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| import numpy as np | |
| import time | |
| import streamlit as st | |
| from scienceworld import ScienceWorldEnv | |
| st.title("ScienceWorld interactive demo") | |
| hash_env = lambda _: None | |
| import os | |
| stream = os.popen('java -version') | |
| output = stream.read() | |
| st.write('output') | |
| def load_env(): | |
| simplification_str = 'easy' | |
| task_idx = None | |
| print('Loading envs') | |
| step_limit = 100 | |
| env = ScienceWorldEnv("", None, step_limit, 0) | |
| if task_idx is None: | |
| task_idx = 13 | |
| if isinstance(task_idx, int): | |
| task_names = env.getTaskNames() | |
| task_name = task_names[task_idx] | |
| else: | |
| task_name = task_idx | |
| # Just reset to variation 0, as another call (e.g. reset_with_variation...) will setup | |
| # an appropriate variation (train/dev/test) | |
| env.load(task_name, 0, simplification_str) | |
| obs, info = env.resetWithVariation(0, simplification_str) | |
| return env, obs, info | |
| class RandomAgent(): | |
| def act(self, info): | |
| return np.random.choice(info['valid']) | |
| num_episodes = 10 | |
| env, initial_obs, initial_info = load_env() | |
| act = st.text_input('action to perform') | |
| st.write(f'Action: {act}') | |
| obs, reward, done, info = env.step(act) | |
| st.write(f'Observation: {obs.strip()}') | |