import torch
import sys
import pandas as pd
pd.set_option('future.no_silent_downcasting', True)
from typing import TypedDict, Optional, Tuple
import datetime
import math
import importlib.util
from huggingface_hub import hf_hub_download
import pickle


"""
Data container class representing the data shape of the synapse coming into `run_inference`
"""


class ProcessedSynapse(TypedDict):
    id: Optional[str]
    nextplace_id: Optional[str]
    property_id: Optional[str]
    listing_id: Optional[str]
    address: Optional[str]
    city: Optional[str]
    state: Optional[str]
    zip_code: Optional[str]
    price: Optional[float]
    beds: Optional[int]
    baths: Optional[float]
    sqft: Optional[int]
    lot_size: Optional[int]
    year_built: Optional[int]
    days_on_market: Optional[int]
    latitude: Optional[float]
    longitude: Optional[float]
    property_type: Optional[str]
    last_sale_date: Optional[str]
    hoa_dues: Optional[float]
    query_date: Optional[str]


"""
This class must do two things
1) The constructor must load the model
2) This class must implement a method called `run_inference` that takes the input data and returns a tuple
    of float, str representing the predicted sale price and the predicted sale date.
"""


class MLBaseModelDriver:

    def __init__(self):
        self.model, self.label_encoder, self.scaler = self.load_model()

    def load_model(self) -> Tuple[any, any, any]:
        """
        load the model and model parameters
        :return: model, label encoder, and scaler
        """
        print(f"Loading model...")
        model_file, scaler_file, label_encoders_file, model_class_file = self._download_model_files()
        model_class = self._import_model_class(model_class_file)

        model = model_class(input_dim=4)
        state_dict = torch.load(model_file, weights_only=False)
        model.load_state_dict(state_dict)
        model.eval()

        # Load additional artifacts
        with open(scaler_file, 'rb') as f:
            scaler = pickle.load(f)

        with open(label_encoders_file, 'rb') as f:
            label_encoders = pickle.load(f)

        print(f"Model Loaded.")
        return model, label_encoders, scaler

    def _download_model_files(self) -> Tuple[str, str, str, str]:
        """
        download files from hugging face
        :return: downloaded files
        """
        model_path = "Nickel5HF/NextPlace"

        # Download the model files from the Hugging Face Hub
        model_file = hf_hub_download(repo_id=model_path, filename="model_files/real_estate_model.pth")
        scaler_file = hf_hub_download(repo_id=model_path, filename="model_files/scaler.pkl")
        label_encoders_file = hf_hub_download(repo_id=model_path, filename="model_files/label_encoder.pkl")
        model_class_file = hf_hub_download(repo_id=model_path, filename="MLBaseModel.py")

        # Load the model and artifacts
        return model_file, scaler_file, label_encoders_file, model_class_file

    def _import_model_class(self, model_class_file):
        """
        import the model class and instantiate it
        :param model_class_file: file path to the model class
        :return: None
        """
        # Reference docs here: https://docs.python.org/3/library/importlib.html#importlib.util.spec_from_loader
        module_name = "MLBaseModel"
        spec = importlib.util.spec_from_file_location(module_name, model_class_file)
        model_module = importlib.util.module_from_spec(spec)
        sys.modules[module_name] = model_module
        spec.loader.exec_module(model_module)

        if hasattr(model_module, "MLBaseModel"):
            return model_module.MLBaseModel
        else:
            raise AttributeError(f"The module does not contain a class named 'MLBaseModel'")

    def run_inference(self, input_data: ProcessedSynapse) -> Tuple[float, str]:
        """
        run inference using the MLBaseModel
        :param input_data: synapse from the validator
        :return: the predicted sale price and date
        """
        input_tensor = self._preprocess_input(input_data)

        with torch.no_grad():
            prediction = self.model(input_tensor)
            predicted_sale_price, predicted_days_on_market = prediction[0].numpy()
            predicted_days_on_market = math.floor(predicted_days_on_market)
            predicted_sale_date = self._sale_date_predictor(input_data['days_on_market'], predicted_days_on_market)

        return float(predicted_sale_price), predicted_sale_date.strftime("%Y-%m-%d")

    def _sale_date_predictor(self, days_on_market: Optional[int], predicted_days_on_market: int) -> datetime.date:
        """
        convert predicted days on market to a sale date
        :param days_on_market: number of days this home has been on the market
        :param predicted_days_on_market: the predicted number of days for this home on the market
        :return: the predicted sale date
        """
        if days_on_market is None:
            days_on_market = 0
            
        if days_on_market < predicted_days_on_market:
            days_until_sale = predicted_days_on_market - days_on_market
            sale_date = datetime.date.today() + datetime.timedelta(days=days_until_sale)
            return sale_date
        else:
            return datetime.date.today() + datetime.timedelta(days=1)

    def _preprocess_input(self, data: ProcessedSynapse) -> torch.tensor:
        """
        preprocess the input for inference
        :param data: synapse from the validator
        :return: tensor representing the synapse
        """
        df = pd.DataFrame([data])
    
        # Set defaults
        default_beds = 3
        default_sqft = 1500.0
        default_property_type = 6  # ensure it's int, not str
        default_price = 300000.0  # optional: fallback if price is NaN
    
        # Clean and fill fields
        df['beds'] = pd.to_numeric(df['beds'], errors='coerce').fillna(default_beds).astype(int)
        df['sqft'] = pd.to_numeric(df['sqft'], errors='coerce').fillna(default_sqft)
        df['price'] = pd.to_numeric(df['price'], errors='coerce').fillna(default_price)
        df['property_type'] = pd.to_numeric(df['property_type'], errors='coerce').fillna(default_property_type).astype(int)
    
        # Scale numeric features
        df[['sqft', 'price']] = self.scaler.transform(df[['sqft', 'price']])
    
        # Final input matrix
        X = df[['beds', 'sqft', 'property_type', 'price']].astype('float32')
        input_tensor = torch.tensor(X.values, dtype=torch.float32)
        return input_tensor