from smolagents import Tool
from langchain_community.retrievers import BM25Retriever
from langchain.docstore.document import Document
from tools import CrashInfoRetrieverTool  # Import the CrashInfoRetrieverTool
import datasets
import pandas as pd
import requests

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."


def load_guest_dataset():
    # 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
    ]

    # Return the tool
    return GuestInfoRetrieverTool(docs)

def load_crash_data():
    # Public Philly crash fatality (csv)
    url = "https://phl.carto.com/api/v2/sql?filename=fatal_crashes&format=csv&skipfields=cartodb_id,the_geom,the_geom_webmercator&q=SELECT%20*,%20ST_Y(the_geom)%20AS%20lat,%20ST_X(the_geom)%20AS%20lng%20FROM%20fatal_crashes"
    response = requests.get(url)
    with open("fatal_crashes.csv", "wb") as f:
        f.write(response.content)

    df = pd.read_csv("fatal_crashes.csv")

    # Convert to LangChain documents
    docs = []
    for _, row in df.iterrows():
        content = "\n".join([
            f"Year: {row.get('year', 'N/A')}",
            f"Street: {row.get('primary_st', 'N/A')}",
            f"Age: {row.get('age', 'N/A')}",
            f"Arrest: {row.get('arrest_yes', 'N/A')}",
            f"Primary Vehicle: {row.get('veh1', 'N/A')}",
            f"Crash Type: {row.get('veh2', 'N/A')}",
            f"Outcome: {row.get('investigat', 'N/A')}",
            f"RecordID: {row.get('objectid', 'N/A')}"
        ])
        docs.append(Document(page_content=content, metadata={"id": row.get("id", "unknown")}))

    # Return an instance of CrashInfoRetrieverTool
    return CrashInfoRetrieverTool(docs)