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# + tags=["hide_inp"]
desc = """
# ChatGPT
"ChatGPT" like examples. Adapted from
[LangChain](https://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html)'s
version of this [blog post](https://www.engraved.blog/building-a-virtual-machine-inside/).
"""
# -
import warnings
from dataclasses import dataclass
from typing import List, Tuple
import minichain
# + tags=["hide_inp"]
warnings.filterwarnings("ignore")
# -
# Generic stateful Memory
MEMORY = 2
@dataclass
class State:
memory: List[Tuple[str, str]]
human_input: str = ""
def push(self, response: str) -> "State":
memory = self.memory if len(self.memory) < MEMORY else self.memory[1:]
return State(memory + [(self.human_input, response)])
# Chat prompt with memory
class ChatPrompt(minichain.TemplatePrompt):
template_file = "chatgpt.pmpt.tpl"
def parse(self, out: str, inp: State) -> State:
result = out.split("Assistant:")[-1]
return inp.push(result)
# class Human(minichain.Prompt):
# def parse(self, out: str, inp: State) -> State:
# return inp.human_input = out
with minichain.start_chain("chat") as backend:
prompt = ChatPrompt(backend.OpenAI())
state = State([])
examples = [
"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.",
"ls ~",
"cd ~",
"{Please make a file jokes.txt inside and put some jokes inside}",
"""echo -e "x=lambda y:y*5+3;print('Result:' + str(x(6)))" > run.py && python3 run.py""",
"""echo -e "print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])" > run.py && python3 run.py""",
"""echo -e "echo 'Hello from Docker" > entrypoint.sh && echo -e "FROM ubuntu:20.04\nCOPY entrypoint.sh entrypoint.sh\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image""",
"nvidia-smi"
]
gradio = prompt.to_gradio(fields= ["human_input"],
initial_state= state,
examples=examples,
out_type="json",
description=desc
)
if __name__ == "__main__":
gradio.launch()
# for i in range(len(fake_human)):
# human.chain(prompt)
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