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# + tags=["hide_inp"]
desc = """
### Chat
A chat-like example for multi-turn chat with state. [](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/chat.ipynb)
(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/).)
"""
# -
# $
from dataclasses import dataclass, replace
from typing import List, Tuple
from minichain import OpenAI, prompt, show, transform, Mock
# 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)])
def __str__(self):
return self.memory[-1][-1]
# Chat prompt with memory
@prompt(OpenAI(), template_file="chat.pmpt.tpl")
def chat_response(model, state: State) -> State:
return model.stream(state)
@transform()
def update(state, chat_output):
result = chat_output.split("Assistant:")[-1]
return state.push(result)
def chat(command, state):
state = replace(state, human_input=command)
return update(state, chat_response(state))
# $
examples = [
"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"
]
print(chat("ls", State([])).run())
gradio = show(chat,
initial_state=State([]),
subprompts=[chat_response],
examples=examples,
out_type="json",
description=desc,
code=open("chat.py", "r").read().split("$")[1].strip().strip("#").strip(),
)
if __name__ == "__main__":
gradio.queue().launch()
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