from smolagents import CodeAgent  
from smolagents import HfApiModel
from smolagents import tool
#from smolagents import DuckDuckGoSearchTool
import os
from datasets import load_dataset
dataset = load_dataset("bprateek/amazon_product_description", revision="main", token=os.getenv('Testing'))


@tool
def predict_price_tool(arg1:str)-> float: #it's import to specify the return type
    #Keep this format for the description / args / args description but feel free to modify the tool
    """This is a tool which look on a dataset as defined by user input and give you a price 
    Args:
        arg1: the category of product
    """
    filter_dataset = dataset['Category' == arg1]
    filter_dataset_min = filter_dataset['Selling Price'].min()
    filter_dataset_max = filter_dataset['Selling Price'].min()

    return (filter_dataset_min + filter_dataset_max )/2


#Agent Example 
model = HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct", token=os.getenv('Testing'))
agent = CodeAgent(tools=[predict_price_tool], model=model)
agent.run("Get price quoatition for catageory = Toys & Games | Arts & Crafts | Craft Kits | Paper Craft")

# Access HF Hub 
#from huggingface_hub import list_models

#for model in list_models(limit=10, sort="downloads", direction=-1):
#   print(model.id, model.downloads)