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from hydra.core.global_hydra import GlobalHydra
import gradio as gr
import os
import hydra
import random
import re
import openai
import IPython
import time
import pybullet as p
import traceback
from datetime import datetime
from pprint import pprint
import cv2
import re
import random
import json
from gensim.agent import Agent
from gensim.critic import Critic
from gensim.sim_runner import SimulationRunner
from gensim.memory import Memory
from gensim.utils import set_gpt_model, clear_messages
class DemoRunner:
def __init__(self):
self._env = None
GlobalHydra.instance().clear()
hydra.initialize(version_base="1.2", config_path='cliport/cfg')
self._cfg = hydra.compose(config_name="data")
def setup(self, api_key):
cfg = self._cfg
openai.api_key = api_key
cfg['model_output_dir'] = 'temp'
cfg['prompt_folder'] = 'bottomup_task_generation_prompt'
set_gpt_model(cfg['gpt_model'])
cfg['load_memory'] = True
cfg['use_template'] = True
cfg['task_description_candidate_num'] = 10
cfg['record']['save_video'] = True
print("cfg = ", cfg)
memory = Memory(cfg)
agent = Agent(cfg, memory)
critic = Critic(cfg, memory)
self.simulation_runner = SimulationRunner(cfg, agent, critic, memory)
info = '### Set up '
return info
def setup_top_down(self, api_key, target_task_name):
cfg = self._cfg
openai.api_key = api_key
cfg['model_output_dir'] = 'temp'
cfg['prompt_folder'] = 'topdown_task_generation_prompt'
set_gpt_model(cfg['gpt_model'])
cfg['load_memory'] = True
cfg['use_template'] = True
cfg['target_task_name'] = target_task_name
cfg['task_description_candidate_num'] = 10
cfg['record']['save_video'] = True
print("cfg = ", cfg)
memory = Memory(cfg)
agent = Agent(cfg, memory)
critic = Critic(cfg, memory)
self.simulation_runner = SimulationRunner(cfg, agent, critic, memory)
info = '### Set up '
return info
def run(self, instruction, progress):
cfg = self._cfg
cfg['target_task_name'] = instruction
# self._env.cache_video = []
self.simulation_runner._md_logger = ''
# progress(0.2)
yield "Task Generating ==>", None, None
yield from self.simulation_runner.task_creation()
yield from self.simulation_runner.simulate_task()
def run_example(self):
cfg = self._cfg
# self._env.cache_video = []
self.simulation_runner._md_logger = ''
# progress(0.2)
yield "Task Generating ==>", None, None
yield from self.simulation_runner.example_task_creation()
yield from self.simulation_runner.simulate_task()
def setup(api_key, option_choice, target_task_name):
print(option_choice)
if not api_key:
return 'Please enter your OpenAI API key!', None
if option_choice is None:
return 'Please choose the mode!', None
demo_runner = DemoRunner()
if option_choice == 'top-down':
info = demo_runner.setup_top_down(api_key, target_task_name) + option_choice
elif option_choice == 'bottom-up':
info = demo_runner.setup(api_key) + option_choice
else:
raise NotImplementedError
return info, demo_runner
def run(instruction, demo_runner, progress=gr.Progress()):
yield from demo_runner.run(instruction, progress=progress)
def run_example():
demo_runner = DemoRunner()
demo_runner.setup(1)
yield from demo_runner.run_example()
if __name__ == '__main__':
os.environ['GENSIM_ROOT'] = os.getcwd()
with open('README.md', 'r') as f:
for _ in range(12):
next(f)
readme_text = f.read()
with gr.Blocks() as demo:
state = gr.State(None)
gr.Markdown(readme_text)
gr.Markdown('# Interactive Demo')
with gr.Row():
with gr.Column():
btn_example_run = gr.Button("Run Example (OpenAI API Key not required)")
with gr.Row():
inp_api_key = gr.Textbox(label='OpenAI API Key (this is not stored anywhere)', lines=1)
option_choice = gr.Radio(["bottom-up", "top-down"], label="Which mode?", interactive=True)
inp_instruction = gr.Textbox(label='Target Task Name (if top-down)', lines=1)
info_setup = gr.Markdown(label='Setup Info')
btn_setup = gr.Button("Setup/Reset Simulation")
btn_run = gr.Button("Run (this may take 30+ seconds)")
# with gr.Column():
with gr.Row():
with gr.Column(scale=1, min_width=600):
progress = gr.Markdown(label='Progress')
generated_task = gr.Markdown(label='Generated Task')
generated_asset = gr.Markdown(label='Generated Asset')
generated_code = gr.Code(label='Generated Code', language="python", interactive=True)
video_run = gr.Video(label='Video of Last Instruction')
btn_setup.click(
setup,
inputs=[inp_api_key, option_choice, inp_instruction],
outputs=[info_setup, state]
)
btn_run.click(
run,
inputs=[inp_instruction, state],
outputs=[progress, generated_code, video_run]
)
btn_example_run.click(
run_example,
inputs=[],
outputs=[progress, generated_code, video_run]
)
demo.queue().launch(show_error=True)
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