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from __future__ import annotations
import random
import json
import re
from aiohttp import ClientSession
from ..typing import AsyncResult, Messages
from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
from ..image import ImageResponse
def split_long_message(message: str, max_length: int = 4000) -> list[str]:
return [message[i:i+max_length] for i in range(0, len(message), max_length)]
class Airforce(AsyncGeneratorProvider, ProviderModelMixin):
url = "https://api.airforce"
image_api_endpoint = "https://api.airforce/imagine2"
text_api_endpoint = "https://api.airforce/chat/completions"
working = True
default_model = 'llama-3-70b-chat'
supports_stream = True
supports_system_message = True
supports_message_history = True
text_models = [
'claude-3-haiku-20240307',
'claude-3-sonnet-20240229',
'claude-3-5-sonnet-20240620',
'claude-3-opus-20240229',
'chatgpt-4o-latest',
'gpt-4',
'gpt-4-turbo',
'gpt-4o-mini-2024-07-18',
'gpt-4o-mini',
'gpt-3.5-turbo',
'gpt-3.5-turbo-0125',
'gpt-3.5-turbo-1106',
default_model,
'llama-3-70b-chat-turbo',
'llama-3-8b-chat',
'llama-3-8b-chat-turbo',
'llama-3-70b-chat-lite',
'llama-3-8b-chat-lite',
'llama-2-13b-chat',
'llama-3.1-405b-turbo',
'llama-3.1-70b-turbo',
'llama-3.1-8b-turbo',
'LlamaGuard-2-8b',
'Llama-Guard-7b',
'Llama-3.2-90B-Vision-Instruct-Turbo',
'Mixtral-8x7B-Instruct-v0.1',
'Mixtral-8x22B-Instruct-v0.1',
'Mistral-7B-Instruct-v0.1',
'Mistral-7B-Instruct-v0.2',
'Mistral-7B-Instruct-v0.3',
'Qwen1.5-7B-Chat',
'Qwen1.5-14B-Chat',
'Qwen1.5-72B-Chat',
'Qwen1.5-110B-Chat',
'Qwen2-72B-Instruct',
'gemma-2b-it',
'gemma-2-9b-it',
'gemma-2-27b-it',
'gemini-1.5-flash',
'gemini-1.5-pro',
'deepseek-llm-67b-chat',
'Nous-Hermes-2-Mixtral-8x7B-DPO',
'Nous-Hermes-2-Yi-34B',
'WizardLM-2-8x22B',
'SOLAR-10.7B-Instruct-v1.0',
'MythoMax-L2-13b',
'cosmosrp',
]
image_models = [
'flux',
'flux-realism',
'flux-anime',
'flux-3d',
'flux-disney',
'flux-pixel',
'flux-4o',
'any-dark',
]
models = [
*text_models,
*image_models,
]
model_aliases = {
"claude-3-haiku": "claude-3-haiku-20240307",
"claude-3-sonnet": "claude-3-sonnet-20240229",
"gpt-4o": "chatgpt-4o-latest",
"llama-3-70b": "llama-3-70b-chat",
"llama-3-8b": "llama-3-8b-chat",
"mixtral-8x7b": "Mixtral-8x7B-Instruct-v0.1",
"qwen-1.5-7b": "Qwen1.5-7B-Chat",
"gemma-2b": "gemma-2b-it",
"gemini-flash": "gemini-1.5-flash",
"mythomax-l2-13b": "MythoMax-L2-13b",
"solar-10.7b": "SOLAR-10.7B-Instruct-v1.0",
}
@classmethod
def get_model(cls, model: str) -> str:
if model in cls.models:
return model
elif model in cls.model_aliases:
return cls.model_aliases.get(model, cls.default_model)
else:
return cls.default_model
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
proxy: str = None,
seed: int = None,
size: str = "1:1",
stream: bool = False,
**kwargs
) -> AsyncResult:
model = cls.get_model(model)
if model in cls.image_models:
async for result in cls._generate_image(model, messages, proxy, seed, size):
yield result
elif model in cls.text_models:
async for result in cls._generate_text(model, messages, proxy, stream):
yield result
@classmethod
async def _generate_image(
cls,
model: str,
messages: Messages,
proxy: str = None,
seed: int = None,
size: str = "1:1",
**kwargs
) -> AsyncResult:
headers = {
"accept": "*/*",
"accept-language": "en-US,en;q=0.9",
"cache-control": "no-cache",
"origin": "https://llmplayground.net",
"user-agent": "Mozilla/5.0"
}
if seed is None:
seed = random.randint(0, 100000)
prompt = messages[-1]['content']
async with ClientSession(headers=headers) as session:
params = {
"model": model,
"prompt": prompt,
"size": size,
"seed": seed
}
async with session.get(f"{cls.image_api_endpoint}", params=params, proxy=proxy) as response:
response.raise_for_status()
content_type = response.headers.get('Content-Type', '').lower()
if 'application/json' in content_type:
async for chunk in response.content.iter_chunked(1024):
if chunk:
yield chunk.decode('utf-8')
elif 'image' in content_type:
image_data = b""
async for chunk in response.content.iter_chunked(1024):
if chunk:
image_data += chunk
image_url = f"{cls.image_api_endpoint}?model={model}&prompt={prompt}&size={size}&seed={seed}"
alt_text = f"Generated image for prompt: {prompt}"
yield ImageResponse(images=image_url, alt=alt_text)
@classmethod
async def _generate_text(
cls,
model: str,
messages: Messages,
proxy: str = None,
stream: bool = False,
**kwargs
) -> AsyncResult:
headers = {
"accept": "*/*",
"accept-language": "en-US,en;q=0.9",
"authorization": "Bearer missing api key",
"content-type": "application/json",
"user-agent": "Mozilla/5.0"
}
async with ClientSession(headers=headers) as session:
formatted_prompt = cls._format_messages(messages)
prompt_parts = split_long_message(formatted_prompt)
full_response = ""
for part in prompt_parts:
data = {
"messages": [{"role": "user", "content": part}],
"model": model,
"max_tokens": 4096,
"temperature": 1,
"top_p": 1,
"stream": stream
}
async with session.post(cls.text_api_endpoint, json=data, proxy=proxy) as response:
response.raise_for_status()
part_response = ""
if stream:
async for line in response.content:
if line:
line = line.decode('utf-8').strip()
if line.startswith("data: ") and line != "data: [DONE]":
json_data = json.loads(line[6:])
content = json_data['choices'][0]['delta'].get('content', '')
part_response += content
else:
json_data = await response.json()
content = json_data['choices'][0]['message']['content']
part_response = content
part_response = re.sub(
r"One message exceeds the \d+chars per message limit\..+https:\/\/discord\.com\/invite\/\S+",
'',
part_response
)
part_response = re.sub(
r"Rate limit \(\d+\/minute\) exceeded\. Join our discord for more: .+https:\/\/discord\.com\/invite\/\S+",
'',
part_response
)
full_response += part_response
yield full_response
@classmethod
def _format_messages(cls, messages: Messages) -> str:
return " ".join([msg['content'] for msg in messages])
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