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# api/gizai.py
from __future__ import annotations
import json
from aiohttp import ClientSession
from typing import AsyncGenerator, Union
from .models import AsyncResult, Messages, ImageResponse
from .utils import strip_model_prefix
class AsyncGeneratorProvider:
@classmethod
async def create_async_generator(cls, *args, **kwargs) -> AsyncGenerator:
"""Abstract method to create an asynchronous generator."""
raise NotImplementedError
class ProviderModelMixin:
@classmethod
def get_model(cls, model: str) -> str:
"""Abstract method to get the actual model name."""
raise NotImplementedError
class GizAI(AsyncGeneratorProvider, ProviderModelMixin):
url = "https://app.giz.ai/assistant/"
api_endpoint = "https://app.giz.ai/api/data/users/inferenceServer.infer"
working = True
supports_system_message = True
supports_message_history = True
# Chat models
default_model = 'chat-gemini-flash'
chat_models = [
default_model,
'chat-gemini-pro',
'chat-gpt4m',
'chat-gpt4',
'claude-sonnet',
'claude-haiku',
'llama-3-70b',
'llama-3-8b',
'mistral-large',
'chat-o1-mini'
]
# Image models
image_models = [
'flux1',
'sdxl',
'sd',
'sd35',
]
models = [*chat_models, *image_models]
model_aliases = {
# Chat model aliases
"gemini-flash": "chat-gemini-flash",
"gemini-pro": "chat-gemini-pro",
"gpt-4o-mini": "chat-gpt4m",
"gpt-4o": "chat-gpt4",
"claude-3.5-sonnet": "claude-sonnet",
"claude-3-haiku": "claude-haiku",
"llama-3.1-70b": "llama-3-70b",
"llama-3.1-8b": "llama-3-8b",
"o1-mini": "chat-o1-mini",
# Image model aliases
"sd-1.5": "sd",
"sd-3.5": "sd35",
"flux-schnell": "flux1",
}
@classmethod
def get_model(cls, model: str) -> str:
"""Retrieve the actual model name, handling aliases."""
if model in cls.models:
return model
elif model in cls.model_aliases:
return cls.model_aliases[model]
else:
return cls.default_model
@classmethod
def is_image_model(cls, model: str) -> bool:
"""Determine if the given model is an image generation model."""
return model in cls.image_models
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
proxy: str = None,
**kwargs
) -> AsyncResult:
"""Create an asynchronous generator for processing requests."""
model = cls.get_model(model)
headers = {
'Accept': 'application/json, text/plain, */*',
'Accept-Language': 'en-US,en;q=0.9',
'Cache-Control': 'no-cache',
'Connection': 'keep-alive',
'Content-Type': 'application/json',
'Origin': 'https://app.giz.ai',
'Pragma': 'no-cache',
'Sec-Fetch-Dest': 'empty',
'Sec-Fetch-Mode': 'cors',
'Sec-Fetch-Site': 'same-origin',
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36',
'sec-ch-ua': '"Not?A_Brand";v="99", "Chromium";v="130"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Linux"'
}
async with ClientSession() as session:
if cls.is_image_model(model):
# Image generation
prompt = messages[-1]["content"]
data = {
"model": model,
"input": {
"width": "1024",
"height": "1024",
"steps": 4,
"output_format": "webp",
"batch_size": 1,
"mode": "plan",
"prompt": prompt
}
}
async with session.post(
cls.api_endpoint,
headers=headers,
data=json.dumps(data),
proxy=proxy
) as response:
response.raise_for_status()
response_data = await response.json()
if response_data.get('status') == 'completed' and response_data.get('output'):
for url in response_data['output']:
yield ImageResponse(images=url, alt="Generated Image")
else:
# Chat completion
# Directly format the prompt without using a separate helper
prompt = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
data = {
"model": model,
"input": {
"messages": [
{
"type": "human",
"content": prompt
}
],
"mode": "plan"
},
"noStream": True
}
async with session.post(
cls.api_endpoint,
headers=headers,
data=json.dumps(data),
proxy=proxy
) as response:
response.raise_for_status()
result = await response.json()
yield result.get('output', '')
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