mirror of
https://github.com/xtekky/gpt4free.git
synced 2025-09-26 20:31:14 +08:00
507 lines
21 KiB
Python
507 lines
21 KiB
Python
from __future__ import annotations
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import time
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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, quote_plus
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from typing import Optional
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from aiohttp import ClientSession, ClientTimeout
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from .helper import filter_none, format_media_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 MissingAuthError
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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 import use_aspect_ratio
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from ..providers.response import ImageResponse, Reasoning, TitleGeneration, SuggestedFollowups
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from ..tools.media import render_messages
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from ..config import STATIC_URL
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from .template.OpenaiTemplate import read_response
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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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FOLLOWUPS_TOOLS = [{
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"type": "function",
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"function": {
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"name": "options",
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"description": "Provides options for the conversation",
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"parameters": {
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"properties": {
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"title": {
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"title": "Conversation title. Prefixed with one or more emojies",
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"type": "string"
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},
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"followups": {
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"items": {
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"type": "string"
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},
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"title": "Suggested 4 Followups (only user messages)",
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"type": "array"
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}
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},
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"title": "Conversation",
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"type": "object"
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}
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}
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}]
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FOLLOWUPS_DEVELOPER_MESSAGE = [{
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"role": "developer",
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"content": "Provide conversation options.",
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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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login_url = "https://auth.pollinations.ai"
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active_by_default = True
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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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fallback_model = "deepseek"
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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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default_voice = "alloy"
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text_models = [default_model, "evil"]
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image_models = [default_image_model, "turbo", "kontext"]
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audio_models = {default_audio_model: []}
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vision_models = [default_vision_model]
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_models_loaded = False
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model_aliases = {
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"gpt-4.1-nano": "openai-fast",
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"llama-4-scout": "llamascout",
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"deepseek-r1": "deepseek-reasoning",
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"sdxl-turbo": "turbo",
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"gpt-image": "gptimage",
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"flux-dev": "flux",
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"flux-schnell": "flux",
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"flux-pro": "flux",
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"flux": "flux",
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"flux-kontext": "kontext",
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}
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swap_model_aliases = {v: k for k, v in model_aliases.items()}
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@classmethod
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def get_models(cls, **kwargs):
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def get_alias(model: dict) -> str:
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alias = model.get("name")
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if (model.get("aliases")):
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alias = model.get("aliases")[0]
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elif alias in cls.swap_model_aliases:
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alias = cls.swap_model_aliases[alias]
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return alias.replace("-instruct", "").replace("qwen-", "qwen").replace("qwen", "qwen-")
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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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image_models = cls.image_models.copy() # 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 new_image_models:
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if model not in image_models:
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image_models.append(model)
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cls.image_models = image_models
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text_response = requests.get("https://g4f.dev/api/pollinations.ai/models")
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if not text_response.ok:
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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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for alias, model in cls.model_aliases.items():
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if model in cls.audio_models and alias not in cls.audio_models:
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cls.audio_models.update({alias: {}})
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cls.vision_models.extend([
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get_alias(model)
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for model in models
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if model.get("vision") and get_alias(model) not in cls.vision_models
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])
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for model in models:
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alias = get_alias(model)
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if alias not in cls.text_models:
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cls.text_models.append(alias)
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if alias != model.get("name"):
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cls.model_aliases[alias] = model.get("name")
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elif model.get("name") not in cls.text_models:
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cls.text_models.append(model.get("name"))
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cls.live += 1
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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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finally:
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cls._models_loaded = True
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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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if cls.default_audio_model in cls.audio_models:
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all_models.extend(cls.audio_models[cls.default_audio_model])
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return list(dict.fromkeys(all_models))
