import asyncio
import shutil
import time
from functools import wraps
from mimetypes import guess_type
from google.genai.types import File, Part
from ub_core.utils import get_tg_media_details
from app import BOT, Message, extra_config
from app.plugins.ai.gemini import DB_SETTINGS, AIConfig, async_client
def run_basic_check(function):
@wraps(function)
async def wrapper(bot: BOT, message: Message):
if not extra_config.GEMINI_API_KEY:
await message.reply(
"Gemini API KEY not found."
"\nGet it HERE "
"and set GEMINI_API_KEY var."
)
return
if not (message.input or message.replied):
await message.reply("Ask a Question | Reply to a Message")
return
await function(bot, message)
return wrapper
async def save_file(message: Message, check_size: bool = True) -> File | None:
media = get_tg_media_details(message)
if check_size:
assert getattr(media, "file_size", 0) <= 1048576 * 25, "File size exceeds 25mb."
download_dir = f"downloads/{time.time()}/"
try:
downloaded_file: str = await message.download(download_dir)
uploaded_file = await async_client.files.upload(
file=downloaded_file,
config={
"mime_type": getattr(media, "mime_type", None) or guess_type(downloaded_file)[0]
},
)
while uploaded_file.state.name == "PROCESSING":
await asyncio.sleep(5)
uploaded_file = await async_client.files.get(name=uploaded_file.name)
return uploaded_file
finally:
shutil.rmtree(download_dir, ignore_errors=True)
PROMPT_MAP = {
"video": "Summarize video and audio from the file",
"photo": "Summarize the image file",
"voice": (
"\nDo not summarise."
"\nTranscribe the audio file to english alphabets AS IS."
"\nTranslate it only if the audio is not english."
"\nIf the audio is in hindi: Transcribe it to hinglish without translating."
),
}
PROMPT_MAP["audio"] = PROMPT_MAP["voice"]
async def create_prompts(
message: Message, is_chat: bool = False, check_size: bool = True
) -> list[File, str] | list[Part]:
default_media_prompt = "Analyse the file and explain."
input_prompt = message.filtered_input or "answer"
# Conversational
if is_chat:
if message.media:
prompt = message.caption or PROMPT_MAP.get(message.media.value) or default_media_prompt
text_part = Part.from_text(text=prompt)
uploaded_file = await save_file(message=message, check_size=check_size)
file_part = Part.from_uri(file_uri=uploaded_file.uri, mime_type=uploaded_file.mime_type)
return [text_part, file_part]
return [Part.from_text(text=message.text)]
# Single Use
if reply := message.replied:
if reply.media:
prompt = (
message.filtered_input or PROMPT_MAP.get(reply.media.value) or default_media_prompt
)
text_part = Part.from_text(text=prompt)
uploaded_file = await save_file(message=reply, check_size=check_size)
file_part = Part.from_uri(file_uri=uploaded_file.uri, mime_type=uploaded_file.mime_type)
return [text_part, file_part]
return [Part.from_text(text=input_prompt), Part.from_text(text=str(reply.text))]
return [Part.from_text(text=input_prompt)]
@BOT.add_cmd(cmd="llms")
async def list_ai_models(bot: BOT, message: Message):
"""
CMD: LIST MODELS
INFO: List and change Gemini Models.
USAGE: .llms
"""
model_list = [
model.name.lstrip("models/")
async for model in await async_client.models.list(config={"query_base": True})
if "generateContent" in model.supported_actions
]
model_str = "\n\n".join(model_list)
update_str = (
f"Current Model: "
f"{AIConfig.TEXT_MODEL if "-i" not in message.flags else AIConfig.IMAGE_MODEL}"
f"\n\n
{model_str}"
"\n\nReply to this message with the model name to change to a different model."
)
model_info_response = await message.reply(update_str)
model_response = await model_info_response.get_response(
timeout=60, reply_to_message_id=model_info_response.id, from_user=message.from_user.id
)
if not model_response:
await model_info_response.delete()
return
if model_response.text not in model_list:
await model_info_response.edit(f"Invalid Model... Try again")
return
if "-i" in message.flags:
data_key = "image_model_name"
AIConfig.IMAGE_MODEL = model_response.text
else:
data_key = "model_name"
AIConfig.TEXT_MODEL = model_response.text
await DB_SETTINGS.add_data({"_id": "gemini_model_info", data_key: model_response.text})
resp_str = f"{model_response.text} saved as model."
await model_info_response.edit(resp_str)
await bot.log_text(text=resp_str, type=f"ai_{data_key}")