from __future__ import annotations
from typing import Generator, Optional, Dict, Any, Union, List
import random
import asyncio
import base64
from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
from ..typing import AsyncResult, Messages
from ..requests import StreamSession, raise_for_status
from ..errors import ResponseError
from ..image import ImageResponse
class ReplicateHome(AsyncGeneratorProvider, ProviderModelMixin):
url = "https://replicate.com"
parent = "Replicate"
working = True
default_model = 'meta/meta-llama-3-70b-instruct'
text_models = {"meta/meta-llama-3-70b-instruct", "mistralai/mixtral-8x7b-instruct-v0.1", "google-deepmind/gemma-2b-it"}
image_models = {"stability-ai/stable-diffusion-3", "bytedance/sdxl-lightning-4step", "playgroundai/playground-v2.5-1024px-aesthetic"}
models = [
*text_models,
*image_models
]
versions = {
# Model versions for generating images
'stability-ai/stable-diffusion-3': [
"527d2a6296facb8e47ba1eaf17f142c240c19a30894f437feee9b91cc29d8e4f"
],
'bytedance/sdxl-lightning-4step': [
"5f24084160c9089501c1b3545d9be3c27883ae2239b6f412990e82d4a6210f8f"
],
'playgroundai/playground-v2.5-1024px-aesthetic': [
"a45f82a1382bed5c7aeb861dac7c7d191b0fdf74d8d57c4a0e6ed7d4d0bf7d24"
],
# Model versions for text generation
'meta/meta-llama-3-70b-instruct': [
"dp-cf04fe09351e25db628e8b6181276547"
],
'mistralai/mixtral-8x7b-instruct-v0.1': [
"dp-89e00f489d498885048e94f9809fbc76"
],
'google-deepmind/gemma-2b-it': [
"dff94eaf770e1fc211e425a50b51baa8e4cac6c39ef074681f9e39d778773626"
]
}
model_aliases = {
"sd-3": "stability-ai/stable-diffusion-3",
"sdxl": "bytedance/sdxl-lightning-4step",
"playground-v2.5": "playgroundai/playground-v2.5-1024px-aesthetic",
"llama-3-70b": "meta/meta-llama-3-70b-instruct",
"mixtral-8x7b": "mistralai/mixtral-8x7b-instruct-v0.1",
"gemma-2b": "google-deepmind/gemma-2b-it",
}
@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_model
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
**kwargs: Any
) -> Generator[Union[str, ImageResponse], None, None]:
yield await cls.create_async(messages[-1]["content"], model, **kwargs)
@classmethod
async def create_async(
cls,
prompt: str,
model: str,
api_key: Optional[str] = None,
proxy: Optional[str] = None,
timeout: int = 180,
version: Optional[str] = None,
extra_data: Dict[str, Any] = {},
**kwargs: Any
) -> Union[str, ImageResponse]:
model = cls.get_model(model) # Use the get_model method to resolve model name
headers = {
'Accept-Encoding': 'gzip, deflate, br',
'Accept-Language': 'en-US',
'Connection': 'keep-alive',
'Origin': cls.url,
'Referer': f'{cls.url}/',
'Sec-Fetch-Dest': 'empty',
'Sec-Fetch-Mode': 'cors',
'Sec-Fetch-Site': 'same-site',
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
'sec-ch-ua': '"Google Chrome";v="119", "Chromium";v="119", "Not?A_Brand";v="24"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"macOS"',
}
if version is None:
version = random.choice(cls.versions.get(model, []))
if api_key is not None:
headers["Authorization"] = f"Bearer {api_key}"
async with StreamSession(
proxies={"all": proxy},
headers=headers,
timeout=timeout
) as session:
data = {
"input": {
"prompt": prompt,
**extra_data
},
"version": version
}
if api_key is None:
data["model"] = model
url = "https://homepage.replicate.com/api/prediction"
else:
url = "https://api.replicate.com/v1/predictions"
async with session.post(url, json=data) as response:
await raise_for_status(response)
result = await response.json()
if "id" not in result:
raise ResponseError(f"Invalid response: {result}")
while True:
if api_key is None:
url = f"https://homepage.replicate.com/api/poll?id={result['id']}"
else:
url = f"https://api.replicate.com/v1/predictions/{result['id']}"
async with session.get(url) as response:
await raise_for_status(response)
result = await response.json()
if "status" not in result:
raise ResponseError(f"Invalid response: {result}")
if result["status"] == "succeeded":
output = result['output']
if model in cls.text_models:
return ''.join(output) if isinstance(output, list) else output
elif model in cls.image_models:
images: List[Any] = output
images = images[0] if len(images) == 1 else images
return ImageResponse(images, prompt)
elif result["status"] == "failed":
raise ResponseError(f"Prediction failed: {result}")
await asyncio.sleep(0.5)
