mirror of
https://github.com/xtekky/gpt4free.git
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9c8aedbeb1
Updates for the response of the BackendApi Update of the demo model list Improve web search tool Moved copy_images to /image
275 lines
11 KiB
Python
275 lines
11 KiB
Python
import json
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import random
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import re
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import requests
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from aiohttp import ClientSession
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from typing import List
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from ...typing import AsyncResult, Messages
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from ...providers.response import ImageResponse, FinishReason, Usage
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from ...requests.raise_for_status import raise_for_status
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from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
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from ... import debug
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def split_message(message: str, max_length: int = 1000) -> List[str]:
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"""Splits the message into parts up to (max_length)."""
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chunks = []
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while len(message) > max_length:
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split_point = message.rfind(' ', 0, max_length)
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if split_point == -1:
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split_point = max_length
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chunks.append(message[:split_point])
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message = message[split_point:].strip()
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if message:
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chunks.append(message)
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return chunks
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class Airforce(AsyncGeneratorProvider, ProviderModelMixin):
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url = "https://api.airforce"
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api_endpoint_completions = "https://api.airforce/chat/completions"
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api_endpoint_imagine2 = "https://api.airforce/imagine2"
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working = False
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supports_stream = True
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supports_system_message = True
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supports_message_history = True
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default_model = "llama-3.1-70b-chat"
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default_image_model = "flux"
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models = []
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image_models = []
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hidden_models = {"Flux-1.1-Pro"}
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additional_models_imagine = ["flux-1.1-pro", "midjourney", "dall-e-3"]
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model_aliases = {
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# Alias mappings for models
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"openchat-3.5": "openchat-3.5-0106",
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"deepseek-coder": "deepseek-coder-6.7b-instruct",
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"hermes-2-dpo": "Nous-Hermes-2-Mixtral-8x7B-DPO",
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"hermes-2-pro": "hermes-2-pro-mistral-7b",
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"openhermes-2.5": "openhermes-2.5-mistral-7b",
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"lfm-40b": "lfm-40b-moe",
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"german-7b": "discolm-german-7b-v1",
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"llama-2-7b": "llama-2-7b-chat-int8",
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"llama-3.1-70b": "llama-3.1-70b-chat",
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"llama-3.1-8b": "llama-3.1-8b-chat",
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"llama-3.1-70b": "llama-3.1-70b-turbo",
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"llama-3.1-8b": "llama-3.1-8b-turbo",
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"neural-7b": "neural-chat-7b-v3-1",
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"zephyr-7b": "zephyr-7b-beta",
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"evil": "any-uncensored",
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"sdxl": "stable-diffusion-xl-lightning",
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"sdxl": "stable-diffusion-xl-base",
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"flux-pro": "flux-1.1-pro",
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"llama-3.1-8b": "llama-3.1-8b-chat"
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}
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@classmethod
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def get_models(cls):
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"""Get available models with error handling"""
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if not cls.image_models:
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try:
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response = requests.get(
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f"{cls.url}/imagine2/models",
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headers={
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36",
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}
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)
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response.raise_for_status()
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cls.image_models = response.json()
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if isinstance(cls.image_models, list):
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cls.image_models.extend(cls.additional_models_imagine)
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else:
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cls.image_models = cls.additional_models_imagine.copy()
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except Exception as e:
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debug.log(f"Error fetching image models: {e}")
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cls.image_models = cls.additional_models_imagine.copy()
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if not cls.models:
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try:
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response = requests.get(
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f"{cls.url}/models",
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headers={
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36",
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}
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)
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response.raise_for_status()
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data = response.json()
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if isinstance(data, dict) and 'data' in data:
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cls.models = [model['id'] for model in data['data']]
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cls.models.extend(cls.image_models)
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cls.models = [model for model in cls.models if model not in cls.hidden_models]
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else:
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cls.models = list(cls.model_aliases.keys())
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except Exception as e:
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debug.log(f"Error fetching text models: {e}")
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cls.models = list(cls.model_aliases.keys())
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return cls.models or list(cls.model_aliases.keys())
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@classmethod
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def get_model(cls, model: str) -> str:
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"""Get the actual model name from alias"""
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return cls.model_aliases.get(model, model or cls.default_model)
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@classmethod
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def _filter_content(cls, part_response: str) -> str:
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"""
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Filters out unwanted content from the partial response.
