mirror of
https://github.com/xtekky/gpt4free.git
synced 2025-12-15 14:51:19 -08:00
263 lines
9.5 KiB
Python
263 lines
9.5 KiB
Python
from __future__ import annotations
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import json
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import random
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import requests
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from urllib.parse import quote_plus
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from typing import Optional
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from aiohttp import ClientSession
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from .helper import filter_none, format_image_prompt
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from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
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from ..typing import AsyncResult, Messages, ImagesType
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from ..image import to_data_uri
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from ..requests.raise_for_status import raise_for_status
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from ..requests.aiohttp import get_connector
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from ..providers.response import ImageResponse, ImagePreview, FinishReason, Usage, Reasoning
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DEFAULT_HEADERS = {
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'Accept': '*/*',
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'Accept-Language': 'en-US,en;q=0.9',
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'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36',
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}
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class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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label = "Pollinations AI"
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url = "https://pollinations.ai"
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working = True
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supports_stream = False
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supports_system_message = True
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supports_message_history = True
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# API endpoints
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text_api_endpoint = "https://text.pollinations.ai/openai"
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image_api_endpoint = "https://image.pollinations.ai/"
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# Models configuration
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default_model = "openai"
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default_image_model = "flux"
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default_vision_model = "gpt-4o"
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extra_image_models = ["midjourney", "dall-e-3", "flux-pro"]
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vision_models = [default_vision_model, "gpt-4o-mini"]
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reasoning_models = ['deepseek-reasoner', 'deepseek-r1']
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extra_text_models = ["claude", "claude-email", "p1"] + vision_models + reasoning_models
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model_aliases = {
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### Text Models ###
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"gpt-4o-mini": "openai",
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"gpt-4": "openai-large",
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"gpt-4o": "openai-large",
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"qwen-2.5-72b": "qwen",
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"qwen-2.5-coder-32b": "qwen-coder",
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"llama-3.3-70b": "llama",
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"mistral-nemo": "mistral",
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"gpt-4o-mini": "rtist",
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"gpt-4o": "searchgpt",
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"gpt-4o-mini": "p1",
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"deepseek-chat": "deepseek",
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"deepseek-chat": "claude-hybridspace",
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"llama-3.1-8b": "llamalight",
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"gpt-4o-vision": "gpt-4o",
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"gpt-4o-mini-vision": "gpt-4o-mini",
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"gpt-4o-mini": "claude",
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"deepseek-chat": "claude-email",
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"deepseek-r1": "deepseek-reasoner",
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### Image Models ###
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"sdxl-turbo": "turbo",
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}
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text_models = []
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@classmethod
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def get_models(cls, **kwargs):
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if not cls.image_models:
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url = "https://image.pollinations.ai/models"
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response = requests.get(url)
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raise_for_status(response)
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cls.image_models = response.json()
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cls.image_models = list(dict.fromkeys([*cls.image_models, *cls.extra_image_models]))
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if not cls.text_models:
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url = "https://text.pollinations.ai/models"
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response = requests.get(url)
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raise_for_status(response)
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original_text_models = [model.get("name") for model in response.json()]
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combined_text = cls.extra_text_models + [
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model for model in original_text_models
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if model not in cls.extra_text_models
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]
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cls.text_models = list(dict.fromkeys(combined_text))
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return list(dict.fromkeys([*cls.text_models, *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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proxy: str = None,
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prompt: str = None,
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width: int = 1024,
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height: int = 1024,
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seed: Optional[int] = None,
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nologo: bool = True,
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private: bool = False,
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enhance: bool = False,
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safe: bool = False,
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images: ImagesType = None,
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temperature: float = None,
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presence_penalty: float = None,
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top_p: float = 1,
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frequency_penalty: float = None,
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response_format: Optional[dict] = None,
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cache: bool = False,
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**kwargs
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) -> AsyncResult:
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if images is not None and not model:
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model = cls.default_vision_model
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model = cls.get_model(model)
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if not cache and seed is None:
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seed = random.randint(0, 100000)
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if model in cls.image_models:
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async for chunk in cls._generate_image(
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model=model,
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prompt=format_image_prompt(messages, prompt),
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proxy=proxy,
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width=width,
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height=height,
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seed=seed,
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nologo=nologo,
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private=private,
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enhance=enhance,
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safe=safe
