Add app + predictor with HF artifact download

app.py

@@ -114,4 +114,6 @@ with gr.Blocks(title="Loan Approval Predictor") as demo:
 
             btn_batch.click(fn=run_batch, inputs=file_in, outputs=[preview, file_out])
 
-demo.launch(server_name="0.0.0.0", server_port=7860)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

predictor.py

@@ -1,28 +1,26 @@
 from __future__ import annotations
 
-from typing import Any, Dict
 from pathlib import Path
+from typing import Any, Dict
+from shutil import copyfile
 
 import joblib
 import numpy as np
 import pandas as pd
 from huggingface_hub import hf_hub_download
 
+REPO_ID = "Fola-lad/loan-artifacts"
 
 ARTIFACT_DIR = Path("artifacts")
 ARTIFACT_DIR.mkdir(exist_ok=True)
 
-REPO_ID = "Fola-lad/loan-artifacts"  # model repo
-
-
 def _get_artifact(filename: str) -> Path:
-    path = hf_hub_download(repo_id=REPO_ID, filename=filename)
+    downloaded = hf_hub_download(repo_id=REPO_ID, filename=filename)
     dst = ARTIFACT_DIR / filename
     if not dst.exists():
-        Path(path).replace(dst)
+        copyfile(downloaded, dst)
     return dst
 
-
 missing_value_handler = joblib.load(_get_artifact("missing_value_handler.joblib"))
 preprocessor = joblib.load(_get_artifact("preprocessor.joblib"))
 model = joblib.load(_get_artifact("loan_model.joblib"))
@@ -44,13 +42,11 @@ CLEANED_FEATURE_COLS = [
 
 EXPECTED_INPUT_COLS = ["Loan_ID"] + CLEANED_FEATURE_COLS
 
-
 def _safe_log(series: pd.Series) -> np.ndarray:
     v = pd.to_numeric(series, errors="coerce").fillna(0).to_numpy(dtype=float)
     v = np.where(v > 0, v, 1.0)
     return np.log(v)
 
-
 def feature_engineering(df: pd.DataFrame) -> pd.DataFrame:
     df = df.copy()
 
@@ -70,7 +66,6 @@ def feature_engineering(df: pd.DataFrame) -> pd.DataFrame:
     )
     return df
 
-
 def _normalize_input(df: pd.DataFrame) -> pd.DataFrame:
     df = df.copy()
 
@@ -88,7 +83,6 @@ def _normalize_input(df: pd.DataFrame) -> pd.DataFrame:
 
     return df
 
-
 def _prepare_features(raw_df: pd.DataFrame):
     raw_df = _normalize_input(raw_df)
 
@@ -100,7 +94,6 @@ def _prepare_features(raw_df: pd.DataFrame):
 
     return preprocessor.transform(fe_df)
 
-
 def predict_one(payload: Dict[str, Any]) -> Dict[str, Any]:
     df = pd.DataFrame([payload])
     X = _prepare_features(df)
@@ -111,7 +104,6 @@ def predict_one(payload: Dict[str, Any]) -> Dict[str, Any]:
 
     return {"Loan_Status": str(label), "confidence": float(np.max(proba))}
 
-
 def predict_batch(df: pd.DataFrame) -> pd.DataFrame:
     X = _prepare_features(df)