tools.py

import datasets
import random
from langchain_core.documents import Document
from langchain_core.tools import Tool
from huggingface_hub import list_models

# Load the dataset
guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")

# Convert dataset entries into Document objects
docs = [
    Document(
        page_content="\n".join([
            f"Name: {guest['name']}",
            f"Relation: {guest['relation']}",
            f"Description: {guest['description']}",
            f"Email: {guest['email']}"
        ]),
        metadata={"name": guest["name"]}
    )
    for guest in guest_dataset
]

def get_weather_info(location: str) -> str:
    """Fetches dummy weather information for a given location."""
    weather_conditions = [
        {"condition": "Rainy", "temp_c": 15},
        {"condition": "Clear", "temp_c": 25},
        {"condition": "Windy", "temp_c": 20}
    ]
    data = random.choice(weather_conditions)
    return f"Weather in {location}: {data['condition']}, {data['temp_c']}°C"

weather_info_tool = Tool(
    name="get_weather_info",
    func=get_weather_info,
    description="Fetches dummy weather information for a given location."
)

def get_hub_stats(author: str) -> str:
    """Fetches the most downloaded model from a specific author on the Hugging Face Hub."""
    try:
        models = list(list_models(author=author, sort="downloads", direction=-1, limit=1))
        if models:
            model = models[0]
            return f"The most downloaded model by {author} is {model.id} with {model.downloads:,} downloads."
        else:
            return f"No models found for author {author}."
    except Exception as e:
        return f"Error fetching models for {author}: {str(e)}"

hub_stats_tool = Tool(
    name="get_hub_stats",
    func=get_hub_stats,
    description="Fetches the most downloaded model from a specific author on the Hugging Face Hub."
)