| 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() |
|
|
