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from __future__ import annotations
import base64
import json
import requests
from typing import Optional
from aiohttp import ClientSession, BaseConnector
from ...typing import AsyncResult, Messages, MediaListType
from ...image import to_bytes, is_data_an_media
from ...errors import MissingAuthError, ModelNotFoundError
from ...requests import raise_for_status, iter_lines
from ...providers.response import Usage, FinishReason
from ...image.copy_images import save_response_media
from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
from ..helper import get_connector, to_string, format_media_prompt, get_system_prompt
from ... import debug
class GeminiPro(AsyncGeneratorProvider, ProviderModelMixin):
label = "Google Gemini API"
url = "https://ai.google.dev"
login_url = "https://aistudio.google.com/u/0/apikey"
api_base = "https://generativelanguage.googleapis.com/v1beta"
active_by_default = True
working = True
supports_message_history = True
supports_system_message = True
needs_auth = True
default_model = "gemini-2.5-flash"
default_vision_model = default_model
fallback_models = [
"gemini-2.0-flash",
"gemini-2.0-flash-lite",
"gemini-2.0-flash-thinking-exp",
"gemini-2.5-flash",
"gemma-3-1b-it",
"gemma-3-12b-it",
"gemma-3-27b-it",
"gemma-3-4b-it",
"gemma-3n-e2b-it",
"gemma-3n-e4b-it",
]
@classmethod
def get_models(cls, api_key: str = None, api_base: str = api_base) -> list[str]:
if not api_key:
return cls.fallback_models
if not cls.models:
try:
url = f"{cls.api_base if not api_base else api_base}/models"
response = requests.get(url, params={"key": api_key})
raise_for_status(response)
data = response.json()
cls.models = [
model.get("name").split("/").pop()
for model in data.get("models")
if "generateContent" in model.get("supportedGenerationMethods")
]
cls.models.sort()
cls.live += 1
except Exception as e:
debug.error(e)
if api_key is not None:
raise MissingAuthError("Invalid API key")
return cls.fallback_models
return cls.models
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
stream: bool = False,
proxy: str = None,
api_key: str = None,
api_base: str = api_base,
use_auth_header: bool = False,
media: MediaListType = None,
tools: Optional[list] = None,
connector: BaseConnector = None,
**kwargs
) -> AsyncResult:
if not api_key:
raise MissingAuthError('Add a "api_key"')
try:
model = cls.get_model(model, api_key=api_key, api_base=api_base)
except ModelNotFoundError:
pass
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": to_string(message["content"])}]
}
for message in messages
if message["role"] not in ["system", "developer"]
]
if media is not None:
if not contents:
contents.append({"role": "user", "parts": []})
for media_data, filename in media:
media_data = to_bytes(media_data)
contents[-1]["parts"].append({
"inline_data": {
"mime_type": is_data_an_media(media_data, filename),
"data": base64.b64encode(media_data).decode()
}
})
responseModalities = {"responseModalities": ["AUDIO"]} if "tts" in model else {}
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"),
**responseModalities,
},
"tools": [{
"function_declarations": [{
"name": tool["function"]["name"],
"description": tool["function"]["description"],
"parameters": {
"type": "object",
"properties": {key: {
"type": value["type"],
"description": value["title"]
} for key, value in tool["function"]["parameters"]["properties"].items()}
},
} for tool in tools]
}] if tools else None
}
system_prompt = get_system_prompt(messages)
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 = []
buffer = b""
async for chunk in iter_lines(response.content.iter_any()):
buffer += chunk
if chunk == b"[{":
lines = [b"{"]
elif chunk == b"," or chunk == b"]":
try:
data = json.loads(b"".join(lines))
content = data["candidates"][0]["content"]
if "parts" in content and content["parts"]:
if "text" in content["parts"][0]:
yield content["parts"][0]["text"]
elif "inlineData" in content["parts"][0]:
async for media in save_response_media(
content["parts"][0]["inlineData"], format_media_prompt(messages)
):
yield media
if "finishReason" in data["candidates"][0]:
yield FinishReason(data["candidates"][0]["finishReason"].lower())
usage = data.get("usageMetadata")
if usage:
yield Usage(
prompt_tokens=usage.get("promptTokenCount"),
completion_tokens=usage.get("candidatesTokenCount"),
total_tokens=usage.get("totalTokenCount")
)
except Exception as e:
raise RuntimeError(f"Read chunk failed") from e
lines = []
else:
lines.append(chunk)
else:
data = await response.json()
candidate = data["candidates"][0]
if "content" in candidate:
content = candidate["content"]
if "parts" in content and content["parts"]:
for part in content["parts"]:
if "text" in part:
yield part["text"]
elif "inlineData" in part:
async for media in save_response_media(
part["inlineData"], format_media_prompt(messages)
):
yield media
if "finishReason" in candidate:
yield FinishReason(candidate["finishReason"].lower())
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