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# from smolagents import DuckDuckGoSearchTool | |
# from smolagents import Tool | |
# import random | |
# from huggingface_hub import list_models | |
# # Initialize the DuckDuckGo search tool | |
# #search_tool = DuckDuckGoSearchTool() | |
# class WeatherInfoTool(Tool): | |
# name = "weather_info" | |
# description = "Fetches dummy weather information for a given location." | |
# inputs = { | |
# "location": { | |
# "type": "string", | |
# "description": "The location to get weather information for." | |
# } | |
# } | |
# output_type = "string" | |
# def forward(self, location: str): | |
# # Dummy weather data | |
# weather_conditions = [ | |
# {"condition": "Rainy", "temp_c": 15}, | |
# {"condition": "Clear", "temp_c": 25}, | |
# {"condition": "Windy", "temp_c": 20} | |
# ] | |
# # Randomly select a weather condition | |
# data = random.choice(weather_conditions) | |
# return f"Weather in {location}: {data['condition']}, {data['temp_c']}Β°C" | |
# class HubStatsTool(Tool): | |
# name = "hub_stats" | |
# description = "Fetches the most downloaded model from a specific author on the Hugging Face Hub." | |
# inputs = { | |
# "author": { | |
# "type": "string", | |
# "description": "The username of the model author/organization to find models from." | |
# } | |
# } | |
# output_type = "string" | |
# def forward(self, author: str): | |
# try: | |
# # List models from the specified author, sorted by downloads | |
# 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)}" | |
from langchain.tools import Tool | |
from huggingface_hub import list_models | |
import random | |
from langchain_community.tools import DuckDuckGoSearchRun | |
search_tool = DuckDuckGoSearchRun() | |
results = search_tool.invoke("Who's the current President of France?") | |
print(results) | |
def get_weather_info(location: str) -> str: | |
"""Fetches dummy weather information for a given location.""" | |
# Dummy weather data | |
weather_conditions = [ | |
{"condition": "Rainy", "temp_c": 15}, | |
{"condition": "Clear", "temp_c": 25}, | |
{"condition": "Windy", "temp_c": 20} | |
] | |
# Randomly select a weather condition | |
data = random.choice(weather_conditions) | |
return f"Weather in {location}: {data['condition']}, {data['temp_c']}Β°C" | |
# Initialize the tool | |
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: | |
# List models from the specified author, sorted by downloads | |
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)}" | |
# Initialize the tool | |
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." | |
) | |
# Example usage | |
print(hub_stats_tool("facebook")) # Example: Get the most downloaded model by Facebook |