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import os
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from loguru import logger
from pydantic import SecretStr
@tool("produce_classifier_tool", parse_docstring=True)
def produce_classifier(food_name: str) -> str:
"""
Classifies a food item as either 'fruit' or 'vegetable' from a botanical perspective.
Args:
food_name (str): The name of the food item to classify.
Returns:
str: The classification of the food item, either 'fruit' or 'vegetable'.
"""
logger.info(f"use produce_classifier_tool with param: {food_name}")
chat = ChatOpenAI(
model="gpt-4o-mini",
temperature=0,
api_key = SecretStr(os.environ['OPENAI_API_KEY'])
)
prompt = (f"From a botanical perspective, classify {food_name} as either 'fruit' or 'vegetable'. "
#f"If it's not a produce name, classify as 'neither'. "
f"Respond only with a JSON in this exact format: "
f"{{\"name\": \"{food_name}\", \"kind\": \"[classification]\"}}, "
f"where [classification] should be replaced with just 'fruit' or 'vegetable'." #, or 'neither'. "
f"No other text or explanation.")
return chat.invoke(prompt).content
def _print_produce_kind(food_name: str):
print(f'{food_name}: {produce_classifier.invoke(food_name)}')
if __name__ == "__main__":
#_print_produce_kind("orange")
#_print_produce_kind("sweet potatoes")
#_print_produce_kind("bell pepper")
#_print_produce_kind("egg")
#_print_produce_kind("table")
_print_produce_kind("zucchini")
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