import copy
import json
import re
import requests
from curl_cffi import requests as cffi_requests
from tclogger import logger
from constants.models import MODEL_MAP
from constants.envs import PROXIES
from constants.headers import HUGGINGCHAT_POST_HEADERS, HUGGINGCHAT_SETTINGS_POST_DATA
from messagers.message_outputer import OpenaiStreamOutputer
from messagers.message_composer import MessageComposer
from messagers.token_checker import TokenChecker
class HuggingchatRequester:
def __init__(self, model: str):
if model in MODEL_MAP.keys():
self.model = model
else:
self.model = "nous-mixtral-8x7b"
self.model_fullname = MODEL_MAP[self.model]
def get_hf_chat_id(self):
request_url = "https://huggingface.co/chat/settings"
request_body = copy.deepcopy(HUGGINGCHAT_SETTINGS_POST_DATA)
extra_body = {
"activeModel": self.model_fullname,
}
request_body.update(extra_body)
logger.note(f"> hf-chat ID:", end=" ")
res = cffi_requests.post(
request_url,
headers=HUGGINGCHAT_POST_HEADERS,
json=request_body,
proxies=PROXIES,
timeout=10,
impersonate="chrome",
)
self.hf_chat_id = res.cookies.get("hf-chat")
if self.hf_chat_id:
logger.success(f"[{self.hf_chat_id}]")
else:
logger.warn(f"[{res.status_code}]")
logger.warn(res.text)
raise ValueError(f"Failed to get hf-chat ID: {res.text}")
def get_conversation_id(self, system_prompt: str = ""):
request_url = "https://huggingface.co/chat/conversation"
request_headers = HUGGINGCHAT_POST_HEADERS
extra_headers = {
"Cookie": f"hf-chat={self.hf_chat_id}",
}
request_headers.update(extra_headers)
request_body = {
"model": self.model_fullname,
"preprompt": system_prompt,
}
logger.note(f"> Conversation ID:", end=" ")
res = requests.post(
request_url,
headers=request_headers,
json=request_body,
proxies=PROXIES,
timeout=10,
)
if res.status_code == 200:
conversation_id = res.json()["conversationId"]
logger.success(f"[{conversation_id}]")
else:
logger.warn(f"[{res.status_code}]")
raise ValueError("Failed to get conversation ID!")
self.conversation_id = conversation_id
return conversation_id
def get_last_message_id(self):
request_url = f"https://huggingface.co/chat/conversation/{self.conversation_id}/__data.json?x-sveltekit-invalidated=11"
request_headers = HUGGINGCHAT_POST_HEADERS
extra_headers = {
"Cookie": f"hf-chat={self.hf_chat_id}",
}
request_headers.update(extra_headers)
logger.note(f"> Message ID:", end=" ")
message_id = None
res = requests.post(
request_url,
headers=request_headers,
proxies=PROXIES,
timeout=10,
)
if res.status_code == 200:
data = res.json()["nodes"][1]["data"]
# find the last element which matches the format of uuid4
uuid_pattern = re.compile(
r"^[\da-f]{8}-[\da-f]{4}-[\da-f]{4}-[\da-f]{4}-[\da-f]{12}$"
)
for item in data:
if type(item) == str and uuid_pattern.match(item):
message_id = item
logger.success(f"[{message_id}]")
else:
logger.warn(f"[{res.status_code}]")
raise ValueError("Failed to get message ID!")
