mirror of
https://github.com/xtekky/gpt4free.git
synced 2025-12-06 02:30:41 -08:00
06546649db
* **Provider/Blackbox.py**
* Raise `RateLimitError` when `"You have reached your request limit for the hour"` substring is detected
* **Provider/Copilot.py**
* Convert class to `AsyncGeneratorProvider`; rename `create_completion` → `create_async_generator`
* Swap `curl_cffi.requests.Session` for `AsyncSession`; reduce default timeout to **30 s**
* Fully async websocket flow (`await session.ws_connect`, `await wss.send/recv/close`)
* Emit new response types: `TitleGeneration`, `SourceLink`, aggregated `Sources`
* Track request completion with `done` flag; collect citations in `sources` dict
* **Provider/DuckDuckGo.py**
* Replace `duckduckgo_search.DDGS` with `duckai.DuckAI`
* Change base class to `AbstractProvider`; drop nodriver‑based auth
* **Provider/PollinationsAI.py**
* Re‑build text/audio model lists ensuring uniqueness; remove unused `extra_text_models`
* Fix image seed logic (`i==1` for first retry); propagate streaming `error` field via `ResponseError`
* **Provider/hf_space**
* **New file** `LMArenaProvider.py` implementing async queue/stream client
* Register `LMArenaProvider` in `hf_space/__init__.py`; delete `G4F` import
* **Provider/needs_auth/CopilotAccount.py**
* Inherit order changed to `Copilot, AsyncAuthedProvider`
* Refactor token & cookie propagation; add `cookies_to_dict` helper
* **Provider/needs_auth/OpenaiChat.py**
* Parse reasoning thoughts/summary; yield `Reasoning` responses
* Tighten access‑token validation and nodriver JS evaluations (`return_by_value`)
* Extend `Conversation` with `p` and `thoughts_summary`
* **providers/response.py**
* Add `SourceLink` response class returning single formatted citation link
* **providers/base_provider.py**
* Serialize `AuthResult` with custom `json.dump` to handle non‑serializable fields
* Gracefully skip empty cache files when loading auth data
* **image/copy_images.py**
* Ignore file extensions longer than 4 chars when inferring type
* **requests/__init__.py**
* Use `return_by_value=True` for `navigator.userAgent` extraction
* **models.py**
* Remove `G4F` from model provider lists; update `janus_pro_7b` best providers
* **GUI server/api.py**
* Stream `SuggestedFollowups` to client (`"suggestions"` event)
* **GUI static assets**
* **style.css**: bold chat title, add `.suggestions` styles, remove padding from `.chat-body`
* **chat.v1.js**
* Capture `suggestions` packets, render buttons, and send as quick replies
* Re‑order finish‑reason logic; adjust token count placement and system‑prompt toggling
* **chat-top-panel / footer** interactions updated accordingly
* **gui/client/static/js/chat.v1.js** & **css** further UI refinements (scroll handling, token counting, hide prompt toggle)
* Minor updates across multiple files to match new async interfaces and headers (`userAgent`, `raise_for_status`)
361 lines
14 KiB
Python
361 lines
14 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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import asyncio
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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, MediaListType
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from ..image import is_data_an_audio
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from ..errors import ModelNotFoundError, ResponseError
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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 ..image.copy_images import save_response_media
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from ..image import use_aspect_ratio
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from ..providers.response import FinishReason, Usage, ToolCalls, ImageResponse
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from ..tools.media import render_messages
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from .. import debug
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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/133.0.0.0 Safari/537.36",
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"referer": "https://pollinations.ai/",
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"origin": "https://pollinations.ai",
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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_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"
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openai_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 = default_model
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default_audio_model = "openai-audio"
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text_models = [default_model]
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image_models = [default_image_model]
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audio_models = [default_audio_model]
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extra_image_models = ["flux-pro", "flux-dev", "flux-schnell", "midjourney", "dall-e-3", "turbo"]
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vision_models = [default_vision_model, "gpt-4o-mini", "openai", "openai-large", "searchgpt"]
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_models_loaded = False
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# https://github.com/pollinations/pollinations/blob/master/text.pollinations.ai/generateTextPortkey.js#L15
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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-coder-32b": "qwen-coder",
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"llama-3.3-70b": "llama",
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"llama-4-scout": "llamascout",
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"mistral-nemo": "mistral",
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"llama-3.1-8b": "llamalight",
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"llama-3.3-70b": "llama-scaleway",
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"phi-4": "phi",
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"gemini-2.0": "gemini",
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"gemini-2.0-flash": "gemini",
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"gemini-2.0-flash-thinking": "gemini-thinking",
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"deepseek-r1": "deepseek-reasoning-large",
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"deepseek-r1": "deepseek-reasoning",
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"deepseek-v3": "deepseek",
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"llama-3.2-11b": "llama-vision",
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"gpt-4o-audio": "openai-audio",
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### Image Models ###
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"sdxl-turbo": "turbo",
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}
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@classmethod
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def get_models(cls, **kwargs):
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if not cls._models_loaded:
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try:
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# Update of image models
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image_response = requests.get("https://image.pollinations.ai/models")
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if image_response.ok:
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new_image_models = image_response.json()
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else:
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new_image_models = []
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# Combine image models without duplicates
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all_image_models = [cls.default_image_model] # Start with default model
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# Add extra image models if not already in the list
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for model in cls.extra_image_models + new_image_models:
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if model not in all_image_models:
