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Merge pull request #1672 from hlohaus/phind2 

Fix HuggingChat and PerplexityLabs and add HuggingFace provider
This commit is contained in:
H Lohaus 2024-03-11 07:47:35 +01:00 committed by GitHub
parent cf7f5f6cda ec51e9c764
commit 0b850ac9fc
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GPG Key ID: B5690EEEBB952194
12 changed files with 234 additions and 76 deletions
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4
g4f/Provider/BingCreateImages.py
View File

@ -2,7 +2,7 @@ from __future__ import annotations
import asyncio
import os
from typing import Generator
from typing import Iterator, Union
from ..cookies import get_cookies
from ..image import ImageResponse
@ -16,7 +16,7 @@ class BingCreateImages:
self.cookies = cookies
self.proxy = proxy
def create(self, prompt: str) -> Generator[ImageResponse, None, None]:
def create(self, prompt: str) -> Iterator[Union[ImageResponse, str]]:
"""
Generator for creating imagecompletion based on a prompt.

35
g4f/Provider/HuggingChat.py
View File

@ -1,12 +1,12 @@
from __future__ import annotations
import json, uuid
import json
from aiohttp import ClientSession, BaseConnector
from ..typing import AsyncResult, Messages
from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
from .helper import format_prompt, get_cookies, get_connector
from .helper import format_prompt, get_connector
class HuggingChat(AsyncGeneratorProvider, ProviderModelMixin):
@ -24,7 +24,6 @@ class HuggingChat(AsyncGeneratorProvider, ProviderModelMixin):
]
model_aliases = {
"openchat/openchat_3.5": "openchat/openchat-3.5-1210",
"mistralai/Mixtral-8x7B-Instruct-v0.1": "mistralai/Mistral-7B-Instruct-v0.2"
}
@classmethod
@ -39,9 +38,11 @@ class HuggingChat(AsyncGeneratorProvider, ProviderModelMixin):
cookies: dict = None,
**kwargs
) -> AsyncResult:
if not cookies:
cookies = get_cookies(".huggingface.co", False)
options = {"model": cls.get_model(model)}
system_prompt = "\n".join([message["content"] for message in messages if message["role"] == "system"])
if system_prompt:
options["preprompt"] = system_prompt
messages = [message for message in messages if message["role"] != "system"]
headers = {
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
}
@ -50,20 +51,27 @@ class HuggingChat(AsyncGeneratorProvider, ProviderModelMixin):
headers=headers,
connector=get_connector(connector, proxy)
) as session:
async with session.post(f"{cls.url}/conversation", json={"model": cls.get_model(model)}, proxy=proxy) as response:
async with session.post(f"{cls.url}/conversation", json=options, proxy=proxy) as response:
response.raise_for_status()
conversation_id = (await response.json())["conversationId"]
send = {
"id": str(uuid.uuid4()),
async with session.get(f"{cls.url}/conversation/{conversation_id}/__data.json") as response:
response.raise_for_status()
data: list = (await response.json())["nodes"][1]["data"]
keys: list[int] = data[data[0]["messages"]]
message_keys: dict = data[keys[0]]
message_id: str = data[message_keys["id"]]
options = {
"id": message_id,
"inputs": format_prompt(messages),
"is_continue": False,
"is_retry": False,
"response_id": str(uuid.uuid4()),
"web_search": web_search
}
async with session.post(f"{cls.url}/conversation/{conversation_id}", json=send, proxy=proxy) as response:
async with session.post(f"{cls.url}/conversation/{conversation_id}", json=options) as response:
first_token = True
async for line in response.content:
line = json.loads(line[:-1])
response.raise_for_status()
line = json.loads(line)
if "type" not in line:
raise RuntimeError(f"Response: {line}")
elif line["type"] == "stream":
@ -74,6 +82,5 @@ class HuggingChat(AsyncGeneratorProvider, ProviderModelMixin):
yield token
elif line["type"] == "finalAnswer":
break
async with session.delete(f"{cls.url}/conversation/{conversation_id}", proxy=proxy) as response:
response.raise_for_status()

75
g4f/Provider/HuggingFace.py Normal file
View File

@ -0,0 +1,75 @@
from __future__ import annotations
import json
from aiohttp import ClientSession, BaseConnector
from ..typing import AsyncResult, Messages
from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
from .helper import get_connector
from ..errors import RateLimitError, ModelNotFoundError
class HuggingFace(AsyncGeneratorProvider, ProviderModelMixin):
url = "https://huggingface.co/chat"
working = True
supports_message_history = True
default_model = "mistralai/Mixtral-8x7B-Instruct-v0.1"
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
stream: bool = True,
proxy: str = None,
connector: BaseConnector = None,
api_base: str = "https://api-inference.huggingface.co",
api_key: str = None,
max_new_tokens: int = 1024,
temperature: float = 0.7,
**kwargs
) -> AsyncResult:
model = cls.get_model(model)
headers = {}
if api_key is not None:
headers["Authorization"] = f"Bearer {api_key}"
params = {
"return_full_text": False,
"max_new_tokens": max_new_tokens,
"temperature": temperature,
**kwargs
}
payload = {"inputs": format_prompt(messages), "parameters": params, "stream": stream}
async with ClientSession(
headers=headers,
connector=get_connector(connector, proxy)
) as session:
async with session.post(f"{api_base.rstrip('/')}/models/{model}", json=payload) as response:
if response.status == 429:
raise RateLimitError("Rate limit reached. Set a api_key")
elif response.status == 404:
raise ModelNotFoundError(f"Model is not supported: {model}")
elif response.status != 200:
raise RuntimeError(f"Response {response.status}: {await response.text()}")
if stream:
first = True
async for line in response.content:
if line.startswith(b"data:"):
data = json.loads(line[5:])
if not data["token"]["special"]:
chunk = data["token"]["text"]
if first:
first = False
chunk = chunk.lstrip()
yield chunk
else:
yield (await response.json())[0]["generated_text"].strip()
def format_prompt(messages: Messages) -> str:
system_messages = [message["content"] for message in messages if message["role"] == "system"]
question = " ".join([messages[-1]["content"], *system_messages])
history = "".join([
f"<s>[INST]{messages[idx-1]['content']} [/INST] {message}</s>"
for idx, message in enumerate(messages)
if message["role"] == "assistant"
])
return f"{history}<s>[INST] {question} [/INST]"

16
g4f/Provider/Llama2.py
View File

@ -28,6 +28,10 @@ class Llama2(AsyncGeneratorProvider, ProviderModelMixin):
model: str,
messages: Messages,
proxy: str = None,
system_message: str = "You are a helpful assistant.",
temperature: float = 0.75,
top_p: float = 0.9,
max_tokens: int = 8000,
**kwargs
) -> AsyncResult:
headers = {
@ -47,14 +51,18 @@ class Llama2(AsyncGeneratorProvider, ProviderModelMixin):
"TE": "trailers"
}
async with ClientSession(headers=headers) as session:
system_messages = [message["content"] for message in messages if message["role"] == "system"]
if system_messages:
system_message = "\n".join(system_messages)
messages = [message for message in messages if message["role"] != "system"]
prompt = format_prompt(messages)
data = {
"prompt": prompt,
"model": cls.get_model(model),
"systemPrompt": kwargs.get("system_message", "You are a helpful assistant."),
"temperature": kwargs.get("temperature", 0.75),
"topP": kwargs.get("top_p", 0.9),
"maxTokens": kwargs.get("max_tokens", 8000),
"systemPrompt": system_message,
"temperature": temperature,
"topP": top_p,
"maxTokens": max_tokens,
"image": None
}
started = False

47
g4f/Provider/PerplexityLabs.py
View File

@ -14,17 +14,18 @@ WS_URL = "wss://labs-api.perplexity.ai/socket.io/"
class PerplexityLabs(AsyncGeneratorProvider, ProviderModelMixin):
url = "https://labs.perplexity.ai"
working = True
default_model = 'pplx-70b-online'
default_model = "sonar-medium-online"
models = [
'pplx-7b-online', 'pplx-70b-online', 'pplx-7b-chat', 'pplx-70b-chat', 'mistral-7b-instruct',
'codellama-34b-instruct', 'llama-2-70b-chat', 'llava-7b-chat', 'mixtral-8x7b-instruct',
'mistral-medium', 'related'
"sonar-small-online", "sonar-medium-online", "sonar-small-chat", "sonar-medium-chat", "mistral-7b-instruct",
"codellama-70b-instruct", "llava-v1.5-7b-wrapper", "llava-v1.6-34b", "mixtral-8x7b-instruct",
"gemma-2b-it", "gemma-7b-it"
"mistral-medium", "related"
]
model_aliases = {
"mistralai/Mistral-7B-Instruct-v0.1": "mistral-7b-instruct",
"meta-llama/Llama-2-70b-chat-hf": "llama-2-70b-chat",
"mistralai/Mixtral-8x7B-Instruct-v0.1": "mixtral-8x7b-instruct",
"codellama/CodeLlama-34b-Instruct-hf": "codellama-34b-instruct"
"codellama/CodeLlama-70b-Instruct-hf": "codellama-70b-instruct",
"llava-v1.5-7b": "llava-v1.5-7b-wrapper"
}
@classmethod
@ -50,38 +51,40 @@ class PerplexityLabs(AsyncGeneratorProvider, ProviderModelMixin):
"TE": "trailers",
}
async with ClientSession(headers=headers, connector=get_connector(connector, proxy)) as session:
t = format(random.getrandbits(32), '08x')
t = format(random.getrandbits(32), "08x")
async with session.get(
f"{API_URL}?EIO=4&transport=polling&t={t}"
) as response:
text = await response.text()
sid = json.loads(text[1:])['sid']
sid = json.loads(text[1:])["sid"]
post_data = '40{"jwt":"anonymous-ask-user"}'
async with session.post(
f'{API_URL}?EIO=4&transport=polling&t={t}&sid={sid}',
f"{API_URL}?EIO=4&transport=polling&t={t}&sid={sid}",
data=post_data
) as response:
assert await response.text() == 'OK'
assert await response.text() == "OK"
async with session.ws_connect(f'{WS_URL}?EIO=4&transport=websocket&sid={sid}', autoping=False) as ws:
await ws.send_str('2probe')
assert(await ws.receive_str() == '3probe')
await ws.send_str('5')
async with session.ws_connect(f"{WS_URL}?EIO=4&transport=websocket&sid={sid}", autoping=False) as ws:
await ws.send_str("2probe")
assert(await ws.receive_str() == "3probe")
await ws.send_str("5")
assert(await ws.receive_str())
assert(await ws.receive_str() == '6')
assert(await ws.receive_str() == "6")
message_data = {
'version': '2.2',
'source': 'default',
'model': cls.get_model(model),
'messages': messages
"version": "2.5",
"source": "default",
"model": cls.get_model(model),
"messages": messages
}
await ws.send_str('42' + json.dumps(['perplexity_labs', message_data]))
await ws.send_str("42" + json.dumps(["perplexity_labs", message_data]))
last_message = 0
while True:
message = await ws.receive_str()
if message == '2':
await ws.send_str('3')
if message == "2":
if last_message == 0:
raise RuntimeError("Unknown error")
await ws.send_str("3")
continue
try:
data = json.loads(message[2:])[1]

41
g4f/Provider/You.py
View File

@ -1,21 +1,37 @@
from __future__ import annotations
import re
import json
import base64
import uuid
from aiohttp import ClientSession, FormData, BaseConnector
from ..typing import AsyncResult, Messages, ImageType, Cookies
from .base_provider import AsyncGeneratorProvider
from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
from ..providers.helper import get_connector, format_prompt
from ..image import to_bytes
from ..image import to_bytes, ImageResponse
from ..requests.defaults import DEFAULT_HEADERS
class You(AsyncGeneratorProvider):
class You(AsyncGeneratorProvider, ProviderModelMixin):
url = "https://you.com"
working = True
supports_gpt_35_turbo = True
supports_gpt_4 = True
default_model = "gpt-3.5-turbo"
models = [
"gpt-3.5-turbo",
"gpt-4",
"gpt-4-turbo",
"claude-instant",
"claude-2",
"claude-3-opus",
"claude-3-sonnet",
"gemini-pro",
"zephyr"
]
model_aliases = {
"claude-v2": "claude-2"
}
_cookies = None
_cookies_used = 0
@ -35,10 +51,15 @@ class You(AsyncGeneratorProvider):
connector=get_connector(connector, proxy),
headers=DEFAULT_HEADERS
) as client:
if image:
if image is not None:
chat_mode = "agent"
elif model == "gpt-4":
chat_mode = model
elif not model or model == cls.default_model:
chat_mode = "default"
elif model.startswith("dall-e"):
chat_mode = "create"
else:
chat_mode = "custom"
model = cls.get_model(model)
cookies = await cls.get_cookies(client) if chat_mode != "default" else None
upload = json.dumps([await cls.upload_file(client, cookies, to_bytes(image), image_name)]) if image else ""
#questions = [message["content"] for message in messages if message["role"] == "user"]
@ -63,6 +84,8 @@ class You(AsyncGeneratorProvider):
"userFiles": upload,
"selectedChatMode": chat_mode,
}
if chat_mode == "custom":
params["selectedAIModel"] = model.replace("-", "_")
async with (client.post if chat_mode == "default" else client.get)(
f"{cls.url}/api/streamingSearch",
data=data,
@ -80,7 +103,11 @@ class You(AsyncGeneratorProvider):
if event == "youChatToken" and event in data:
yield data[event]
elif event == "youChatUpdate" and "t" in data:
yield data["t"]
match = re.search(r"!\[fig\]\((.+?)\)", data["t"])
if match:
yield ImageResponse(match.group(1), messages[-1]["content"])
else:
yield data["t"]
@classmethod
async def upload_file(cls, client: ClientSession, cookies: Cookies, file: bytes, filename: str = None) -> dict:

1
g4f/Provider/__init__.py
View File

@ -46,6 +46,7 @@ from .GptGod import GptGod
from .GptTalkRu import GptTalkRu
from .Hashnode import Hashnode
from .HuggingChat import HuggingChat
from .HuggingFace import HuggingFace
from .Koala import Koala
from .Liaobots import Liaobots
from .Llama2 import Llama2

4
g4f/Provider/bing/create_images.py
View File

@ -20,7 +20,7 @@ except ImportError:
from ...providers.create_images import CreateImagesProvider
from ..helper import get_connector
from ...providers.types import ProviderType
from ...errors import MissingRequirementsError
from ...errors import MissingRequirementsError, RateLimitError
from ...webdriver import WebDriver, get_driver_cookies, get_browser
BING_URL = "https://www.bing.com"
@ -125,6 +125,8 @@ async def create_images(session: ClientSession, prompt: str, proxy: str = None,
async with session.post(url, allow_redirects=False, data=payload, timeout=timeout) as response:
response.raise_for_status()
text = (await response.text()).lower()
if "0 coins available" in text:
raise RateLimitError("No coins left. Log in with a different account or wait a while")
for error in ERRORS:
if error in text:
raise RuntimeError(f"Create images failed: {error}")

54
g4f/client.py
View File

@ -10,10 +10,12 @@ from .stubs import ChatCompletion, ChatCompletionChunk, Image, ImagesResponse
from .typing import Union, Iterator, Messages, ImageType
from .providers.types import BaseProvider, ProviderType
from .image import ImageResponse as ImageProviderResponse
from .errors import NoImageResponseError, RateLimitError, MissingAuthError
from . import get_model_and_provider, get_last_provider
from .Provider.BingCreateImages import BingCreateImages
from .Provider.needs_auth import Gemini, OpenaiChat
from .errors import NoImageResponseError
from . import get_model_and_provider, get_last_provider
from .Provider.You import You
ImageProvider = Union[BaseProvider, object]
Proxies = Union[dict, str]
@ -163,6 +165,7 @@ class Chat():
class ImageModels():
gemini = Gemini
openai = OpenaiChat
you = You
def __init__(self, client: Client) -> None:
self.client = client
@ -171,31 +174,44 @@ class ImageModels():
def get(self, name: str, default: ImageProvider = None) -> ImageProvider:
return getattr(self, name) if hasattr(self, name) else default or self.default
def iter_image_response(response: Iterator) -> Union[ImagesResponse, None]:
for chunk in list(response):
if isinstance(chunk, ImageProviderResponse):
return ImagesResponse([Image(image) for image in chunk.get_list()])
def create_image(client: Client, provider: ProviderType, prompt: str, model: str = "", **kwargs) -> Iterator:
prompt = f"create a image with: {prompt}"
return provider.create_completion(
model,
[{"role": "user", "content": prompt}],
True,
proxy=client.get_proxy(),
**kwargs
)
class Images():
def __init__(self, client: Client, provider: ImageProvider = None):
self.client: Client = client
self.provider: ImageProvider = provider
self.models: ImageModels = ImageModels(client)
def generate(self, prompt, model: str = None, **kwargs):
def generate(self, prompt, model: str = None, **kwargs) -> ImagesResponse:
provider = self.models.get(model, self.provider)
if isinstance(provider, BaseProvider) or isinstance(provider, type) and issubclass(provider, BaseProvider):
prompt = f"create a image: {prompt}"
response = provider.create_completion(
"",
[{"role": "user", "content": prompt}],
True,
proxy=self.client.get_proxy(),
**kwargs
)
if isinstance(provider, type) and issubclass(provider, BaseProvider):
response = create_image(self.client, provider, prompt, **kwargs)
else:
response = provider.create(prompt)
for chunk in response:
if isinstance(chunk, ImageProviderResponse):
images = [chunk.images] if isinstance(chunk.images, str) else chunk.images
return ImagesResponse([Image(image) for image in images])
raise NoImageResponseError()
try:
response = list(provider.create(prompt))
except (RateLimitError, MissingAuthError) as e:
# Fallback for default provider
if self.provider is None:
response = create_image(self.client, self.models.you, prompt, model or "dall-e", **kwargs)
else:
raise e
image = iter_image_response(response)
if image is None:
raise NoImageResponseError()
return image
def create_variation(self, image: ImageType, model: str = None, **kwargs):
provider = self.models.get(model, self.provider)

