app.py

import gradio as gr
from train import TrainingLoop
from scipy.special import softmax
import numpy as np


train = None

frames, attributions = None, None

lunar_lander_spec_conversion = {
    0: "X-coordinate",
    1: "Y-coordinate",
    2: "Linear velocity in the X-axis",
    3: "Linear velocity in the Y-axis",
    4: "Angle",
    5: "Angular velocity",
    6: "Left leg touched the floor",
    7: "Right leg touched the floor"
}

def create_training_loop(env_spec):
    global train
    train = TrainingLoop(env_spec=env_spec)
    train.create_agent()
    
    return train.env.spec

def display_softmax(inputs):
    inputs = np.array(inputs)
    probabilities = softmax(inputs)

    softmax_dict = {name: float(prob) for name, prob in zip(lunar_lander_spec_conversion.values(), probabilities)}

    return softmax_dict

def generate_output(num_iterations, option):
    global frames, attributions
    frames, attributions = train.explain_trained(num_iterations=num_iterations, option=option)
    slider.maximum = len(frames)

def get_frame_and_attribution(slider_value):
    global frames, attributions
    slider_value = min(slider_value, len(frames) - 1)
    frame = frames[slider_value]

    print(f"{frame.shape=}")

    attribution = display_softmax(attributions[slider_value])

    return frame, attribution
    
with gr.Blocks() as demo:
    gr.Markdown("# Introspection in Deep Reinforcement Learning")

    with gr.Tab(label="Attribute"):
        env_spec = gr.Dropdown(choices=["LunarLander-v2"],type="value",multiselect=False, label="Environment Specification (e.g.: LunarLander-v2)", lines=1)
        env = gr.Interface(title="Create the Environment", allow_flagging="never", inputs=env_spec, fn=create_training_loop, outputs=gr.JSON())

        with gr.Row():
            option = gr.Dropdown(choices=["Torch Tensor of 0's", "Running Average"], type="index")
            baselines = gr.Slider(label="Number of Baseline Iterations", interactive=True, minimum=0, maximum=100, value=10, step=5, info="Baseline inputs to collect for the average", render=True)
        gr.Button("ATTRIBUTE").click(fn=generate_output, inputs=[baselines, option])
        slider = gr.Slider(label="Key Frame", minimum=0, maximum=1000, step=1, value=0)

        gr.Interface(fn=get_frame_and_attribution, inputs=slider, live=True, outputs=[gr.Image(label="Timestep")])

demo.launch()