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e142bc5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import gradio as gr
import json
import os
import matplotlib.pyplot as plt # Kept for potential future gr.Plot use or if figures are handled directly
from simulation import RFTSimulation
from utils import export_json, seed_everything
from IPython.display import Image, display # For Colab dev mode inline image display
# Helper function to run simulation and generate plots/export data
def run_simulation_and_plot(num_agents, steps, mutation_rate, drift_rate, seed=None):
if seed is not None:
seed_everything(seed)
# Instantiate simulation with save_plots=True, so it saves plots during its run() method
simulation = RFTSimulation(num_agents, steps, mutation_rate, drift_rate, save_plots=True)
# Run the simulation. The internal run method will save plots if save_plots=True.
# It returns all_agents_history, coherence_list, stability_list.
all_agents_history, coherence_list, stability_list = simulation.run()
# Prepare final agent states for JSON export
final_agent_states = []
for i, agent in enumerate(simulation.agents):
final_agent_states.append({
'id': i + 1,
'phi': agent.phi,
'tau_eff': agent.tau_eff,
'tier': agent.tier,
'fitness': agent.fitness
})
# Export JSON
json_filename = "final_agent_states.json"
export_json(final_agent_states, json_filename)
# Return filenames of the generated plots and the JSON for Gradio or dev mode display
plot_filenames = [
'phi_plot.png',
'tau_plot.png',
'fitness_plot.png',
'coherence_plot.png',
'stability_plot.png'
]
return (*plot_filenames, json_filename)
# Gradio Interface setup
iface = gr.Interface(
fn=run_simulation_and_plot,
inputs=[
gr.Slider(1, 10, value=3, step=1, label="Number of Agents"),
gr.Slider(10, 500, value=100, step=10, label="Steps"),
gr.Slider(0.01, 0.5, value=0.05, step=0.01, label="Mutation Rate"),
gr.Slider(0.01, 0.5, value=0.02, step=0.01, label="Drift Rate"),
gr.Number(value=42, label="Random Seed (optional, leave empty for random)") # Clarified label
],
outputs=[
gr.Image(label="Phi Plot"), # Changed to gr.Image to display saved PNGs
gr.Image(label="Tau Effective Plot"),
gr.Image(label="Fitness Plot"),
gr.Image(label="Coherence Plot"),
gr.Image(label="Stability Plot"),
gr.File(label="Exported Final Agent States (JSON)")
],
title="RFT Agent Simulation Engine MVP",
description="Simulate RFT Agents and visualize their dynamics."
)
# Colab-friendly "dev mode" or Gradio launch logic
# Check if running in Colab environment (simple heuristic)
IN_COLAB = 'COLAB_GPU' in os.environ or 'Google' in os.environ.get('COLAB_CLOUD_PLATFORM', '')
if IN_COLAB:
print("Running in Colab development mode (skipping Gradio launch).")
# Run the test simulation logic as per requirements
num_agents = 3
steps = 100
mutation_rate = 0.05
drift_rate = 0.02
seed = 42
print("\n--- Running Test Simulation in Dev Mode ---")
# Call the core simulation function. It will save plots and JSON.
plot_files_and_json_file = run_simulation_and_plot(num_agents, steps, mutation_rate, drift_rate, seed)
plot_files = plot_files_and_json_file[:-1]
json_file = plot_files_and_json_file[-1]
print("\nSimulation complete. Displaying plots and exported JSON path:")
for plot_file in plot_files:
print(f" - {os.path.abspath(plot_file)}")
if os.path.exists(plot_file):
display(Image(filename=plot_file)) # Display inline in Colab
else:
print(f"Warning: Plot file {plot_file} not found for inline display.")
print(f" - {os.path.abspath(json_file)}")
print(f"Final agent states exported to {json_file}")
print("\n--- End of Test Simulation in Dev Mode ---")
else:
print("Running Gradio interface. Call iface.launch() to start it.")
# For local development, uncomment this to launch Gradio:
# iface.launch()
print("app.py updated successfully.")
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