import os
import numpy as np
import pandas as pd
import joblib
import gradio as gr
import spaces

ARTIFACTS_DIR = os.path.join(os.path.dirname(__file__), "ml", "artifacts")

processor     = joblib.load(os.path.join(ARTIFACTS_DIR, "manual_processor.joblib"))
stack         = joblib.load(os.path.join(ARTIFACTS_DIR, "stack.joblib"))
core_features = joblib.load(os.path.join(ARTIFACTS_DIR, "core_features.joblib"))


@spaces.GPU
def predict(
    GrLivArea, TotalBsmtSF, GarageArea, LotArea, LotFrontage,
    floor_1st, floor_2nd, WoodDeckSF, OpenPorchSF,
    OverallQual, OverallCond, KitchenQual, ExterQual, BsmtQual,
    HeatingQC, FireplaceQu, FullBath, HalfBath, BsmtFullBath,
    TotRmsAbvGrd, Fireplaces, YearBuilt, YearRemodAdd, YrSold,
    Neighborhood, MSZoning, BldgType, Foundation, GarageQual, GarageCond, Electrical,
):
    inputs = {
        "GrLivArea": float(GrLivArea),
        "TotalBsmtSF": float(TotalBsmtSF),
        "LotArea": float(LotArea),
        "GarageArea": float(GarageArea),
        "PoolArea": 0.0,
        "LotFrontage": float(LotFrontage) if LotFrontage else None,
        "2ndFlrSF": float(floor_2nd),
        "LowQualFinSF": 0.0,
        "BsmtUnfSF": 0.0,
        "1stFlrSF": float(floor_1st),
        "WoodDeckSF": float(WoodDeckSF),
        "OpenPorchSF": float(OpenPorchSF),
        "EnclosedPorch": 0.0,
        "3SsnPorch": 0.0,
        "ScreenPorch": 0.0,
        "FullBath": int(FullBath),
        "HalfBath": int(HalfBath),
        "BsmtFullBath": int(BsmtFullBath),
        "BsmtHalfBath": 0,
        "TotRmsAbvGrd": int(TotRmsAbvGrd),
        "Fireplaces": int(Fireplaces),
        "YearBuilt": int(YearBuilt),
        "YrSold": int(YrSold),
        "YearRemodAdd": int(YearRemodAdd),
        "OverallQual": int(OverallQual),
        "OverallCond": int(OverallCond),
        "HeatingQC": HeatingQC,
        "BsmtQual": None if BsmtQual == "None" else BsmtQual,
        "PoolQC": None,
        "ExterQual": ExterQual,
        "KitchenQual": KitchenQual,
        "Functional": "Typ",
        "FireplaceQu": None if FireplaceQu == "None" else FireplaceQu,
        "BsmtCond": "TA",
        "ExterCond": "TA",
        "Neighborhood": Neighborhood,
        "MSZoning": MSZoning,
        "MSSubClass": 20,
        "LandSlope": "Gtl",
        "Alley": np.nan,
        "LandContour": "Lvl",
        "BldgType": BldgType,
        "Condition1": "Norm",
        "RoofStyle": "Gable",
        "Foundation": Foundation,
        "SaleCondition": "Normal",
        "Exterior1st": "VinylSd",
        "Utilities": "AllPub",
        "Electrical": Electrical,
        "GarageQual": GarageQual,
        "GarageCond": GarageCond,
    }

    df = pd.DataFrame([inputs])
    X = df[core_features].copy()
    X_processed = processor.transform(X)
    log_pred = stack.predict(X_processed)
    price = float(np.expm1(log_pred)[0])
    return f"${price:,.0f}"


with gr.Blocks(title="House Price Predictor") as demo:
    gr.Markdown("# 🏠 House Price Predictor")
    gr.Markdown("Stacking ensemble: GradientBoosting + CatBoost + KernelRidge · **Top 16 / 4,000+** on Kaggle Housing Prices Competition")

    with gr.Row():
        with gr.Column():
            gr.Markdown("### Size")
            GrLivArea   = gr.Slider(300, 6000, value=1500, step=50, label="Above-grade living area (sq ft)")
            TotalBsmtSF = gr.Slider(0, 3000, value=800, step=50, label="Total basement area (sq ft)")
            GarageArea  = gr.Slider(0, 1500, value=400, step=50, label="Garage area (sq ft)")
            LotArea     = gr.Slider(1000, 100000, value=8000, step=500, label="Lot area (sq ft)")
            LotFrontage = gr.Slider(0, 200, value=65, step=5, label="Lot frontage (ft) — 0 if unknown")
            gr.Markdown("### Floors & Extras")
            floor_1st   = gr.Slider(0, 4000, value=856, step=50, label="1st floor area (sq ft)")
            floor_2nd   = gr.Slider(0, 2000, value=0, step=50, label="2nd floor area (sq ft)")
            WoodDeckSF  = gr.Slider(0, 800, value=0, step=10, label="Wood deck (sq ft)")
            OpenPorchSF = gr.Slider(0, 500, value=0, step=10, label="Open porch (sq ft)")

