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import requests
from typing import Any, AsyncGenerator, Dict, Optional
import json
import re
from ..AIutel import Optimizers
from ..AIutel import Conversation
from ..AIutel import AwesomePrompts, sanitize_stream
from ..AIbase import Provider, AsyncProvider
from webscout import exceptions
class ChatGPTUK(Provider):
"""
A class to interact with the ChatGPT UK API.
"""
def __init__(
self,
is_conversation: bool = True,
max_tokens: int = 600,
temperature: float = 0.9,
presence_penalty: float = 0,
frequency_penalty: float = 0,
top_p: float = 1,
model: str = "google-gemini-pro",
timeout: int = 30,
intro: str = None,
filepath: str = None,
update_file: bool = True,
proxies: dict = {},
history_offset: int = 10250,
act: str = None,
) -> None:
"""
Initializes the ChatGPTUK API with given parameters.
Args:
is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
temperature (float, optional): Charge of the generated text's randomness. Defaults to 0.9.
presence_penalty (float, optional): Chances of topic being repeated. Defaults to 0.
frequency_penalty (float, optional): Chances of word being repeated. Defaults to 0.
top_p (float, optional): Sampling threshold during inference time. Defaults to 1.
model (str, optional): LLM model name. Defaults to "google-gemini-pro".
timeout (int, optional): Http request timeout. Defaults to 30.
intro (str, optional): Conversation introductory prompt. Defaults to None.
filepath (str, optional): Path to file containing conversation history. Defaults to None.
update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
proxies (dict, optional): Http request proxies. Defaults to {}.
history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
"""
self.session = requests.Session()
self.is_conversation = is_conversation
self.max_tokens_to_sample = max_tokens
self.api_endpoint = "https://free.chatgpt.org.uk/api/openai/v1/chat/completions"
self.stream_chunk_size = 64
self.timeout = timeout
self.last_response = {}
self.model = model
self.temperature = temperature
self.presence_penalty = presence_penalty
self.frequency_penalty = frequency_penalty
self.top_p = top_p
self.headers = {"Content-Type": "application/json"}
self.__available_optimizers = (
method
for method in dir(Optimizers)
if callable(getattr(Optimizers, method)) and not method.startswith("__")
)
self.session.headers.update(self.headers)
Conversation.intro = (
AwesomePrompts().get_act(
act, raise_not_found=True, default=None, case_insensitive=True
)
if act
else intro or Conversation.intro
)
self.conversation = Conversation(
is_conversation, self.max_tokens_to_sample, filepath, update_file
)
self.conversation.history_offset = history_offset
self.session.proxies = proxies
def ask(
self,
prompt: str,
stream: bool = False,
raw: bool = False,
optimizer: str = None,
conversationally: bool = False,
) -> dict:
"""Chat with AI
Args:
prompt (str): Prompt to be send.
stream (bool, optional): Flag for streaming response. Defaults to False.
raw (bool, optional): Stream back raw response as received. Defaults to False.
optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
Returns:
dict : {}
```json
{
"text" : "How may I assist you today?"
}
```
"""
conversation_prompt = self.conversation.gen_complete_prompt(prompt)
if optimizer:
if optimizer in self.__available_optimizers:
conversation_prompt = getattr(Optimizers, optimizer)(
conversation_prompt if conversationally else prompt
)
else:
raise Exception(
f"Optimizer is not one of {self.__available_optimizers}"
)
self.session.headers.update(self.headers)
payload = {
"messages": [
{"role": "system", "content": "Keep your responses long and detailed"},
{"role": "user", "content": conversation_prompt}
],
"stream": True,
"model": self.model,
"temperature": self.temperature,
"presence_penalty": self.presence_penalty,
"frequency_penalty": self.frequency_penalty,
"top_p": self.top_p,
"max_tokens": self.max_tokens_to_sample
}
def for_stream():
response = self.session.post(
self.api_endpoint, json=payload, stream=True, timeout=self.timeout
)
if not response.ok:
raise exceptions.FailedToGenerateResponseError(
f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
)
streaming_response = ""
for line in response.iter_lines(decode_unicode=True, chunk_size=1):
if line:
modified_line = re.sub("data:", "", line)
try:
json_data = json.loads(modified_line)
content = json_data['choices'][0]['delta']['content']
streaming_response += content
yield content if raw else dict(text=streaming_response)
except:
continue
self.last_response.update(dict(text=streaming_response))
self.conversation.update_chat_history(
prompt, self.get_message(self.last_response)
)
def for_non_stream():
for _ in for_stream():
pass
return self.last_response
return for_stream() if stream else for_non_stream()
def chat(
self,
prompt: str,
stream: bool = False,
optimizer: str = None,
conversationally: bool = False,
) -> str:
"""Generate response `str`
Args:
prompt (str): Prompt to be send.
stream (bool, optional): Flag for streaming response. Defaults to False.
optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
Returns:
str: Response generated
"""
def for_stream():
for response in self.ask(
prompt, True, optimizer=optimizer, conversationally=conversationally
):
yield self.get_message(response)
def for_non_stream():
return self.get_message(
self.ask(
prompt,
False,
optimizer=optimizer,
conversationally=conversationally,
)
)
return for_stream() if stream else for_non_stream()
def get_message(self, response: dict) -> str:
"""Retrieves message only from response
Args:
response (dict): Response generated by `self.ask`
Returns:
str: Message extracted
"""
assert isinstance(response, dict), "Response should be of dict data-type only"
return response["text"] |