mirror of
https://github.com/xtekky/gpt4free.git
synced 2025-10-19 06:34:38 +08:00
93986d15f6
- Fixed duplicate model entries in Blackbox provider model_aliases - Added meta-llama- to llama- name cleaning in Cloudflare provider - Enhanced PollinationsAI provider with improved vision model detection - Added reasoning support to PollinationsAI provider - Fixed HuggingChat authentication to include headers and impersonate - Removed unused max_inputs_length parameter from HuggingFaceAPI - Renamed extra_data to extra_body for consistency across providers - Added Puter provider with grouped model support - Enhanced AnyProvider with grouped model display and better model organization - Fixed model cleaning in AnyProvider to handle more model name variations - Added api_key handling for HuggingFace providers in AnyProvider - Added see_stream helper function to parse event streams - Updated GUI server to handle JsonConversation properly - Fixed aspect ratio handling in image generation functions - Added ResponsesConfig and ClientResponse for new API endpoint - Updated requirements to include markitdown
127 lines
4.5 KiB
Python
127 lines
4.5 KiB
Python
from __future__ import annotations
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import requests
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from ...typing import AsyncResult, Messages
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from ...requests import StreamSession, raise_for_status
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from ...providers.response import ImageResponse
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from ..template import OpenaiTemplate
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from ..helper import format_image_prompt
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class DeepInfra(OpenaiTemplate):
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url = "https://deepinfra.com"
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login_url = "https://deepinfra.com/dash/api_keys"
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api_base = "https://api.deepinfra.com/v1/openai"
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working = True
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needs_auth = True
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default_model = "meta-llama/Meta-Llama-3.1-70B-Instruct"
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default_image_model = "stabilityai/sd3.5"
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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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url = 'https://api.deepinfra.com/models/featured'
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response = requests.get(url)
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models = response.json()
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cls.models = []
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cls.image_models = []
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for model in models:
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if model["type"] == "text-generation":
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cls.models.append(model['model_name'])
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elif model["reported_type"] == "text-to-image":
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cls.image_models.append(model['model_name'])
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cls.models.extend(cls.image_models)
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return cls.models
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@classmethod
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def get_image_models(cls, **kwargs):
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if not cls.image_models:
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cls.get_models()
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return cls.image_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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stream: bool,
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prompt: str = None,
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temperature: float = 0.7,
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max_tokens: int = 1028,
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**kwargs
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) -> AsyncResult:
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if model in cls.get_image_models():
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yield cls.create_async_image(
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format_image_prompt(messages, prompt),
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model,
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**kwargs
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)
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return
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headers = {
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'Accept-Encoding': 'gzip, deflate, br',
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'Accept-Language': 'en-US',
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'Origin': 'https://deepinfra.com',
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'Referer': 'https://deepinfra.com/',
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'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
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'X-Deepinfra-Source': 'web-embed',
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}
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async for chunk in super().create_async_generator(
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model, messages,
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stream=stream,
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temperature=temperature,
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max_tokens=max_tokens,
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headers=headers,
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**kwargs
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):
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yield chunk
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@classmethod
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async def create_async_image(
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cls,
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prompt: str,
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model: str,
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api_key: str = None,
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api_base: str = "https://api.deepinfra.com/v1/inference",
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proxy: str = None,
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timeout: int = 180,
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extra_body: dict = {},
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**kwargs
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) -> ImageResponse:
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headers = {
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'Accept-Encoding': 'gzip, deflate, br',
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'Accept-Language': 'en-US',
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'Connection': 'keep-alive',
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'Origin': 'https://deepinfra.com',
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'Referer': 'https://deepinfra.com/',
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'Sec-Fetch-Dest': 'empty',
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'Sec-Fetch-Mode': 'cors',
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'Sec-Fetch-Site': 'same-site',
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'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
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'X-Deepinfra-Source': 'web-embed',
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'sec-ch-ua': '"Google Chrome";v="119", "Chromium";v="119", "Not?A_Brand";v="24"',
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'sec-ch-ua-mobile': '?0',
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'sec-ch-ua-platform': '"macOS"',
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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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async with StreamSession(
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proxies={"all": proxy},
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headers=headers,
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timeout=timeout
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) as session:
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model = cls.get_model(model)
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data = {"prompt": prompt, **extra_body}
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data = {"input": data} if model == cls.default_model else data
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async with session.post(f"{api_base.rstrip('/')}/{model}", json=data) as response:
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await raise_for_status(response)
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data = await response.json()
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images = data.get("output", data.get("images", data.get("image_url")))
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if not images:
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raise RuntimeError(f"Response: {data}")
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images = images[0] if len(images) == 1 else images
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return ImageResponse(images, prompt) |