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* refactor(g4f/Provider/Airforce.py): improve model handling and filtering - Add hidden_models set to exclude specific models - Add evil alias for uncensored model handling - Extend filtering for model-specific response tokens - Add response buffering for streamed content - Update model fetching with error handling * refactor(g4f/Provider/Blackbox.py): improve caching and model handling - Add caching system for validated values with file-based storage - Rename 'flux' model to 'ImageGeneration' and update references - Add temperature, top_p and max_tokens parameters to generator - Simplify HTTP headers and remove redundant options - Add model alias mapping for ImageGeneration - Add file system utilities for cache management * feat(g4f/Provider/RobocodersAPI.py): add caching and error handling - Add file-based caching system for access tokens and sessions - Add robust error handling with specific error messages - Add automatic dialog continuation on resource limits - Add HTML parsing with BeautifulSoup for token extraction - Add debug logging for error tracking - Add timeout configuration for API requests * refactor(g4f/Provider/DarkAI.py): update DarkAI default model and aliases - Change default model from llama-3-405b to llama-3-70b - Remove llama-3-405b from supported models list - Remove llama-3.1-405b from model aliases * feat(g4f/Provider/Blackbox2.py): add image generation support - Add image model 'flux' with dedicated API endpoint - Refactor generator to support both text and image outputs - Extract headers into reusable static method - Add type hints for AsyncGenerator return type - Split generation logic into _generate_text and _generate_image methods - Add ImageResponse handling for image generation results BREAKING CHANGE: create_async_generator now returns AsyncGenerator instead of AsyncResult * refactor(g4f/Provider/ChatGptEs.py): update ChatGptEs model configuration - Update models list to include gpt-3.5-turbo - Remove chatgpt-4o-latest from supported models - Remove model_aliases mapping for gpt-4o * feat(g4f/Provider/DeepInfraChat.py): add Accept-Language header support - Add Accept-Language header for internationalization - Maintain existing header configuration - Improve request compatibility with language preferences * refactor(g4f/Provider/needs_auth/Gemini.py): add ProviderModelMixin inheritance - Add ProviderModelMixin to class inheritance - Import ProviderModelMixin from base_provider - Move BaseConversation import to base_provider imports * refactor(g4f/Provider/Liaobots.py): update model details and aliases - Add version suffix to o1 model IDs - Update model aliases for o1-preview and o1-mini - Standardize version format across model definitions * refactor(g4f/Provider/PollinationsAI.py): enhance model support and generation - Split generation logic into dedicated image/text methods - Add additional text models including sur and claude - Add width/height parameters for image generation - Add model existence validation - Add hasattr checks for model lists initialization * chore(gitignore): add provider cache directory - Add g4f/Provider/.cache to gitignore patterns * refactor(g4f/Provider/ReplicateHome.py): update model configuration - Update default model to gemma-2b-it - Add default_image_model configuration - Remove llava-13b from supported models - Simplify request headers * feat(g4f/models.py): expand provider and model support - Add new providers DarkAI and PollinationsAI - Add new models for Mistral, Flux and image generation - Update provider lists for existing models - Add P1 and Evil models with experimental providers BREAKING CHANGE: Remove llava-13b model support * refactor(Airforce): Update type hint for split_message return - Change return type of from to for consistency with import. - Maintain overall functionality and structure of the class. - Ensure compatibility with type hinting standards in Python. * refactor(g4f/Provider/Airforce.py): Update type hint for split_message return - Change return type of 'split_message' from 'list[str]' to 'List[str]' for consistency with import. - Maintain overall functionality and structure of the 'Airforce' class. - Ensure compatibility with type hinting standards in Python. * feat(g4f/Provider/RobocodersAPI.py): Add support for optional BeautifulSoup dependency - Introduce a check for the BeautifulSoup library and handle its absence gracefully. - Raise a if BeautifulSoup is not installed, prompting the user to install it. - Remove direct import of BeautifulSoup to avoid import errors when the library is missing. * fix: Updating provider documentation and small fixes in providers * Disabled the provider (RobocodersAPI) * Fix: Conflicting file g4f/models.py * Update g4f/models.py g4f/Provider/Airforce.py * Update docs/providers-and-models.md g4f/models.py g4f/Provider/Airforce.py g4f/Provider/PollinationsAI.py * Update docs/providers-and-models.md * Update .gitignore * Update g4f/models.py * Update g4f/Provider/PollinationsAI.py --------- Co-authored-by: kqlio67 <>
72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
from __future__ import annotations
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import time
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import hashlib
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import random
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from typing import AsyncGenerator, Optional, Dict, Any
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from ..typing import Messages
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from ..requests import StreamSession, raise_for_status
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from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
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from ..errors import RateLimitError
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# Constants
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DOMAINS = [
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"https://s.aifree.site",
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"https://v.aifree.site/",
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"https://al.aifree.site/",
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"https://u4.aifree.site/"
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]
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RATE_LIMIT_ERROR_MESSAGE = "当前地区当日额度已消耗完"
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class FreeGpt(AsyncGeneratorProvider, ProviderModelMixin):
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url = "https://freegptsnav.aifree.site"
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working = True
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supports_message_history = True
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supports_system_message = True
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default_model = 'gemini-pro'
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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: Optional[str] = None,
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timeout: int = 120,
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**kwargs: Any
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) -> AsyncGenerator[str, None]:
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prompt = messages[-1]["content"]
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timestamp = int(time.time())
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data = cls._build_request_data(messages, prompt, timestamp)
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domain = random.choice(DOMAINS)
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async with StreamSession(
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impersonate="chrome",
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timeout=timeout,
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proxies={"all": proxy} if proxy else None
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) as session:
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async with session.post(f"{domain}/api/generate", json=data) as response:
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await raise_for_status(response)
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async for chunk in response.iter_content():
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chunk_decoded = chunk.decode(errors="ignore")
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if chunk_decoded == RATE_LIMIT_ERROR_MESSAGE:
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raise RateLimitError("Rate limit reached")
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yield chunk_decoded
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@staticmethod
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def _build_request_data(messages: Messages, prompt: str, timestamp: int, secret: str = "") -> Dict[str, Any]:
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return {
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"messages": messages,
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"time": timestamp,
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"pass": None,
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"sign": generate_signature(timestamp, prompt, secret)
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}
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def generate_signature(timestamp: int, message: str, secret: str = "") -> str:
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data = f"{timestamp}:{message}:{secret}"
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return hashlib.sha256(data.encode()).hexdigest()
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