4
g4f/image.py
View File

@ -172,6 +172,7 @@ def process_image(image: Image, new_width: int, new_height: int) -> Image:
white = new_image('RGB', image.size, (255, 255, 255))
white.paste(image, mask=image.split()[-1])
return white
# Convert to RGB for jpg format
elif image.mode != "RGB":
image = image.convert("RGB")
return image
@ -255,6 +256,9 @@ class ImageResponse:
def get(self, key: str):
return self.options.get(key)
def get_list(self) -> list[str]:
return [self.images] if isinstance(self.images, str) else self.images
class ImageRequest:
def __init__(
self,

27
g4f/models.py
View File

@ -10,6 +10,7 @@ from .Provider import (
GeminiProChat,
ChatgptNext,
HuggingChat,
HuggingFace,
ChatgptDemo,
FreeChatgpt,
GptForLove,
@ -112,32 +113,32 @@ llama2_13b = Model(
llama2_70b = Model(
name = "meta-llama/Llama-2-70b-chat-hf",
base_provider = "meta",
best_provider = RetryProvider([Llama2, DeepInfra, HuggingChat, PerplexityLabs])
best_provider = RetryProvider([Llama2, DeepInfra, HuggingChat])
)
codellama_34b_instruct = Model(
name = "codellama/CodeLlama-34b-Instruct-hf",
base_provider = "meta",
best_provider = RetryProvider([HuggingChat, PerplexityLabs, DeepInfra])
best_provider = RetryProvider([HuggingChat, DeepInfra])
)
codellama_70b_instruct = Model(
name = "codellama/CodeLlama-70b-Instruct-hf",
base_provider = "meta",
best_provider = DeepInfra
best_provider = RetryProvider([DeepInfra, PerplexityLabs])
)
# Mistral
mixtral_8x7b = Model(
name = "mistralai/Mixtral-8x7B-Instruct-v0.1",
base_provider = "huggingface",
best_provider = RetryProvider([DeepInfra, HuggingChat, PerplexityLabs])
best_provider = RetryProvider([DeepInfra, HuggingChat, HuggingFace, PerplexityLabs])
)
mistral_7b = Model(
name = "mistralai/Mistral-7B-Instruct-v0.1",
base_provider = "huggingface",
best_provider = RetryProvider([DeepInfra, HuggingChat, PerplexityLabs])
best_provider = RetryProvider([DeepInfra, HuggingChat, HuggingFace, PerplexityLabs])
)
# Misc models
@ -184,6 +185,18 @@ claude_v2 = Model(
best_provider = RetryProvider([FreeChatgpt, Vercel])
)
claude_3_opus = Model(
name = 'claude-3-opus',
base_provider = 'anthropic',
best_provider = You
)
claude_3_sonnet = Model(
name = 'claude-3-sonnet',
base_provider = 'anthropic',
best_provider = You
)
gpt_35_turbo_16k = Model(
name = 'gpt-3.5-turbo-16k',
base_provider = 'openai',
@ -223,7 +236,7 @@ gpt_4_32k_0613 = Model(
gemini_pro = Model(
name = 'gemini-pro',
base_provider = 'google',
best_provider = RetryProvider([FreeChatgpt, GeminiProChat])
best_provider = RetryProvider([FreeChatgpt, GeminiProChat, You])
)
pi = Model(
@ -272,6 +285,8 @@ class ModelUtils:
'gemini': gemini,
'gemini-pro': gemini_pro,
'claude-v2': claude_v2,
'claude-3-opus': claude_3_opus,
'claude-3-sonnet': claude_3_sonnet,
'pi': pi
}

2
g4f/providers/base_provider.py
View File

@ -274,7 +274,7 @@ class ProviderModelMixin:
model = cls.default_model
elif model in cls.model_aliases:
model = cls.model_aliases[model]
elif model not in cls.get_models():
elif model not in cls.get_models() and cls.models:
raise ModelNotSupportedError(f"Model is not supported: {model} in: {cls.__name__}")
debug.last_model = model
return model