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e44cbb4 | 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 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | from smolagents import DuckDuckGoSearchTool
from smolagents import Tool
import random
# from smolagents import Tool
from huggingface_hub import list_models
from dotenv import load_dotenv
import os
load_dotenv()
from smolagents import CodeAgent, InferenceClientModel
# Load the Hugging Face API key from environment variables
api_key = os.getenv("HUGGINGFACE_API_KEY")
# Initialize the DuckDuckGo search tool
search_tool = DuckDuckGoSearchTool()
# Example usage
# results = search_tool("Who's the current President of France?")
# print(results)
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']}°C"
# Initialize the tool
weather_info_tool = WeatherInfoTool()
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)}"
# Initialize the tool
hub_stats_tool = HubStatsTool()
# Example usage
# print(hub_stats_tool("facebook")) # Example: Get the most downloaded model by Facebook
# Initialize the Hugging Face model
model = InferenceClientModel(token=api_key)
# Create Alfred with all the tools
alfred = CodeAgent(
tools=[search_tool, weather_info_tool, hub_stats_tool],
model=model
)
# Example query Alfred might receive during the gala
# response = alfred.run("I'am planning a trip to Paris. What is the weathere there, and can you tell me who the current mayor is? Also, what's the most popular machine learning model from French researchers?")
# print("🎩 Alfred's Response:")
# print(response) |