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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 <>
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
from __future__ import annotations
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from aiohttp import ClientSession
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from ..typing import AsyncResult, Messages
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from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
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from .helper import format_prompt
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class GizAI(AsyncGeneratorProvider, ProviderModelMixin):
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url = "https://app.giz.ai/assistant"
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api_endpoint = "https://app.giz.ai/api/data/users/inferenceServer.infer"
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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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default_model = 'chat-gemini-flash'
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models = [default_model]
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model_aliases = {"gemini-flash": "chat-gemini-flash",}
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@classmethod
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def get_model(cls, model: str) -> str:
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if model in cls.models:
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return model
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elif model in cls.model_aliases:
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return cls.model_aliases[model]
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else:
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return cls.default_model
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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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**kwargs
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) -> AsyncResult:
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model = cls.get_model(model)
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headers = {
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'Accept': 'application/json, text/plain, */*',
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'Accept-Language': 'en-US,en;q=0.9',
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'Cache-Control': 'no-cache',
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'Connection': 'keep-alive',
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'Content-Type': 'application/json',
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'DNT': '1',
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'Origin': 'https://app.giz.ai',
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'Pragma': 'no-cache',
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'Sec-Fetch-Dest': 'empty',
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'Sec-Fetch-Mode': 'cors',
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'Sec-Fetch-Site': 'same-origin',
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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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'sec-ch-ua': '"Not?A_Brand";v="99", "Chromium";v="130"',
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'sec-ch-ua-mobile': '?0',
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'sec-ch-ua-platform': '"Linux"'
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}
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prompt = format_prompt(messages)
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async with ClientSession(headers=headers) as session:
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data = {
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"model": model,
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"input": {
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"messages": [{"type": "human", "content": prompt}],
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"mode": "plan"
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},
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"noStream": True
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}
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async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
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if response.status == 201:
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result = await response.json()
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yield result['output'].strip()
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else:
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raise Exception(f"Unexpected response status: {response.status}")
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