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import ssl
import certifi
from functools import partial
ssl.default_ca_certs = certifi.where()
ssl.create_default_context = partial(
ssl.create_default_context,
cafile=certifi.where()
)
import g4f.api
import g4f.Provider
import json
import time
import requests
from g4f.Provider.helper import filter_none
from g4f.Provider.base_provider import AsyncGeneratorProvider, ProviderModelMixin, RaiseErrorMixin
from g4f.typing import Union, Optional, AsyncResult, Messages, ImagesType
from g4f.requests import StreamSession, raise_for_status
from g4f.providers.response import FinishReason, ToolCalls, Usage, Reasoning, ImageResponse
from g4f.errors import MissingAuthError, ResponseError
from g4f.image import to_data_uri
from g4f import debug
class OpenaiTemplate(AsyncGeneratorProvider, ProviderModelMixin, RaiseErrorMixin):
api_base = ""
supports_message_history = True
supports_system_message = True
default_model = ""
fallback_models = []
sort_models = True
verify = None
@classmethod
def get_models(cls, api_key: str = None, api_base: str = None) -> list[str]:
if not cls.models:
try:
headers = {}
if api_base is None:
api_base = cls.api_base
if api_key is not None:
headers["authorization"] = f"Bearer {api_key}"
response = requests.get(f"{api_base}/models", headers=headers, verify=cls.verify)
raise_for_status(response)
data = response.json()
data = data.get("data") if isinstance(data, dict) else data
cls.image_models = [model.get("id") for model in data if model.get("image")]
cls.models = [model.get("id") for model in data]
if cls.sort_models:
cls.models.sort()
except Exception as e:
debug.log(e)
return cls.fallback_models
return cls.models
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
proxy: str = None,
timeout: int = 120,
images: ImagesType = None,
api_key: str = None,
api_endpoint: str = None,
api_base: str = None,
temperature: float = None,
max_tokens: int = None,
top_p: float = None,
stop: Union[str, list[str]] = None,
stream: bool = False,
prompt: str = None,
headers: dict = None,
impersonate: str = None,
tools: Optional[list] = None,
extra_data: dict = {},
**kwargs
) -> AsyncResult:
if cls.needs_auth and api_key is None:
raise MissingAuthError('Add a "api_key"')
async with StreamSession(
proxy=proxy,
headers=cls.get_headers(stream, api_key, headers),
timeout=timeout,
impersonate=impersonate,
) as session:
model = cls.get_model(model, api_key=api_key, api_base=api_base)
if api_base is None:
api_base = cls.api_base
# Proxy for image generation feature
if prompt and model and model in cls.image_models:
data = {
"prompt": messages[-1]["content"] if prompt is None else prompt,
"model": model,
}
async with session.post(f"{api_base.rstrip('/')}/images/generations", json=data, ssl=cls.verify) as response:
data = await response.json()
cls.raise_error(data)
await raise_for_status(response)
yield ImageResponse([image["url"] for image in data["data"]], prompt)
return
if images is not None and messages:
if not model and hasattr(cls, "default_vision_model"):
model = cls.default_vision_model
last_message = messages[-1].copy()
last_message["content"] = [
*[{
"type": "image_url",
"image_url": {"url": to_data_uri(image)}
} for image, _ in images],
{
"type": "text",
"text": messages[-1]["content"]
}
]
messages[-1] = last_message
data = filter_none(
messages=messages,
model=model,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
stop=stop,
stream=stream,
tools=tools,
**extra_data
)
if api_endpoint is None:
api_endpoint = f"{api_base.rstrip('/')}/chat/completions"
async with session.post(api_endpoint, json=data, ssl=cls.verify) as response:
content_type = response.headers.get("content-type", "text/event-stream" if stream else "application/json")
if content_type.startswith("application/json"):
data = await response.json()
cls.raise_error(data)
await raise_for_status(response)
choice = data["choices"][0]
if "content" in choice["message"] and choice["message"]["content"]:
yield choice["message"]["content"].strip()
elif "tool_calls" in choice["message"]:
yield ToolCalls(choice["message"]["tool_calls"])
if "usage" in data:
yield Usage(**data["usage"])
if "finish_reason" in choice and choice["finish_reason"] is not None:
yield FinishReason(choice["finish_reason"])
return
elif content_type.startswith("text/event-stream"):
await raise_for_status(response)
first = True
is_thinking = 0
async for line in response.iter_lines():
if line.startswith(b"data: "):
chunk = line[6:]
if chunk == b"[DONE]":
break
data = json.loads(chunk)
cls.raise_error(data)
choice = data["choices"][0]
if "content" in choice["delta"] and choice["delta"]["content"]:
delta = choice["delta"]["content"]
if first:
delta = delta.lstrip()
if delta:
first = False
if is_thinking:
if "</think>" in delta:
yield Reasoning(None, f"Finished in {round(time.time()-is_thinking, 2)} seconds")
is_thinking = 0
else:
yield Reasoning(delta)
elif "<think>" in delta:
is_thinking = time.time()
yield Reasoning(None, "Is thinking...")
else:
yield delta
if "usage" in data and data["usage"]:
yield Usage(**data["usage"])
if "finish_reason" in choice and choice["finish_reason"] is not None:
yield FinishReason(choice["finish_reason"])
break
else:
await raise_for_status(response)
raise ResponseError(f"Not supported content-type: {content_type}")
@classmethod
def get_headers(cls, stream: bool, api_key: str = None, headers: dict = None) -> dict:
return {
"Accept": "text/event-stream" if stream else "application/json",
"Content-Type": "application/json",
**(
{"Authorization": f"Bearer {api_key}"}
if api_key is not None else {}
),
**({} if headers is None else headers)
}
class Feature(OpenaiTemplate):
url = "https://ahe.hopto.org"
working = True
verify = False
models = [
*list(set(g4f.Provider.OpenaiAccount.get_models())),
*g4f.Provider.HuggingChat.get_models(),
"MiniMax"
]
@classmethod
def get_model(cls, model, **kwargs):
if model == "MiniMax":
cls.api_base = f"{cls.url}/api/HailuoAI"
elif model in g4f.Provider.OpenaiAccount.get_models():
cls.api_base = f"{cls.url}/api/OpenaiAccount"
elif model in g4f.Provider.HuggingChat.get_models():
cls.api_base = f"{cls.url}/api/HuggingChat"
else:
cls.api_base = f"{cls.url}/v1"
return model
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
api_key: str = None,
**kwargs
) -> AsyncResult:
async for chunk in super().create_async_generator(model, messages, **kwargs):
yield chunk
g4f.Provider.__map__["Feature"] = Feature
app = g4f.api.create_app_with_demo_and_debug() |