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@classmethod
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def get_grouped_models(cls) -> dict[str, list[str]]:
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cls.get_models()
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return [
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{"group": "Text Generation", "models": cls.text_models},
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{"group": "Image Generation", "models": cls.image_models},
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{"group": "Audio Generation", "models": list(cls.audio_models.keys())},
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{"group": "Audio Voices", "models": cls.audio_models.get(cls.default_audio_model, [])},
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]
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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 = None,
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referrer: str = STATIC_URL,
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api_key: str = None,
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extra_body: dict = None,
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# Image generation parameters
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prompt: str = None,
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aspect_ratio: str = None,
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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 = None,
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safe: bool = False,
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transparent: 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",
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"logit_bias", "voice", "modalities", "audio"],
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**kwargs
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) -> AsyncResult:
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if cache is None:
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cache = kwargs.get("action") == "next"
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if extra_body is None:
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extra_body = {}
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if not model:
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has_audio = "audio" in kwargs or "audio" in kwargs.get("modalities", [])
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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 = cls.default_audio_model if has_audio else model
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elif cls._models_loaded or cls.get_models():
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if model in cls.model_aliases:
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model = cls.model_aliases[model]
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debug.log(f"Using model: {model}")
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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="gptimage" if model == "transparent" else model,
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prompt=format_media_prompt(messages, prompt),
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media=media,
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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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transparent=transparent or model == "transparent",
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n=n,
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referrer=referrer,
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api_key=api_key
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):
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yield chunk
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else:
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if prompt is not None and len(messages) == 1:
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messages = [{
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"role": "user",
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"content": prompt
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}]
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if model and model in cls.audio_models[cls.default_audio_model]:
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kwargs["audio"] = {
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"voice": model,
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}
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model = cls.default_audio_model
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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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referrer=referrer,
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api_key=api_key,
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extra_body=extra_body,
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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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media: MediaListType,
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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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transparent: bool,
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n: int,
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referrer: str,
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api_key: str,
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timeout: int = 120
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) -> AsyncResult:
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if enhance is None:
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enhance = True if model == "flux" else False
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params = {
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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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"referrer": referrer
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}
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if transparent:
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params["transparent"] = "true"
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image = [data for data, _ in media if isinstance(data, str) and data.startswith("http")] if media else []
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if image:
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params["image"] = ",".join(image)
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if model != "gptimage":
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params = use_aspect_ratio({
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"width": width,
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"height": height,
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**params
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}, "1:1" if aspect_ratio is None else aspect_ratio)
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query = "&".join(f"{k}={quote(str(v))}" for k, v in params.items() if v is not None)
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encoded_prompt = prompt.strip(". \n")
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if model == "gptimage" and aspect_ratio is not None:
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encoded_prompt = f"{encoded_prompt} aspect-ratio: {aspect_ratio}"
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encoded_prompt = quote_plus(encoded_prompt)[:4096 - len(cls.image_api_endpoint) - len(query) - 8].rstrip("%")
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url = f"{cls.image_api_endpoint}prompt/{encoded_prompt}?{query}"
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def get_url_with_seed(i: int, seed: Optional[int] = None):
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if model == "gptimage":
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return url
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if i == 0:
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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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headers = {"referer": referrer}
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if api_key:
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headers["authorization"] = f"Bearer {api_key}"
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async with ClientSession(
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headers=DEFAULT_HEADERS,
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connector=get_connector(proxy=proxy),
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timeout=ClientTimeout(timeout)
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) as session:
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responses = set()
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yield Reasoning(label=f"Generating {n} {'image' if n == 1 else 'images'}")
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finished = 0
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start = time.time()
