| """ |
| HuggingFace Inference Endpoint handler for the Menon nb-bert relevance scorer. |
| |
| HuggingFace's Endpoints platform expects a class named `EndpointHandler` with: |
| - __init__(self, path: str) → load model from `path` |
| - __call__(self, data: dict) → run inference and return results |
| |
| This handler wraps the same pipeline as `score.py` so the endpoint behaves |
| identically to local scoring: needs_review gate → tokenize → model → threshold. |
| |
| REQUEST FORMAT |
| POST /predict |
| body: {"inputs": "Anskaffelse av samfunnsøkonomisk analyse..."} |
| or: {"inputs": ["text1", "text2", ...]} |
| |
| RESPONSE |
| list of dicts, one per input: |
| - {"label": "RELEVANT", "score": 0.83, "threshold": 0.26, "reason": "ok"} |
| - {"label": "NOT_RELEVANT", "score": 0.05, "threshold": 0.26, "reason": "ok"} |
| - {"label": "needs_review", "score": None, "reason": "non_norwegian(en)"} |
| """ |
|
|
| import json |
| from pathlib import Path |
| from typing import Any, Dict, List |
|
|
| import torch |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
|
|
| from inference_rules import needs_review |
|
|
|
|
| class EndpointHandler: |
| def __init__(self, path: str = ""): |
| self.tokenizer = AutoTokenizer.from_pretrained(path) |
| self.model = AutoModelForSequenceClassification.from_pretrained(path) |
| self.model.eval() |
| with open(Path(path) / "threshold.json") as f: |
| self.threshold = json.load(f)["threshold"] |
|
|
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
| inputs = data.get("inputs", "") |
| if isinstance(inputs, str): |
| inputs = [inputs] |
|
|
| results = [] |
| for text in inputs: |
| flag, reason = needs_review(text) |
| if flag: |
| results.append({ |
| "label": "needs_review", |
| "score": None, |
| "reason": reason, |
| }) |
| continue |
|
|
| enc = self.tokenizer( |
| str(text), |
| truncation=True, |
| padding="max_length", |
| max_length=256, |
| return_tensors="pt", |
| ) |
| with torch.no_grad(): |
| logits = self.model(**enc).logits |
| score = torch.softmax(logits, dim=1)[0, 1].item() |
| label = "RELEVANT" if score >= self.threshold else "NOT_RELEVANT" |
| results.append({ |
| "label": label, |
| "score": score, |
| "threshold": self.threshold, |
| "reason": "ok", |
| }) |
| return results |
|
|
