from smolagents import DuckDuckGoSearchTool
from langchain_community.retrievers import BM25Retriever
from smolagents import Tool
import random
from huggingface_hub import list_models


# Initialize the DuckDuckGo search tool
#search_tool = GuestInfoRetrieverTool()

class GuestInfoRetrieverTool(Tool):
    name = "guest_info_retriever"
    description = "Retrieves detailed information about gala guests based on their name or relation."
    inputs = {
        "query": {
            "type": "string",
            "description": "The name or relation of the guest you want information about."
        }
    }
    output_type = "string"

    def __init__(self, docs):
        self.is_initialized = False
        self.retriever = BM25Retriever.from_documents(docs)

    def forward(self, query: str):
        results = self.retriever.get_relevant_documents(query)
        if results:
            return "\n\n".join([doc.page_content for doc in results[:3]])
        else:
            return "No matching guest information found."

# Initialize the tool
# guest_info_tool = GuestInfoRetrieverTool(docs)


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"

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)}"


class CrashInfoRetrieverTool(Tool):
    name = "crash_info_tool"
    description = "Retrieves traffic crash details in Philadelphia by keyword (e.g. location, date, weather)."
    inputs = {
        "query": {
            "type": "string",
            "description": "Any keyword like a date, street name, or crash type."
        }
    }
    output_type = "string"

    def __init__(self, docs):
        self.retriever = BM25Retriever.from_documents(docs)

    def forward(self, query: str):
        results = self.retriever.get_relevant_documents(query)
        if results:
            return "\n\n".join([doc.page_content for doc in results[:3]])
        else:
            return "No crash data found matching your query."