from pathlib import Path
import argparse,json
import numpy as np
import pandas as pd
import joblib

ROOT=Path(__file__).resolve().parents[1]
MODELS=ROOT/"models"

def load_artifacts():
    metadata=json.loads((MODELS/"model_metadata.json").read_text(encoding="utf-8"))
    model=joblib.load(MODELS/"final_model.joblib")
    return metadata,model

def predict_room_rate(features:dict)->dict:
    metadata,model=load_artifacts()
    required=metadata["feature_columns"]
    missing=[c for c in required if c not in features]
    if missing:
        raise ValueError(f"Missing required features: {missing}")

    row=pd.DataFrame([{c:features.get(c) for c in required}])
    if metadata["model_input_kind"]=="simple_base_price_only":
        pred=float(model.predict(row[["base_price_per_night"]])[0])
    else:
        pred=float(model.predict(row)[0])

    pred=max(0.0,pred)
    base=float(features["base_price_per_night"])
    delta=pred-base
    pct=(delta/base*100.0) if base>0 else None

    return {
        "suggested_price_per_night":round(pred,2),
        "currency":"USD",
        "base_price_per_night":round(base,2),
        "difference_from_base":round(delta,2),
        "percent_change_from_base":None if pct is None else round(pct,2),
        "model_name":metadata["selected_model"],
        "decision_support_only":True,
        "synthetic_training_data_warning":metadata["synthetic_training_data"],
        "production_warning":metadata["production_warning"]
    }

def main():
    parser=argparse.ArgumentParser()
    parser.add_argument("json_file")
    args=parser.parse_args()
    payload=json.loads(Path(args.json_file).read_text(encoding="utf-8"))
    print(json.dumps(
        predict_room_rate(payload.get("features",payload)),
        ensure_ascii=False,indent=2
    ))

if __name__=="__main__":
    main()