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| title: RFT Agent Simulation Engine | |
| emoji: 🧪 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: gradio | |
| app_file: app.py | |
| pinned: false | |
| # RFT Agent Simulation Engine MVP | |
| This project implements a Minimal Viable Product (MVP) of an RFT (Relational Frame Theory) Agent Simulation Engine. It allows for simulating the dynamics of RFT agents, visualizing their behavior, and exporting their states. | |
| ## Project Structure | |
| - `agent.py`: Defines the `RFTAgent` class. | |
| - `simulation.py`: Defines the `RFTSimulation` class to manage agent interactions over time. | |
| - `visualization.py`: Contains functions for plotting simulation results using `matplotlib`. | |
| - `app.py`: Implements a Gradio web interface for running simulations and visualizing results. | |
| - `utils.py`: Provides utility functions for JSON export and seeding random number generators. | |
| - `requirements.txt`: Lists all Python package dependencies. | |
| - `README.md`: This file. | |
| ## How to Run in Colab | |
| 1. **Open this Colab Notebook:** Ensure you have access to the notebook where these files were generated. | |
| 2. **Run All Cells:** Execute all cells in the notebook sequentially. This will create all the necessary `.py` files, `requirements.txt`, and `README.md` in your Colab environment. | |
| 3. **Install Dependencies:** Open a new code cell and run: | |
| ```bash | |
| !pip install -r requirements.txt | |
| ``` | |
| 4. **Run the Gradio App:** To launch the interactive Gradio interface, open a new code cell and run: | |
| ```python | |
| import app | |
| app.iface.launch(debug=True) | |
| ``` | |
| You will get a public URL (or a local URL if running locally) to access the Gradio interface in your browser. | |
| ## How to Deploy to Hugging Face Spaces | |
| 1. **Create a New Space:** Go to [Hugging Face Spaces](https://huggingface.co/spaces) and create a new Space. Choose `Gradio` as the SDK. | |
| 2. **Upload Files:** Upload all the generated files (`agent.py`, `simulation.py`, `visualization.py`, `app.py`, `utils.py`, `requirements.txt`, `README.md`) to your Hugging Face Space repository. | |
| 3. **Configure `app.py`:** Ensure your `app.py` has the `iface.launch()` call at the end (as provided). Hugging Face Spaces automatically detects and runs Gradio apps. | |
| 4. **Monitor Build Logs:** Check the build logs in your Space settings for any dependency installation issues or errors. | |
| 5. **Access the App:** Once successfully built, your Gradio app will be live and accessible via the Space URL. | |
| ## Example Usage | |
| After running `app.iface.launch()` in Colab, navigate to the provided URL. You will see a Gradio interface where you can adjust parameters: | |
| - **Number of Agents:** e.g., 3 | |
| - **Steps:** e.g., 100 | |
| - **Mutation Rate:** e.g., 0.05 | |
| - **Drift Rate:** e.g., 0.02 | |
| - **Random Seed:** e.g., 42 | |
| Click the 'Submit' button to run the simulation. The interface will display: | |
| - Plots for individual agent Phi, Tau Effective, and Fitness over time. | |
| - Plots for System Coherence (average Phi) and System Stability (variance of Phi) over time. | |
| - A downloadable JSON file containing the final states of all agents. | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |