| |
| import os |
| import json |
| import pandas as pd |
| import joblib |
| from typing import Optional, List |
|
|
| from huggingface_hub import hf_hub_download |
| from utils.config import AppConfig |
| from utils.tracing import Tracer |
|
|
|
|
| class PredictTool: |
| """ |
| Loads a sklearn-compatible tabular model from a HF repo and runs predictions. |
| """ |
| def __init__(self, cfg: AppConfig, tracer: Tracer): |
| self.cfg = cfg |
| self.tracer = tracer |
| self._model = None |
| self._feature_meta = {} |
| self._pred_col = "prediction" |
| self._feature_order: Optional[List[str]] = None |
|
|
| def _ensure_loaded(self): |
| if self._model is not None: |
| return |
|
|
| token = os.getenv("HF_TOKEN") |
| repo = self.cfg.hf_model_repo |
|
|
| model_path = hf_hub_download(repo_id=repo, filename="model.pkl", token=token) |
| self._model = joblib.load(model_path) |
|
|
| try: |
| meta_path = hf_hub_download(repo_id=repo, filename="feature_metadata.json", token=token) |
| with open(meta_path, "r", encoding="utf-8") as f: |
| self._feature_meta = json.load(f) or {} |
| except Exception: |
| self._feature_meta = {} |
|
|
| self._pred_col = self._feature_meta.get("prediction_column", "prediction") |
| self._feature_order = self._feature_meta.get("feature_order") |
|
|
| def _select_features(self, df: pd.DataFrame) -> pd.DataFrame: |
| if self._feature_order: |
| missing = [c for c in self._feature_order if c not in df.columns] |
| if missing: |
| raise ValueError(f"Missing required features for model: {missing}") |
| return df[self._feature_order].copy() |
| return df.copy() |
|
|
| def run(self, df: Optional[pd.DataFrame]) -> pd.DataFrame: |
| self._ensure_loaded() |
| if df is None or len(df) == 0: |
| return pd.DataFrame() |
|
|
| X = self._select_features(df) |
| model = self._model |
|
|
| if hasattr(model, "predict_proba"): |
| preds = model.predict_proba(X)[:, -1] |
| elif hasattr(model, "decision_function"): |
| import numpy as np |
| raw = model.decision_function(X) |
| preds = 1 / (1 + np.exp(-raw)) |
| else: |
| preds = model.predict(X) |
|
|
| out = df.copy() |
| out[self._pred_col] = preds |
| try: |
| self.tracer.trace_event("predict", {"rows": len(out)}) |
| except Exception: |
| pass |
| return out |
|
|