return message_id
def log_request(self, url, method="GET"):
logger.note(f"> {method}:", end=" ")
logger.mesg(f"{url}", end=" ")
def log_response(
self, res: requests.Response, stream=False, iter_lines=False, verbose=False
):
status_code = res.status_code
status_code_str = f"[{status_code}]"
if status_code == 200:
logger_func = logger.success
else:
logger_func = logger.warn
logger.enter_quiet(not verbose)
logger_func(status_code_str)
if status_code != 200:
logger_func(res.text)
if stream:
if not iter_lines:
return
for line in res.iter_lines():
line = line.decode("utf-8")
line = re.sub(r"^data:\s*", "", line)
line = line.strip()
if line:
try:
data = json.loads(line, strict=False)
msg_type = data.get("type")
if msg_type == "status":
msg_status = data.get("status")
elif msg_type == "stream":
content = data.get("token", "")
logger_func(content, end="")
elif msg_type == "finalAnswer":
full_content = data.get("text")
logger.success("\n[Finished]")
break
else:
pass
except Exception as e:
logger.warn(e)
else:
logger_func(res.json())
logger.exit_quiet(not verbose)
def chat_completions(self, messages: list[dict], iter_lines=False, verbose=False):
composer = MessageComposer(model=self.model)
system_prompt, input_prompt = composer.decompose_to_system_and_input_prompt(
messages
)
checker = TokenChecker(input_str=system_prompt + input_prompt, model=self.model)
checker.check_token_limit()
self.get_hf_chat_id()
self.get_conversation_id(system_prompt=system_prompt)
message_id = self.get_last_message_id()
request_url = f"https://huggingface.co/chat/conversation/{self.conversation_id}"
request_headers = copy.deepcopy(HUGGINGCHAT_POST_HEADERS)
extra_headers = {
"Content-Type": "text/event-stream",
"Referer": request_url,
"Cookie": f"hf-chat={self.hf_chat_id}",
}
request_headers.update(extra_headers)
request_body = {
"files": [],
"id": message_id,
"inputs": input_prompt,
"is_continue": False,
"is_retry": False,
"web_search": False,
}
self.log_request(request_url, method="POST")
res = requests.post(
request_url,
headers=request_headers,
json=request_body,
proxies=PROXIES,
stream=True,
)
self.log_response(res, stream=True, iter_lines=iter_lines, verbose=verbose)
return res
class HuggingchatStreamer:
def __init__(self, model: str):
if model in MODEL_MAP.keys():
self.model = model
else:
self.model = "nous-mixtral-8x7b"
self.model_fullname = MODEL_MAP[self.model]
self.message_outputer = OpenaiStreamOutputer(model=self.model)
def chat_response(self, messages: list[dict], verbose=False):
requester = HuggingchatRequester(model=self.model)
return requester.chat_completions(
messages=messages, iter_lines=False, verbose=verbose
)
def chat_return_generator(self, stream_response: requests.Response, verbose=False):
is_finished = False
for line in stream_response.iter_lines():
line = line.decode("utf-8")
line = re.sub(r"^data:\s*", "", line)
line = line.strip()
if not line:
continue
content = ""
content_type = "Completions"
try:
data = json.loads(line, strict=False)
msg_type = data.get("type")
if msg_type == "status":
msg_status = data.get("status")
continue
elif msg_type == "stream":
content_type = "Completions"
content = data.get("token", "")
if verbose:
logger.success(content, end="")
elif msg_type == "finalAnswer":
content_type = "Finished"
content = ""
full_content = data.get("text")
if verbose:
logger.success("\n[Finished]")
is_finished = True
break
else:
continue
except Exception as e:
logger.warn(e)
output = self.message_outputer.output(
content=content, content_type=content_type
)
yield output
if not is_finished:
yield self.message_outputer.output(content="", content_type="Finished")
def chat_return_dict(self, stream_response: requests.Response):
final_output = self.message_outputer.default_data.copy()
final_output["choices"] = [
{
"index": 0,
"finish_reason": "stop",
"message": {"role": "assistant", "content": ""},
}
]
final_content = ""
for item in self.chat_return_generator(stream_response):
try:
data = json.loads(item)
delta = data["choices"][0]["delta"]
delta_content = delta.get("content", "")
if delta_content:
final_content += delta_content
except Exception as e:
logger.warn(e)
final_output["choices"][0]["message"]["content"] = final_content.strip()
return final_output
if __name__ == "__main__":
# model = "command-r-plus"
model = "llama3-70b"
# model = "zephyr-141b"
streamer = HuggingchatStreamer(model=model)
messages = [
{
"role": "system",
"content": "You are an LLM developed by CloseAI.\nYour name is Hansimov-Copilot.",
},
{"role": "user", "content": "Hello, what is your role?"},
{"role": "assistant", "content": "I am an LLM."},
{"role": "user", "content": "What is your name?"},
]
streamer.chat_response(messages=messages)
# HF_ENDPOINT=https://hf-mirror.com python -m networks.huggingchat_streamer