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#!/bin/env python3
import sys
import openai
import pickle
from collections import defaultdict
from dataclasses import dataclass
from typing import Tuple
from .openai_wrappers import (
create_chat_completion,
list_models,
OpenAIChatResponse,
OpenAIChatResponseStream,
FinishReason,
Role,
ChatMessage
)
from .argparsing import (
parse_args,
Arguments,
DisplayArguments,
CompletionArguments,
DebugArguments,
)
from .version import VERSION
from .color import get_color_codes
###########################
#### SAVE / REPLAY ####
###########################
def create_singleton_chat_completion(
message : str,
completion_args : CompletionArguments
):
hist = [ ChatMessage( Role.USER, message ) ]
completion = create_chat_completion(hist, completion_args)
return completion
def save_response_and_arguments(args : Arguments) -> None:
message = args.initial_message
completion = create_singleton_chat_completion(message, args.completion_args)
completion = list(completion)
filename = args.debug_args.save_response_to_file
with open(filename, 'wb') as f:
pickle.dump((message, args.completion_args, completion,), f)
def load_response_and_arguments(args : Arguments) \
-> Tuple[CompletionArguments, OpenAIChatResponseStream]:
filename = args.debug_args.load_response_from_file
with open(filename, 'rb') as f:
message, args, completion = pickle.load(f)
return (message, args, completion)
#########################
#### PRETTY PRINTING ####
#########################
@dataclass
class CumulativeResponse:
delta_content: str = ""
finish_reason: FinishReason = FinishReason.NONE
content: str = ""
def take_delta(self : "CumulativeResponse"):
chunk = self.delta_content
self.delta_content = ""
return chunk
def add_content(self : "CumulativeResponse", new_chunk : str):
self.content += new_chunk
self.delta_content += new_chunk
def print_streamed_response(
display_args : DisplayArguments,
completion : OpenAIChatResponseStream,
n_completions : int,
return_responses : bool = False
) -> None:
"""
Print the response in real time by printing the deltas as they occur. If multiple responses
are requested, print the first in real-time, accumulating the others in the background. One the
first response completes, move on to the second response printing the deltas in real time. Continue
on until all responses have been printed.
"""
COLOR_CODE = get_color_codes(no_color = not display_args.color)
adornments = display_args.adornments
cumu_responses = defaultdict(CumulativeResponse)
display_idx = 0
prompt_printed = False
for update in completion:
for choice in update.choices:
delta = choice.delta
if delta.content:
cumu_responses[choice.index].add_content(delta.content)
if choice.finish_reason is not FinishReason.NONE:
cumu_responses[choice.index].finish_reason = choice.finish_reason
display_response = cumu_responses[display_idx]
if not prompt_printed and adornments:
res_indicator = '' if n_completions == 1 else \
f' {display_idx + 1}/{n_completions}'
PROMPT = f'[{COLOR_CODE.GREEN}{update.model}{COLOR_CODE.RESET}{COLOR_CODE.RED}{res_indicator}{COLOR_CODE.RESET}]'
prompt_printed = True
print(PROMPT, end=' ', flush=True)
content = display_response.take_delta()
print(f'{COLOR_CODE.WHITE}{content}{COLOR_CODE.RESET}',
sep='', end='', flush=True)
if display_response.finish_reason is not FinishReason.NONE:
if display_idx < n_completions:
display_idx += 1
prompt_printed = False
if adornments:
print(end='\n\n', flush=True)
else:
print(end='\n', flush=True)
if return_responses:
return [ cumu_responses[i].content for i in range(n_completions) ]
def cmd_version():
print(f'version {VERSION}')
def cmd_list_models():
for model in list_models():
print(model)
def cmd_interactive(args : Arguments):
COLOR_CODE = get_color_codes(no_color = not args.display_args.color)
completion_args = args.completion_args
display_args = args.display_args
hist = []
def print_prompt():
print(f'[{COLOR_CODE.WHITE}#{COLOR_CODE.RESET}]', end=' ', flush=True)
def prompt_message() -> bool:
print_prompt()
# Control-D closes the input stream
try:
message = input()
except EOFError:
print()
return False
hist.append( ChatMessage( Role.USER, message ) )
return True
print(f'GPT Chat CLI version {VERSION}')
print(f'Press Control-D to exit')
if args.initial_message:
print_prompt()
print( args.initial_message )
hist.append( ChatMessage( Role.USER, args.initial_message ) )
else:
prompt_message()
while True:
completion = create_chat_completion(hist, completion_args)
response = print_streamed_response(
display_args, completion, 1, return_responses=True,
)[0]
hist.append( ChatMessage(Role.ASSISTANT, response) )
if not prompt_message():
break
def cmd_singleton(args: Arguments):
completion_args = args.completion_args
debug_args : DebugArguments = args.debug_args
message = args.initial_message
if debug_args.save_response_to_file:
save_response_and_arguments(args)
return
elif debug_args.load_response_from_file:
message, completion_args, completion = load_response_and_arguments(args)
else:
# message is only None is a TTY is not attached
if message is None:
message = sys.stdin.read()
completion = create_singleton_chat_completion(message, completion_args)
print_streamed_response(
args.display_args,
completion,
completion_args.n_completions
)
def main():
args = parse_args()
if args.version:
cmd_version()
elif args.list_models:
cmd_list_models()
elif args.interactive:
cmd_interactive(args)
else:
cmd_singleton(args)
if __name__ == "__main__":
main()
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