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)