        with gr.Column():
            gr.Markdown("### Quality & Condition")
            OverallQual = gr.Slider(1, 10, value=6, step=1, label="Overall quality (1–10)")
            OverallCond = gr.Slider(1, 9, value=5, step=1, label="Overall condition (1–9)")
            KitchenQual = gr.Dropdown(["Ex", "Gd", "TA", "Fa", "Po"], value="TA", label="Kitchen quality")
            ExterQual   = gr.Dropdown(["Ex", "Gd", "TA", "Fa", "Po"], value="TA", label="Exterior quality")
            BsmtQual    = gr.Dropdown(["Ex", "Gd", "TA", "Fa", "Po", "None"], value="TA", label="Basement quality")
            HeatingQC   = gr.Dropdown(["Ex", "Gd", "TA", "Fa", "Po"], value="TA", label="Heating quality")
            FireplaceQu = gr.Dropdown(["None", "Ex", "Gd", "TA", "Fa", "Po"], value="None", label="Fireplace quality")
            gr.Markdown("### Bathrooms & Rooms")
            FullBath     = gr.Slider(0, 4, value=2, step=1, label="Full baths (above grade)")
            HalfBath     = gr.Slider(0, 2, value=0, step=1, label="Half baths (above grade)")
            BsmtFullBath = gr.Slider(0, 2, value=0, step=1, label="Basement full baths")
            TotRmsAbvGrd = gr.Slider(2, 14, value=7, step=1, label="Total rooms above grade")
            Fireplaces   = gr.Slider(0, 4, value=0, step=1, label="Fireplaces")

        with gr.Column():
            gr.Markdown("### Year & Location")
            YearBuilt    = gr.Slider(1870, 2024, value=2000, step=1, label="Year built")
            YearRemodAdd = gr.Slider(1950, 2024, value=2000, step=1, label="Year remodelled")
            YrSold       = gr.Slider(2006, 2030, value=2024, step=1, label="Year sold")
            Neighborhood = gr.Dropdown([
                "NAmes", "CollgCr", "OldTown", "Edwards", "Somerst", "NridgHt",
                "Gilbert", "Sawyer", "NWAmes", "SawyerW", "BrkSide", "Crawfor",
                "Mitchel", "NoRidge", "Timber", "IDOTRR", "ClearCr", "StoneBr",
                "SWISU", "MeadowV", "Blmngtn", "BrDale", "Veenker", "NPkVill", "Blueste",
            ], value="NAmes", label="Neighborhood")
            MSZoning   = gr.Dropdown(["RL", "RM", "FV", "RH", "C (all)"], value="RL", label="Zoning")
            BldgType   = gr.Dropdown(["1Fam", "2fmCon", "Duplex", "TwnhsE", "Twnhs"], value="1Fam", label="Building type")
            Foundation = gr.Dropdown(["PConc", "CBlock", "BrkTil", "Wood", "Slab", "Stone"], value="PConc", label="Foundation")
            gr.Markdown("### Garage & Utilities")
            GarageQual = gr.Dropdown(["TA", "Gd", "Fa", "Ex", "Po"], value="TA", label="Garage quality")
            GarageCond = gr.Dropdown(["TA", "Gd", "Fa", "Ex", "Po"], value="TA", label="Garage condition")
            Electrical = gr.Dropdown(["SBrkr", "FuseA", "FuseF", "FuseP", "Mix"], value="SBrkr", label="Electrical system")

    predict_btn = gr.Button("Predict Price", variant="primary")
    output = gr.Text(label="Estimated Sale Price", text_align="center")

    predict_btn.click(
        fn=predict,
        inputs=[
            GrLivArea, TotalBsmtSF, GarageArea, LotArea, LotFrontage,
            floor_1st, floor_2nd, WoodDeckSF, OpenPorchSF,
            OverallQual, OverallCond, KitchenQual, ExterQual, BsmtQual,
            HeatingQC, FireplaceQu, FullBath, HalfBath, BsmtFullBath,
            TotRmsAbvGrd, Fireplaces, YearBuilt, YearRemodAdd, YrSold,
            Neighborhood, MSZoning, BldgType, Foundation, GarageQual, GarageCond, Electrical,
        ],
        outputs=output,
    )

demo.launch()