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):
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yield chunk
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else:
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async for result in cls._generate_text(
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model=model,
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messages=messages,
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images=images,
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proxy=proxy,
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temperature=temperature,
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presence_penalty=presence_penalty,
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top_p=top_p,
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frequency_penalty=frequency_penalty,
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response_format=response_format,
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seed=seed,
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cache=cache,
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):
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yield result
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@classmethod
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async def _generate_image(
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cls,
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model: str,
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prompt: str,
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proxy: str,
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width: int,
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height: int,
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seed: Optional[int],
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nologo: bool,
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private: bool,
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enhance: bool,
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safe: bool
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) -> AsyncResult:
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params = {
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"seed": seed,
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"width": width,
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"height": height,
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"model": model,
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"nologo": nologo,
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"private": private,
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"enhance": enhance,
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"safe": safe
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}
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params = {k: json.dumps(v) if isinstance(v, bool) else str(v) for k, v in params.items() if v is not None}
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params = "&".join( "%s=%s" % (key, quote_plus(params[key]))
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for key in params.keys())
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url = f"{cls.image_api_endpoint}prompt/{quote_plus(prompt)}?{params}"
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yield ImagePreview(url, prompt)
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async with ClientSession(headers=DEFAULT_HEADERS, connector=get_connector(proxy=proxy)) as session:
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async with session.head(url) as response:
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if response.status != 500: # Server is busy
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await raise_for_status(response)
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yield ImageResponse(str(response.url), prompt)
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@classmethod
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async def _generate_text(
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cls,
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model: str,
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messages: Messages,
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images: Optional[ImagesType],
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proxy: str,
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temperature: float,
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presence_penalty: float,
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top_p: float,
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frequency_penalty: float,
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response_format: Optional[dict],
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seed: Optional[int],
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cache: bool
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) -> AsyncResult:
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jsonMode = False
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if response_format is not None and "type" in response_format:
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if response_format["type"] == "json_object":
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jsonMode = True
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if images is not None and messages:
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last_message = messages[-1].copy()
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last_message["content"] = [
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*[{
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"type": "image_url",
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"image_url": {"url": to_data_uri(image)}
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} for image, _ in images],
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{
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"type": "text",
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"text": messages[-1]["content"]
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}
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]
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messages[-1] = last_message
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async with ClientSession(headers=DEFAULT_HEADERS, connector=get_connector(proxy=proxy)) as session:
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data = {
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"messages": messages,
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"model": model,
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"temperature": temperature,
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"presence_penalty": presence_penalty,
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"top_p": top_p,
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"frequency_penalty": frequency_penalty,
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"jsonMode": jsonMode,
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"stream": False,
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"seed": seed,
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"cache": cache
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}
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async with session.post(cls.text_api_endpoint, json=filter_none(**data)) as response:
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await raise_for_status(response)
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async for line in response.content:
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decoded_chunk = line.decode(errors="replace")
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if "data: [DONE]" in decoded_chunk:
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break
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try:
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json_str = decoded_chunk.replace("data:", "").strip()
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data = json.loads(json_str)
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choice = data["choices"][0]
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message = choice.get("message") or choice.get("delta", {})
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# Handle reasoning content
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if model in cls.reasoning_models:
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if "reasoning_content" in message:
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yield Reasoning(status=message["reasoning_content"].strip())
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if "usage" in data:
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yield Usage(**data["usage"])
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content = message.get("content", "")
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if content:
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yield content.replace("\\(", "(").replace("\\)", ")")
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if "finish_reason" in choice and choice["finish_reason"]:
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yield FinishReason(choice["finish_reason"])
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break
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except json.JSONDecodeError:
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yield decoded_chunk.strip()
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except Exception as e:
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yield FinishReason("error")
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break
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