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from __future__ import annotations |
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import json |
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from aiohttp import ClientSession |
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from ..typing import AsyncResult, Messages |
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from ..image import ImageResponse |
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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_system_message = True |
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supports_message_history = True |
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default_model = 'chat-gemini-flash' |
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chat_models = [ |
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default_model, |
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'chat-gemini-pro', |
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'chat-gpt4m', |
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'chat-gpt4', |
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'claude-sonnet', |
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'claude-haiku', |
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'llama-3-70b', |
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'llama-3-8b', |
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'mistral-large', |
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'chat-o1-mini' |
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] |
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image_models = [ |
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'flux1', |
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'sdxl', |
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'sd', |
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'sd35', |
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] |
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models = [*chat_models, *image_models] |
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model_aliases = { |
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"gemini-flash": "chat-gemini-flash", |
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"gemini-pro": "chat-gemini-pro", |
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"gpt-4o-mini": "chat-gpt4m", |
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"gpt-4o": "chat-gpt4", |
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"claude-3.5-sonnet": "claude-sonnet", |
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"claude-3-haiku": "claude-haiku", |
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"llama-3.1-70b": "llama-3-70b", |
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"llama-3.1-8b": "llama-3-8b", |
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"o1-mini": "chat-o1-mini", |
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"sd-1.5": "sd", |
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"sd-3.5": "sd35", |
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"flux-schnell": "flux1", |
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} |
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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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def is_image_model(cls, model: str) -> bool: |
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return model in cls.image_models |
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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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'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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async with ClientSession() as session: |
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if cls.is_image_model(model): |
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prompt = messages[-1]["content"] |
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data = { |
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"model": model, |
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"input": { |
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"width": "1024", |
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"height": "1024", |
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"steps": 4, |
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"output_format": "webp", |
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"batch_size": 1, |
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"mode": "plan", |
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"prompt": prompt |
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} |
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} |
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async with session.post( |
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cls.api_endpoint, |
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headers=headers, |
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data=json.dumps(data), |
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proxy=proxy |
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) as response: |
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response.raise_for_status() |
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response_data = await response.json() |
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if response_data.get('status') == 'completed' and response_data.get('output'): |
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for url in response_data['output']: |
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yield ImageResponse(images=url, alt="Generated Image") |
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else: |
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data = { |
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"model": model, |
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"input": { |
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"messages": [ |
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{ |
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"type": "human", |
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"content": format_prompt(messages) |
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} |
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], |
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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( |
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cls.api_endpoint, |
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headers=headers, |
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data=json.dumps(data), |
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proxy=proxy |
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) as response: |
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response.raise_for_status() |
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result = await response.json() |
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yield result.get('output', '') |
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