from __future__ import annotations
import json
import uuid
from aiohttp import ClientSession, ClientTimeout
from ..typing import AsyncResult, Messages
from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
from .helper import format_prompt
from ..image import ImageResponse
class AmigoChat(AsyncGeneratorProvider, ProviderModelMixin):
url = "https://amigochat.io/chat/"
chat_api_endpoint = "https://api.amigochat.io/v1/chat/completions"
image_api_endpoint = "https://api.amigochat.io/v1/images/generations"
working = True
supports_gpt_4 = True
supports_stream = True
supports_system_message = True
supports_message_history = True
default_model = 'gpt-4o-mini'
chat_models = [
'gpt-4o',
default_model,
'o1-preview',
'o1-mini',
'meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo',
'meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo',
'claude-3-sonnet-20240229',
'gemini-1.5-pro',
]
image_models = [
'flux-pro/v1.1',
'flux-realism',
'flux-pro',
'dalle-e-3',
]
models = [*chat_models, *image_models]
model_aliases = {
"o1": "o1-preview",
"llama-3.1-405b": "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
"llama-3.2-90b": "meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo",
"claude-3.5-sonnet": "claude-3-sonnet-20240229",
"gemini-pro": "gemini-1.5-pro",
"flux-pro": "flux-pro/v1.1",
"dalle-3": "dalle-e-3",
}
persona_ids = {
'gpt-4o': "gpt",
'gpt-4o-mini': "amigo",
'o1-preview': "openai-o-one",
'o1-mini': "openai-o-one-mini",
'meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo': "llama-three-point-one",
'meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo': "llama-3-2",
'claude-3-sonnet-20240229': "claude",
'gemini-1.5-pro': "gemini-1-5-pro",
'flux-pro/v1.1': "flux-1-1-pro",
'flux-realism': "flux-realism",
'flux-pro': "flux-pro",
'dalle-e-3': "dalle-three",
}
@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[model]
else:
return cls.default_chat_model if model in cls.chat_models else cls.default_image_model
@classmethod
def get_personaId(cls, model: str) -> str:
return cls.persona_ids[model]
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
proxy: str = None,
stream: bool = False,
**kwargs
) -> AsyncResult:
model = cls.get_model(model)
device_uuid = str(uuid.uuid4())
headers = {
"accept": "*/*",
"accept-language": "en-US,en;q=0.9",
"authorization": "Bearer", # You need to implement proper authorization
"cache-control": "no-cache",
"content-type": "application/json",
"origin": cls.url,
"pragma": "no-cache",
"priority": "u=1, i",
"referer": f"{cls.url}/",
"sec-ch-ua": '"Chromium";v="129", "Not=A?Brand";v="8"',
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-platform": '"Linux"',
"user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36",
"x-device-language": "en-US",
"x-device-platform": "web",
"x-device-uuid": device_uuid,
"x-device-version": "1.0.32"
}
async with ClientSession(headers=headers) as session:
if model in cls.chat_models:
# Chat completion
data = {
"messages": [{"role": m["role"], "content": m["content"]} for m in messages],
"model": model,
"personaId": cls.get_personaId(model),
"frequency_penalty": 0,
"max_tokens": 4000,
"presence_penalty": 0,
"stream": stream,
"temperature": 0.5,
"top_p": 0.95
}
timeout = ClientTimeout(total=300) # 5 minutes timeout
async with session.post(cls.chat_api_endpoint, json=data, proxy=proxy, timeout=timeout) as response:
if response.status not in (200, 201):
error_text = await response.text()
raise Exception(f"Error {response.status}: {error_text}")
async for line in response.content:
line = line.decode('utf-8').strip()
if line.startswith('data: '):
if line == 'data: [DONE]':
break
try:
chunk = json.loads(line[6:]) # Remove 'data: ' prefix
if 'choices' in chunk and len(chunk['choices']) > 0:
choice = chunk['choices'][0]
if 'delta' in choice:
content = choice['delta'].get('content')
elif 'text' in choice:
content = choice['text']
else:
content = None
if content:
yield content
except json.JSONDecodeError:
pass
else:
# Image generation
prompt = messages[0]['content']
data = {
"prompt": prompt,
"model": model,
"personaId": cls.get_personaId(model)
}
async with session.post(cls.image_api_endpoint, json=data, proxy=proxy) as response:
response.raise_for_status()
response_data = await response.json()
if "data" in response_data:
image_urls = []
for item in response_data["data"]:
if "url" in item:
image_url = item["url"]
image_urls.append(image_url)
if image_urls:
yield ImageResponse(image_urls, prompt)
else:
yield None
