File size: 1,732 Bytes
22507c4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
import os
from core.llms import LLM
from dotenv import load_dotenv
from typing import Annotated, Optional
# from function_schema import Doc
from core.llms.utils import user_message_with_images
load_dotenv('../.global_env')
# def get_weather(
# city: Annotated[str, "The city to get the weather for"], # <- string value of Annotated is used as a description
# unit: Annotated[Optional[str], "The unit to return the temperature in"] = "celcius",
# ) -> str:
# """Returns the weather for the given city."""
# return f"Weather for {city} is 20°C"
# def get_distance(city1: Annotated[str, 'city to start journey from'], city2: Annotated[str, 'city where journey ends']) -> float:
# ''' Returns distance between two cities '''
# return f"{city1} --- {city2}: 10 KM"
# tools = {"get_weather": get_weather, 'get_distance':get_distance}
# models= [
# ('gemini/gemini-1.5-flash', 'GEMINI_API_KEY'),
# ('groq/llava-v1.5-7b-4096-preview', 'GROQ_API_KEY')
# ]
# model , api_key = models[0]
# api_key = os.getenv(api_key)
# llm = LLM(api_key=api_key, model=model)
# messages = [
# {"role": "system", "content": "You are a helpful assistant."},
# {"role": "user", "content": "Who won the world series in 2020?"},
# {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
# # {"role": "user", "content": "whats weather in new york, what is distance between new york and las vegas"},
# # user_message_with_images(
# # 'explain this image',
# # file_path_list = ['./hehe.jpg'],
# # max_size_px=512,
# # )
# ]
# response = llm.chat(messages)
# print('response: ', response)
from app.app import main
if __name__ == '__main__':
main() |