
| | ### Run the Game |
| | ```bash |
| | python astar_game.py |
| | ``` |
| |
|
| | ### Usage Instructions |
| | 1. Select "Start Point" mode and click a grid cell to set start position |
| | 2. Select "End Point" mode and set destination |
| | 3. Use "Obstacles" mode to add walls |
| | 4. Click "Start" to run the algorithm |
| | 5. Watch the visualization and learn! |
| |
|
| | ## Educational Value |
| |
|
| | This game is perfect for: |
| | - 🎓 Computer Science students learning algorithms |
| | - 🎮 Game developers implementing pathfinding |
| | - 🔍 Anyone curious about how navigation algorithms work |
| | - 💡 Understanding heuristic search methods |
| |
|
| | ## Algorithm Details |
| |
|
| | ### Heuristic Function |
| | Uses Manhattan distance: `h(n) = |x₁ - x₂| + |y₁ - y₂|` |
| |
|
| | ### Cost Calculation |
| | - **Movement Cost**: Euclidean distance between nodes |
| | - **Total Cost**: f(n) = g(n) + h(n) |
| | - **Node Expansion**: Always expands node with lowest f(n) first |
| |
|
| | ### Key Properties |
| | - **Complete**: Always finds a solution if one exists |
| | - **Optimal**: Always finds the shortest path |
| | - **Efficient**: Explores fewer nodes than Dijkstra's algorithm |
| |
|
| | ## File Structure |
| | ``` |
| | astar-game/ |
| | ├── astar_game.py # Main game implementation |
| | ├── requirements.txt # Dependencies |
| | ├── README.md # This file |
| | └── demo.mp4 # Demonstration video |
| | ``` |
| |
|
| | ## Requirements |
| |
|
| | ```txt |
| | PyQt5>=5.15.0 |
| | ``` |
| |
|
| | ## Compatibility |
| |
|
| | - **Python**: 3.6+ |
| | - **OS**: Windows, macOS, Linux |
| | - **Dependencies**: PyQt5 only |
| |
|
| | ## Try It Yourself! |
| |
|
| | ### Example Maze Challenge: |
| | ``` |
| | S = Start, E = End, # = Obstacle |
| | |
| | S . . # . . . . . . |
| | . # . # . # # # . . |
| | . # . . . # . . . . |
| | . # # # . # . # # . |
| | . . . # . # . # . . |
| | # # . # . # . # . . |
| | . . . # . . . # . . |
| | . # # # # # # # . . |
| | . . . . . . . . . E |
| | ``` |
| |
|
| | Can you predict the path the algorithm will find? |
| |
|
| | ## Contributing |
| |
|
| | Feel free to contribute by: |
| | - Adding different heuristic functions |
| | - Implementing other pathfinding algorithms |
| | - Improving the UI/UX |
| | - Adding level challenges |
| |
|
| | ## License |
| |
|
| | This project is open source and available under the MIT License. |
| |
|
| | ## Citation |
| |
|
| | If you use this in an educational setting or project, please credit: |
| |
|
| | ```bibtex |
| | @software{astar_pyqt_game, |
| | title = {Interactive A* Pathfinding Algorithm Game}, |
| | author = {Your Name}, |
| | year = {2024}, |
| | url = {https://huggingface.co/your-username/astar-game} |
| | } |
| | ``` |
| |
|
| | ## Related Algorithms |
| |
|
| | - Dijkstra's Algorithm |
| | - Breadth-First Search (BFS) |
| | - Depth-First Search (DFS) |
| | - Greedy Best-First Search |
| | - Jump Point Search |
| |
|
| | --- |
| |
|
| | **Happy pathfinding!** 🚀 |
| | ``` |
| | |
| | This model card includes all the essential sections that Hugging Face expects: |
| | |
| | 1. **Metadata** (language, tags, widget) |
| | 2. **Overview** and algorithm explanation |
| | 3. **Features** and technical details |
| | 4. **Installation** and usage instructions |
| | 5. **Educational value** |
| | 6. **Technical specifications** |
| | 7. **Compatibility** information |
| | 8. **Interactive examples** |
| | 9. **Citation** template |
| | 10. **Related content** |
| | |
| | The card is structured to be both informative for technical users and accessible for learners new to pathfinding algorithms. You can save this as `README.md` in your Hugging Face model repository. |
