gpt4free/g4f/Provider/HuggingFace.py

from __future__ import annotations

import json
from aiohttp import ClientSession, BaseConnector

from ..typing import AsyncResult, Messages
from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
from .helper import get_connector
from ..errors import RateLimitError, ModelNotFoundError
from ..requests.raise_for_status import raise_for_status

class HuggingFace(AsyncGeneratorProvider, ProviderModelMixin):
    url = "https://huggingface.co/chat"
    working = True
    needs_auth = True
    supports_message_history = True
    default_model = "meta-llama/Meta-Llama-3.1-70B-Instruct"
    models = [
        'meta-llama/Meta-Llama-3.1-70B-Instruct',
        'meta-llama/Meta-Llama-3.1-405B-Instruct-FP8',
        'CohereForAI/c4ai-command-r-plus',
        'mistralai/Mixtral-8x7B-Instruct-v0.1',
        'NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO',
        '01-ai/Yi-1.5-34B-Chat',
        'mistralai/Mistral-7B-Instruct-v0.3',
        'microsoft/Phi-3-mini-4k-instruct',
    ]
    
    model_aliases = {
        "llama-3.1-70b": "meta-llama/Meta-Llama-3.1-70B-Instruct",
        "llama-3.1-405b": "meta-llama/Meta-Llama-3.1-405B-Instruct-FP8",
        "command-r-plus": "CohereForAI/c4ai-command-r-plus",
        "mixtral-8x7b": "mistralai/Mixtral-8x7B-Instruct-v0.1",
        "mixtral-8x7b": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
        "yi-1.5-34b": "01-ai/Yi-1.5-34B-Chat",
        "mistral-7b": "mistralai/Mistral-7B-Instruct-v0.3",
        "phi-3-mini-4k": "microsoft/Phi-3-mini-4k-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
    async def create_async_generator(
        cls,
        model: str,
        messages: Messages,
        stream: bool = True,
        proxy: str = None,
        connector: BaseConnector = None,
        api_base: str = "https://api-inference.huggingface.co",
        api_key: str = None,
        max_new_tokens: int = 1024,
        temperature: float = 0.7,
        **kwargs
    ) -> AsyncResult:
        model = cls.get_model(model)
        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',
        }
        if api_key is not None:
            headers["Authorization"] = f"Bearer {api_key}"
        
        params = {
            "return_full_text": False,
            "max_new_tokens": max_new_tokens,
            "temperature": temperature,
            **kwargs
        }
        payload = {"inputs": format_prompt(messages), "parameters": params, "stream": stream}
        
        async with ClientSession(
            headers=headers,
            connector=get_connector(connector, proxy)
        ) as session:
            async with session.post(f"{api_base.rstrip('/')}/models/{model}", json=payload) as response:
                if response.status == 404:
                    raise ModelNotFoundError(f"Model is not supported: {model}")
                await raise_for_status(response)
                if stream:
                    first = True
                    async for line in response.content:
                        if line.startswith(b"data:"):
                            data = json.loads(line[5:])
                            if not data["token"]["special"]:
                                chunk = data["token"]["text"]
                                if first:
                                    first = False
                                    chunk = chunk.lstrip()
                                yield chunk
                else:
                    yield (await response.json())[0]["generated_text"].strip()

def format_prompt(messages: Messages) -> str:
    system_messages = [message["content"] for message in messages if message["role"] == "system"]
    question = " ".join([messages[-1]["content"], *system_messages])
    history = "".join([
        f"<s>[INST]{messages[idx-1]['content']} [/INST] {message['content']}</s>"
        for idx, message in enumerate(messages)
        if message["role"] == "assistant"
    ])
    return f"{history}<s>[INST] {question} [/INST]"