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"""
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part_response = re.sub(
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r"One message exceeds the \d+chars per message limit\..+https:\/\/discord\.com\/invite\/\S+",
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'',
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part_response
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)
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part_response = re.sub(
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r"Rate limit \(\d+\/minute\) exceeded\. Join our discord for more: .+https:\/\/discord\.com\/invite\/\S+",
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'',
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part_response
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)
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return part_response
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@classmethod
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def _filter_response(cls, response: str) -> str:
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"""
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Filters the full response to remove system errors and other unwanted text.
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"""
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if "Model not found or too long input. Or any other error (xD)" in response:
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raise ValueError(response)
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filtered_response = re.sub(r"\[ERROR\] '\w{8}-\w{4}-\w{4}-\w{4}-\w{12}'", '', response) # any-uncensored
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filtered_response = re.sub(r'<\|im_end\|>', '', filtered_response) # remove <|im_end|> token
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filtered_response = re.sub(r'</s>', '', filtered_response) # neural-chat-7b-v3-1
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filtered_response = re.sub(r'^(Assistant: |AI: |ANSWER: |Output: )', '', filtered_response) # phi-2
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filtered_response = cls._filter_content(filtered_response)
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return filtered_response
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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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size: str,
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seed: int,
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proxy: str = None
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) -> AsyncResult:
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:133.0) Gecko/20100101 Firefox/133.0",
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"Accept": "image/avif,image/webp,image/png,image/svg+xml,image/*;q=0.8,*/*;q=0.5",
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"Accept-Language": "en-US,en;q=0.5",
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"Accept-Encoding": "gzip, deflate, br",
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"Content-Type": "application/json",
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}
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params = {"model": model, "prompt": prompt, "size": size, "seed": seed}
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async with ClientSession(headers=headers) as session:
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async with session.get(cls.api_endpoint_imagine2, params=params, proxy=proxy) as response:
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if response.status == 200:
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image_url = str(response.url)
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yield ImageResponse(images=image_url, alt=prompt)
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else:
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error_text = await response.text()
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raise RuntimeError(f"Image generation failed: {response.status} - {error_text}")
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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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max_tokens: int,
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temperature: float,
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top_p: float,
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stream: bool,
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proxy: str = None
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) -> AsyncResult:
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"""
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Generates text, buffers the response, filters it, and returns the final result.
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"""
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:133.0) Gecko/20100101 Firefox/133.0",
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"Accept": "application/json, text/event-stream",
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"Accept-Language": "en-US,en;q=0.5",
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"Accept-Encoding": "gzip, deflate, br",
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"Content-Type": "application/json",
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}
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final_messages = []
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for message in messages:
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message_chunks = split_message(message["content"], max_length=1000)
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final_messages.extend([{"role": message["role"], "content": chunk} for chunk in message_chunks])
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data = {
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"messages": final_messages,
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"model": model,
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"temperature": temperature,
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"top_p": top_p,
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"stream": stream,
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}
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if max_tokens != 512:
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data["max_tokens"] = max_tokens
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async with ClientSession(headers=headers) as session:
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async with session.post(cls.api_endpoint_completions, json=data, proxy=proxy) as response:
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await raise_for_status(response)
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if stream:
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idx = 0
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async for line in response.content:
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line = line.decode('utf-8').strip()
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if line.startswith('data: '):
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try:
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json_str = line[6:] # Remove 'data: ' prefix
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chunk = json.loads(json_str)
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if 'choices' in chunk and chunk['choices']:
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delta = chunk['choices'][0].get('delta', {})
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if 'content' in delta:
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chunk = cls._filter_response(delta['content'])
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if chunk:
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yield chunk
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idx += 1
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except json.JSONDecodeError:
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continue
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if idx == 512:
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yield FinishReason("length")
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else:
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# Non-streaming response
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result = await response.json()
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if "usage" in result:
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yield Usage(**result["usage"])
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if result["usage"]["completion_tokens"] == 512:
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yield FinishReason("length")
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if 'choices' in result and result['choices']:
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message = result['choices'][0].get('message', {})
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content = message.get('content', '')
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filtered_response = cls._filter_response(content)
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yield filtered_response
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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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prompt: str = None,
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proxy: str = None,
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max_tokens: int = 512,
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temperature: float = 1,
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top_p: float = 1,
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stream: bool = True,
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size: str = "1:1",
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seed: int = None,
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**kwargs
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) -> AsyncResult:
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model = cls.get_model(model)
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if model in cls.image_models:
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if prompt is None:
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prompt = messages[-1]['content']
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if seed is None:
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seed = random.randint(0, 10000)
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async for result in cls.generate_image(model, prompt, size, seed, proxy):
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yield result
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else:
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async for result in cls.generate_text(model, messages, max_tokens, temperature, top_p, stream, proxy):
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yield result
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