WordleSolver

An interactive Wordle solver built with Python and NiceGUI.
You can play Wordle in the browser and let different AI search strategies (BFS, DFS, Entropy-based, CSP-based) automatically solve the puzzle.

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1. Requirements

• Python: 3.9+ (project was tested with Python 3.9)
• OS: Windows, macOS, or Linux
• Dependencies: listed in requirements.txt

You can either use the provided virtual environment in env/ (Windows), or create your own.

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2. Setup

From the project root (WordleSolver/), you have two main options.

• Option A – Use your own virtual environment (recommended)

  1. Create and activate venv

     # from the project root
     python -m venv .venv
     
     # Windows (PowerShell)
     .venv\Scripts\Activate.ps1
     
     # Windows (cmd.exe)
     .venv\Scripts\activate.bat
     
     # macOS / Linux
     source .venv/bin/activate

  2. Install dependencies

     pip install -r requirements.txt

• Option B – Use the existing env/ folder (Windows)
  From PowerShell in the project root:

  .\env\Scripts\Activate.ps1

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3. Running the Wordle Solver GUI

Once your environment is activated and dependencies are installed, run:

python main.py

By default NiceGUI will start a local web server (typically at http://127.0.0.1:8080).
Open the printed URL in your browser.

In the UI you can:

• Type guesses using your physical keyboard.
• Select a solver from the dropdown (BFS, DFS, Entropy Solver, CSP Solver).
• Optionally set a start word.
• Click “Auto-solve” to let the chosen solver play.
• Click “Clear All” to reset the game.

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4. Project Structure (overview)

• main.py: Entry point; starts the NiceGUI app.
• core/
  • game.py: Core Wordle game logic (state, feedback, validity checks).
  • controller.py: Connects game logic, GUI (gui/view.py), and solvers.
• gui/view.py: NiceGUI-based UI (grid, controls, metrics panel).
• solvers/
  • bfs_solver.py, dfs_solver.py, entropy_solver.py, csp_solver2.py: Different search/AI strategies.
• data/: Word lists and precomputed helpers (successors, letter-to-words).
• experiment/: Scripts and charts for running performance experiments.
• preprocessing/: Scripts to precompute helper structures (successors, connectivity, etc.).

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5. Running Experiments

If you want to reproduce or extend the experimental evaluation of the solvers:

1. Ensure your environment is activated and dependencies are installed.

2. From the project root, run:

   python experiment/run_experiment.py

This will use the solvers to run batched games and write statistics/plots into experiment/stats/ and experiment/charts*/.

Note: Depending on your hardware, running all experiments may take some time.

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