gpt4free/g4f/Provider/HuggingChat.py

152 lines
5.7 KiB
Python

from __future__ import annotations
import json
import requests
from curl_cffi import requests as cf_reqs
from ..typing import CreateResult, Messages
from .base_provider import ProviderModelMixin, AbstractProvider
from .helper import format_prompt
class HuggingChat(AbstractProvider, ProviderModelMixin):
url = "https://huggingface.co/chat"
working = True
supports_stream = True
default_model = "meta-llama/Meta-Llama-3.1-70B-Instruct"
models = [
'meta-llama/Meta-Llama-3.1-70B-Instruct',
'CohereForAI/c4ai-command-r-plus-08-2024',
'Qwen/Qwen2.5-72B-Instruct',
'nvidia/Llama-3.1-Nemotron-70B-Instruct-HF',
'meta-llama/Llama-3.2-11B-Vision-Instruct',
'NousResearch/Hermes-3-Llama-3.1-8B',
'mistralai/Mistral-Nemo-Instruct-2407',
'microsoft/Phi-3.5-mini-instruct',
]
model_aliases = {
"llama-3.1-70b": "meta-llama/Meta-Llama-3.1-70B-Instruct",
"command-r-plus": "CohereForAI/c4ai-command-r-plus-08-2024",
"qwen-2-72b": "Qwen/Qwen2.5-72B-Instruct",
"nemotron-70b": "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF",
"llama-3.2-11b": "meta-llama/Llama-3.2-11B-Vision-Instruct",
"hermes-3": "NousResearch/Hermes-3-Llama-3.1-8B",
"mistral-nemo": "mistralai/Mistral-Nemo-Instruct-2407",
"phi-3.5-mini": "microsoft/Phi-3.5-mini-instruct",
}
@classmethod
def get_model(cls, model: str) -> str:
if model in cls.models:
return model
elif model in cls.model_aliases:
return cls.model_aliases[model]
else:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
stream: bool,
**kwargs
) -> CreateResult:
model = cls.get_model(model)
if model in cls.models:
session = cf_reqs.Session()
session.headers = {
'accept': '*/*',
'accept-language': 'en',
'cache-control': 'no-cache',
'origin': 'https://huggingface.co',
'pragma': 'no-cache',
'priority': 'u=1, i',
'referer': 'https://huggingface.co/chat/',
'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"macOS"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'same-origin',
'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
}
json_data = {
'model': model,
}
response = session.post('https://huggingface.co/chat/conversation', json=json_data)
if response.status_code != 200:
raise RuntimeError(f"Request failed with status code: {response.status_code}, response: {response.text}")
conversationId = response.json().get('conversationId')
response = session.get(f'https://huggingface.co/chat/conversation/{conversationId}/__data.json?x-sveltekit-invalidated=11')
data: list = response.json()["nodes"][1]["data"]
keys: list[int] = data[data[0]["messages"]]
message_keys: dict = data[keys[0]]
messageId: str = data[message_keys["id"]]
settings = {
"inputs": format_prompt(messages),
"id": messageId,
"is_retry": False,
"is_continue": False,
"web_search": False,
"tools": []
}
headers = {
'accept': '*/*',
'accept-language': 'en',
'cache-control': 'no-cache',
'origin': 'https://huggingface.co',
'pragma': 'no-cache',
'priority': 'u=1, i',
'referer': f'https://huggingface.co/chat/conversation/{conversationId}',
'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"macOS"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'same-origin',
'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
}
files = {
'data': (None, json.dumps(settings, separators=(',', ':'))),
}
response = requests.post(f'https://huggingface.co/chat/conversation/{conversationId}',
cookies=session.cookies,
headers=headers,
files=files,
)
full_response = ""
for line in response.iter_lines():
if not line:
continue
try:
line = json.loads(line)
except json.JSONDecodeError as e:
print(f"Failed to decode JSON: {line}, error: {e}")
continue
if "type" not in line:
raise RuntimeError(f"Response: {line}")
elif line["type"] == "stream":
token = line["token"].replace('\u0000', '')
full_response += token
elif line["type"] == "finalAnswer":
break
full_response = full_response.replace('<|im_end|', '').replace('\u0000', '').strip()
yield full_response