mirror of
https://github.com/xtekky/gpt4free.git
synced 2024-12-25 12:16:17 +03:00
ea1448001d
Add slim docker image with google-chrome usage, Add the new docker images to publish worklow, Update requirements.txt and pip requirements
111 lines
4.5 KiB
Python
111 lines
4.5 KiB
Python
from __future__ import annotations
|
|
|
|
import base64
|
|
import json
|
|
from aiohttp import ClientSession, BaseConnector
|
|
|
|
from ...typing import AsyncResult, Messages, ImageType
|
|
from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
|
|
from ...image import to_bytes, is_accepted_format
|
|
from ...errors import MissingAuthError
|
|
from ..helper import get_connector
|
|
|
|
class GeminiPro(AsyncGeneratorProvider, ProviderModelMixin):
|
|
label = "Gemini API"
|
|
url = "https://ai.google.dev"
|
|
working = True
|
|
supports_message_history = True
|
|
needs_auth = True
|
|
default_model = "gemini-1.5-pro"
|
|
default_vision_model = default_model
|
|
models = [default_model, "gemini-pro", "gemini-1.5-flash", "gemini-1.5-flash-8b"]
|
|
|
|
@classmethod
|
|
async def create_async_generator(
|
|
cls,
|
|
model: str,
|
|
messages: Messages,
|
|
stream: bool = False,
|
|
proxy: str = None,
|
|
api_key: str = None,
|
|
api_base: str = "https://generativelanguage.googleapis.com/v1beta",
|
|
use_auth_header: bool = False,
|
|
image: ImageType = None,
|
|
connector: BaseConnector = None,
|
|
**kwargs
|
|
) -> AsyncResult:
|
|
model = cls.get_model(model)
|
|
|
|
if not api_key:
|
|
raise MissingAuthError('Add a "api_key"')
|
|
|
|
headers = params = None
|
|
if use_auth_header:
|
|
headers = {"Authorization": f"Bearer {api_key}"}
|
|
else:
|
|
params = {"key": api_key}
|
|
|
|
method = "streamGenerateContent" if stream else "generateContent"
|
|
url = f"{api_base.rstrip('/')}/models/{model}:{method}"
|
|
async with ClientSession(headers=headers, connector=get_connector(connector, proxy)) as session:
|
|
contents = [
|
|
{
|
|
"role": "model" if message["role"] == "assistant" else "user",
|
|
"parts": [{"text": message["content"]}]
|
|
}
|
|
for message in messages
|
|
if message["role"] != "system"
|
|
]
|
|
if image is not None:
|
|
image = to_bytes(image)
|
|
contents[-1]["parts"].append({
|
|
"inline_data": {
|
|
"mime_type": is_accepted_format(image),
|
|
"data": base64.b64encode(image).decode()
|
|
}
|
|
})
|
|
data = {
|
|
"contents": contents,
|
|
"generationConfig": {
|
|
"stopSequences": kwargs.get("stop"),
|
|
"temperature": kwargs.get("temperature"),
|
|
"maxOutputTokens": kwargs.get("max_tokens"),
|
|
"topP": kwargs.get("top_p"),
|
|
"topK": kwargs.get("top_k"),
|
|
}
|
|
}
|
|
system_prompt = "\n".join(
|
|
message["content"]
|
|
for message in messages
|
|
if message["role"] == "system"
|
|
)
|
|
if system_prompt:
|
|
data["system_instruction"] = {"parts": {"text": system_prompt}}
|
|
async with session.post(url, params=params, json=data) as response:
|
|
if not response.ok:
|
|
data = await response.json()
|
|
data = data[0] if isinstance(data, list) else data
|
|
raise RuntimeError(f"Response {response.status}: {data['error']['message']}")
|
|
if stream:
|
|
lines = []
|
|
async for chunk in response.content:
|
|
if chunk == b"[{\n":
|
|
lines = [b"{\n"]
|
|
elif chunk == b",\r\n" or chunk == b"]":
|
|
try:
|
|
data = b"".join(lines)
|
|
data = json.loads(data)
|
|
yield data["candidates"][0]["content"]["parts"][0]["text"]
|
|
except:
|
|
data = data.decode(errors="ignore") if isinstance(data, bytes) else data
|
|
raise RuntimeError(f"Read chunk failed: {data}")
|
|
lines = []
|
|
else:
|
|
lines.append(chunk)
|
|
else:
|
|
data = await response.json()
|
|
candidate = data["candidates"][0]
|
|
if candidate["finishReason"] == "STOP":
|
|
yield candidate["content"]["parts"][0]["text"]
|
|
else:
|
|
yield candidate["finishReason"] + ' ' + candidate["safetyRatings"] |