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https://github.com/xtekky/gpt4free.git
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0638cbc175
Updates for the response of the BackendApi Update of the demo model list Improve web search tool Moved copy_images to /image
78 lines
No EOL
3.4 KiB
Python
78 lines
No EOL
3.4 KiB
Python
from __future__ import annotations
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from ...providers.types import Messages
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from ...typing import ImagesType
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from ...requests import StreamSession, raise_for_status
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from ...errors import ModelNotSupportedError
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from ..template.OpenaiTemplate import OpenaiTemplate
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from .models import model_aliases
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from .HuggingChat import HuggingChat
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from ... import debug
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class HuggingFaceAPI(OpenaiTemplate):
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label = "HuggingFace (Inference API)"
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parent = "HuggingFace"
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url = "https://api-inference.huggingface.com"
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api_base = "https://api-inference.huggingface.co/v1"
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working = True
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needs_auth = True
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default_model = "meta-llama/Llama-3.2-11B-Vision-Instruct"
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default_vision_model = default_model
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vision_models = [default_vision_model, "Qwen/Qwen2-VL-7B-Instruct"]
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model_aliases = model_aliases
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@classmethod
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def get_models(cls, **kwargs):
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if not cls.models:
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HuggingChat.get_models()
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cls.models = HuggingChat.text_models.copy()
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for model in cls.vision_models:
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if model not in cls.models:
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cls.models.append(model)
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return cls.models
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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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api_base: str = None,
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max_tokens: int = 2048,
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max_inputs_lenght: int = 10000,
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images: ImagesType = None,
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**kwargs
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):
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if api_base is None:
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model_name = model
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if model in cls.model_aliases:
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model_name = cls.model_aliases[model]
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api_base = f"https://api-inference.huggingface.co/models/{model_name}/v1"
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async with StreamSession(
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timeout=30,
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) as session:
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async with session.get(f"https://huggingface.co/api/models/{model}") as response:
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if response.status == 404:
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raise ModelNotSupportedError(f"Model is not supported: {model} in: {cls.__name__}")
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await raise_for_status(response)
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model_data = await response.json()
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pipeline_tag = model_data.get("pipeline_tag")
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if images is None and pipeline_tag not in ("text-generation", "image-text-to-text"):
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raise ModelNotSupportedError(f"Model is not supported: {model} in: {cls.__name__} pipeline_tag: {pipeline_tag}")
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elif pipeline_tag != "image-text-to-text":
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raise ModelNotSupportedError(f"Model does not support images: {model} in: {cls.__name__} pipeline_tag: {pipeline_tag}")
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start = calculate_lenght(messages)
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if start > max_inputs_lenght:
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if len(messages) > 6:
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messages = messages[:3] + messages[-3:]
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if calculate_lenght(messages) > max_inputs_lenght:
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if len(messages) > 2:
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messages = [m for m in messages if m["role"] == "system"] + messages[-1:]
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if len(messages) > 1 and calculate_lenght(messages) > max_inputs_lenght:
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messages = [messages[-1]]
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debug.log(f"Messages trimmed from: {start} to: {calculate_lenght(messages)}")
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async for chunk in super().create_async_generator(model, messages, api_base=api_base, max_tokens=max_tokens, images=images, **kwargs):
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yield chunk
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def calculate_lenght(messages: Messages) -> int:
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return sum([len(message["content"]) + 16 for message in messages]) |