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all_image_models.append(model)
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cls.image_models = all_image_models
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text_response = requests.get("https://text.pollinations.ai/models")
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text_response.raise_for_status()
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models = text_response.json()
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# Purpose of audio models
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cls.audio_models = {
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model.get("name"): model.get("voices")
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for model in models
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if "output_modalities" in model and "audio" in model["output_modalities"]
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}
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# Create a set of unique text models starting with default model
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unique_text_models = cls.text_models.copy()
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# Add models from vision_models
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unique_text_models.extend(cls.vision_models)
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# Add models from the API response
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for model in models:
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model_name = model.get("name")
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if model_name and "input_modalities" in model and "text" in model["input_modalities"]:
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unique_text_models.append(model_name)
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# Convert to list and update text_models
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cls.text_models = list(dict.fromkeys(unique_text_models))
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cls._models_loaded = True
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except Exception as e:
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# Save default models in case of an error
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if not cls.text_models:
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cls.text_models = [cls.default_model]
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if not cls.image_models:
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cls.image_models = [cls.default_image_model]
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debug.error(f"Failed to fetch models: {e}")
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# Return unique models across all categories
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all_models = cls.text_models.copy()
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all_models.extend(cls.image_models)
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all_models.extend(cls.audio_models.keys())
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return list(dict.fromkeys(all_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 = True,
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proxy: str = None,
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cache: bool = False,
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# Image generation parameters
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prompt: str = None,
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aspect_ratio: str = "1:1",
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width: int = None,
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height: int = None,
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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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n: int = 1,
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# Text generation parameters
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media: MediaListType = None,
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temperature: float = None,
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presence_penalty: float = None,
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top_p: float = None,
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frequency_penalty: float = None,
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response_format: Optional[dict] = None,
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extra_parameters: list[str] = ["tools", "parallel_tool_calls", "tool_choice", "reasoning_effort", "logit_bias", "voice", "modalities", "audio"],
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**kwargs
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) -> AsyncResult:
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# Load model list
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cls.get_models()
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if not model:
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has_audio = "audio" in kwargs
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if not has_audio and media is not None:
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for media_data, filename in media:
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if is_data_an_audio(media_data, filename):
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has_audio = True
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break
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model = next(iter(cls.audio_models)) if has_audio else model
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try:
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model = cls.get_model(model)
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except ModelNotFoundError:
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if model not in cls.image_models:
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raise
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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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aspect_ratio=aspect_ratio,
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width=width,
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height=height,
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seed=seed,
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cache=cache,
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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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n=n
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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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media=media,
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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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stream=stream,
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extra_parameters=extra_parameters,
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**kwargs
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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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aspect_ratio: str,
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width: int,
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height: int,
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seed: Optional[int],
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cache: bool,
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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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n: int
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) -> AsyncResult:
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params = use_aspect_ratio({
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"width": width,
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"height": height,
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"model": model,
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"nologo": str(nologo).lower(),
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"private": str(private).lower(),
