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import gradio as gr
import os
from moviepy.editor import *
def replay(option):
path = ""
# Get the correct model
if (option == "LunarLander-v2 ππ©βπ"):
path = "./LunarLander-v2.mp4"
elif(option == "CartPole-v1 πΉοΈ"):
path = "./CartPole-v1.mp4"
elif(option == "Atari Space Invaders πΎ"):
path = "./SpaceInvadersNoFrameskip-v4.mp4"
# The only turnaround I found (Base64 video pb)
videoclip = VideoFileClip(path)
videoclip.write_videofile("new_filename.mp4")
return 'new_filename.mp4'
"""
TODO: Next version with live video generation
def replay_classical(hf_model_filename, hf_model_id):
import gym
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
model = PPO.load_from_huggingface(hf_model_id,hf_model_filename)
eval_env = gym.make(option)
def replay_atari(hf_model_filename, hf_model_id):
"""
iface = gr.Interface(
replay,
[
gr.inputs.Dropdown(["Atari Space Invaders πΎ", "CartPole-v1 πΉοΈ", "LunarLander-v2 ππ©βπ"]),
],
"video",
title = 'Stable Baselines 3 with π€',
description = '',
article =
'''<div>
<p style="text-align: center">This version of the RL library allows you to load models directly from the Hugging Face Hub</p>
<p style="text-align: center"> Select the trained agent you want to watch perform.
These models are from <a href="https://github.com/araffin/rl-baselines-zoo">Stable Baseline Zoo</a></p>
<p>
There are currently 3 models:
<ul>
<li><a href="https://huggingface.co/ThomasSimonini/stable-baselines3-ppo-SpaceInvadersNoFrameskip-v4">PPO SpaceInvadersNoFrameskip-v4</a></li>
<li><a href="https://huggingface.co/ThomasSimonini/stable-baselines3-ppo-LunarLander-v2">PPO LunarLander-v2</a></li>
<li><a href="https://huggingface.co/ThomasSimonini/stable-baselines3-ppo-CartPole-v1">PPO CartPole-v1</a></li>
</ul>
</div>'''
)
iface.launch()
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