mirror of
https://github.com/xtekky/gpt4free.git
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108 lines
4.4 KiB
Python
108 lines
4.4 KiB
Python
from __future__ import annotations
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import json
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import base64
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import random
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from ...typing import AsyncResult, Messages
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from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
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from ...errors import ModelNotFoundError
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from ...requests import StreamSession, raise_for_status
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from ...image import ImageResponse
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from .HuggingChat import HuggingChat
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class HuggingFace(AsyncGeneratorProvider, ProviderModelMixin):
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url = "https://huggingface.co/chat"
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working = True
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supports_message_history = True
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default_model = HuggingChat.default_model
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default_image_model = "black-forest-labs/FLUX.1-dev"
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models = [*HuggingChat.models, default_image_model]
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image_models = [default_image_model]
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model_aliases = HuggingChat.model_aliases
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@classmethod
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async def create_async_generator(
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cls,
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model: str,
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messages: Messages,
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stream: bool = True,
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proxy: str = None,
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api_base: str = "https://api-inference.huggingface.co",
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api_key: str = None,
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max_new_tokens: int = 1024,
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temperature: float = 0.7,
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prompt: str = None,
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**kwargs
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) -> AsyncResult:
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model = cls.get_model(model)
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headers = {
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'accept': '*/*',
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'accept-language': 'en',
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'cache-control': 'no-cache',
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'origin': 'https://huggingface.co',
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'pragma': 'no-cache',
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'priority': 'u=1, i',
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'referer': 'https://huggingface.co/chat/',
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'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
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'sec-ch-ua-mobile': '?0',
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'sec-ch-ua-platform': '"macOS"',
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'sec-fetch-dest': 'empty',
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'sec-fetch-mode': 'cors',
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'sec-fetch-site': 'same-origin',
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'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',
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}
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if api_key is not None:
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headers["Authorization"] = f"Bearer {api_key}"
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if model in cls.image_models:
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stream = False
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prompt = messages[-1]["content"] if prompt is None else prompt
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payload = {"inputs": prompt, "parameters": {"seed": random.randint(0, 2**32)}}
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else:
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params = {
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"return_full_text": False,
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"max_new_tokens": max_new_tokens,
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"temperature": temperature,
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**kwargs
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}
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payload = {"inputs": format_prompt(messages), "parameters": params, "stream": stream}
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async with StreamSession(
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headers=headers,
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proxy=proxy,
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timeout=600
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) as session:
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async with session.post(f"{api_base.rstrip('/')}/models/{model}", json=payload) as response:
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if response.status == 404:
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raise ModelNotFoundError(f"Model is not supported: {model}")
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await raise_for_status(response)
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if stream:
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first = True
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async for line in response.iter_lines():
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if line.startswith(b"data:"):
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data = json.loads(line[5:])
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if not data["token"]["special"]:
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chunk = data["token"]["text"]
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if first:
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first = False
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chunk = chunk.lstrip()
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if chunk:
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yield chunk
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else:
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if response.headers["content-type"].startswith("image/"):
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base64_data = base64.b64encode(b"".join([chunk async for chunk in response.iter_content()]))
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url = f"data:{response.headers['content-type']};base64,{base64_data.decode()}"
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yield ImageResponse(url, prompt)
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else:
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yield (await response.json())[0]["generated_text"].strip()
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def format_prompt(messages: Messages) -> str:
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system_messages = [message["content"] for message in messages if message["role"] == "system"]
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question = " ".join([messages[-1]["content"], *system_messages])
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history = "".join([
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f"<s>[INST]{messages[idx-1]['content']} [/INST] {message['content']}</s>"
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for idx, message in enumerate(messages)
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if message["role"] == "assistant"
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])
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return f"{history}<s>[INST] {question} [/INST]"
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