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"enhance": str(enhance).lower(),
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"safe": str(safe).lower()
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}, aspect_ratio)
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query = "&".join(f"{k}={quote_plus(str(v))}" for k, v in params.items() if v is not None)
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prompt = quote_plus(prompt)[:2048-256-len(query)]
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url = f"{cls.image_api_endpoint}prompt/{prompt}?{query}"
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def get_image_url(i: int, seed: Optional[int] = None):
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if i == 1:
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if not cache and seed is None:
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seed = random.randint(0, 2**32)
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else:
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seed = random.randint(0, 2**32)
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return f"{url}&seed={seed}" if seed else url
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async with ClientSession(headers=DEFAULT_HEADERS, connector=get_connector(proxy=proxy)) as session:
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async def get_image(i: int, seed: Optional[int] = None):
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async with session.get(get_image_url(i, seed), allow_redirects=False) as response:
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try:
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await raise_for_status(response)
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except Exception as e:
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debug.error(f"Error fetching image: {e}")
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return str(response.url)
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return str(response.url)
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yield ImageResponse(await asyncio.gather(*[
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get_image(i, seed) for i in range(int(n))
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]), 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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media: MediaListType,
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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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stream: bool,
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extra_parameters: list[str],
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**kwargs
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) -> AsyncResult:
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if not cache and seed is None:
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seed = random.randint(0, 2**32)
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json_mode = False
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if response_format and response_format.get("type") == "json_object":
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json_mode = True
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async with ClientSession(headers=DEFAULT_HEADERS, connector=get_connector(proxy=proxy)) as session:
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if model in cls.audio_models:
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url = cls.text_api_endpoint
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stream = False
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else:
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url = cls.openai_endpoint
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extra_parameters = {param: kwargs[param] for param in extra_parameters if param in kwargs}
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data = filter_none(**{
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"messages": list(render_messages(messages, media)),
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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": json_mode,
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"stream": stream,
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"seed": seed,
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"cache": cache,
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**extra_parameters
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})
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async with session.post(url, json=data) as response:
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await raise_for_status(response)
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async for chunk in save_response_media(response, format_image_prompt(messages), [model]):
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yield chunk
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return
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if response.headers["content-type"].startswith("text/plain"):
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yield await response.text()
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return
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elif response.headers["content-type"].startswith("text/event-stream"):
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async for line in response.content:
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if line.startswith(b"data: "):
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if line[6:].startswith(b"[DONE]"):
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break
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result = json.loads(line[6:])
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if "error" in result:
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raise ResponseError(result["error"].get("message", result["error"]))
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if "usage" in result:
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yield Usage(**result["usage"])
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choices = result.get("choices", [{}])
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choice = choices.pop() if choices else {}
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content = choice.get("delta", {}).get("content")
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if content:
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yield content
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finish_reason = choice.get("finish_reason")
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if finish_reason:
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yield FinishReason(finish_reason)
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return
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result = await response.json()
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if "choices" in result:
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choice = result["choices"][0]
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message = choice.get("message", {})
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content = message.get("content", "")
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if content:
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yield content
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if "tool_calls" in message:
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yield ToolCalls(message["tool_calls"])
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else:
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raise ResponseError(result)
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if "usage" in result:
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yield Usage(**result["usage"])
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finish_reason = choice.get("finish_reason")
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if finish_reason:
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yield FinishReason(finish_reason)
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