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async def get_image(responses: set, i: int, seed: Optional[int] = None):
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try:
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async with session.get(get_url_with_seed(i, seed), allow_redirects=False,
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headers=headers) as response:
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await raise_for_status(response)
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except Exception as e:
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responses.add(e)
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debug.error(f"Error fetching image: {e}")
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if response.headers['content-type'].startswith("image/"):
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responses.add(ImageResponse(str(response.url), prompt, {"headers": headers}))
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else:
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t_ = await response.text()
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debug.error(f"UnHandel Error fetching image: {t_}")
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responses.add(t_)
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tasks: list[asyncio.Task] = []
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for i in range(int(n)):
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tasks.append(asyncio.create_task(get_image(responses, i, seed)))
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while finished < n or len(responses) > 0:
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while len(responses) > 0:
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item = responses.pop()
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if isinstance(item, Exception):
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if finished < 2:
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yield Reasoning(status="")
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for task in tasks:
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task.cancel()
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if cls.login_url in str(item):
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raise MissingAuthError(item)
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raise item
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else:
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finished += 1
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yield Reasoning(
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label=f"Image {finished}/{n} failed after {time.time() - start:.2f}s: {item}")
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else:
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finished += 1
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yield Reasoning(label=f"Image {finished}/{n} generated in {time.time() - start:.2f}s")
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yield item
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await asyncio.sleep(1)
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yield Reasoning(status="")
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await asyncio.gather(*tasks)
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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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referrer: str,
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api_key: str,
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extra_body: dict,
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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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async with ClientSession(headers=DEFAULT_HEADERS, connector=get_connector(proxy=proxy)) as session:
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extra_body.update({param: kwargs[param] for param in extra_parameters if param in kwargs})
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if model in cls.audio_models:
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if "audio" in extra_body and extra_body.get("audio", {}).get("voice") is None:
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extra_body["audio"]["voice"] = cls.default_voice
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elif "audio" not in extra_body:
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extra_body["audio"] = {"voice": cls.default_voice}
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if extra_body.get("audio", {}).get("format") is None:
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extra_body["audio"]["format"] = "mp3"
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stream = False
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if "modalities" not in extra_body:
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extra_body["modalities"] = ["text", "audio"]
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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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response_format=response_format,
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stream=stream,
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seed=None if model == "grok" else seed,
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referrer=referrer,
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**extra_body
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)
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headers = {"referer": referrer}
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if api_key:
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headers["authorization"] = f"Bearer {api_key}"
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async with session.post(cls.openai_endpoint, json=data, headers=headers) as response:
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if response.status in (400, 500):
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debug.error(f"Error: {response.status} - Bad Request: {data}")
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full_resposne = []
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async for chunk in read_response(response, stream, format_media_prompt(messages), cls.get_dict(),
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kwargs.get("download_media", True)):
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if isinstance(chunk, str):
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full_resposne.append(chunk)
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yield chunk
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if full_resposne:
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full_content = "".join(full_resposne)
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if kwargs.get("action") == "next" and model != "evil":
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tool_messages = []
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for message in messages:
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if message.get("role") == "user":
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if isinstance(message.get("content"), str):
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tool_messages.append({"role": "user", "content": message.get("content")})
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elif isinstance(message.get("content"), list):
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next_value = message.get("content").pop()
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if isinstance(next_value, dict):
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next_value = next_value.get("text")
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if next_value:
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tool_messages.append({"role": "user", "content": next_value})
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tool_messages.append({"role": "assistant", "content": full_content})
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data = {
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"model": "openai",
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"messages": tool_messages + FOLLOWUPS_DEVELOPER_MESSAGE,
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"tool_choice": "required",
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"tools": FOLLOWUPS_TOOLS
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}
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async with session.post(cls.openai_endpoint, json=data, headers=headers) as response:
|
|
try:
|
|
await raise_for_status(response)
|
|
tool_calls = (await response.json()).get("choices", [{}])[0].get("message", {}).get(
|
|
"tool_calls", [])
|
|
if tool_calls:
|
|
arguments = json.loads(tool_calls.pop().get("function", {}).get("arguments"))
|
|
if arguments.get("title"):
|
|
yield TitleGeneration(arguments.get("title"))
|
|
if arguments.get("followups"):
|
|
yield SuggestedFollowups(arguments.get("followups"))
|
|
except Exception as e:
|
|
debug.error("Error generating title and followups:", e)
|