File size: 2,316 Bytes
2409f79 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | """Hugging Face Inference Endpoints handler: text in, ranked IPC symbols out.
Copied to the root of the model repository by ``t2ipc hf-export``. The repository
also carries ``scheme/`` and ``index/`` (parquet), ``text2ipc.json`` (which index to
serve) and a copy of the ``text2ipc`` package, so no PyPI release is required.
Request::
{"inputs": "enxada manual com duas lâminas",
"parameters": {"level": "group", "top_k": 5}}
Response (one list per input; a single string yields a single list)::
[{"symbol": "A01B 1/10", "canonical": "A01B0001100000", "level": "subgroup",
"score": 0.83, "similarity": 0.81, "title": "...", "path": "..."}, ...]
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
from typing import Any
ALLOWED_PARAMS = {"level", "top_k", "gap", "auto_margin", "normalize"}
class EndpointHandler:
def __init__(self, path: str = ""):
root = Path(path or ".").resolve()
if str(root) not in sys.path:
sys.path.insert(0, str(root)) # vendored text2ipc package
from text2ipc.classifier import IpcClassifier
cfg = json.loads((root / "text2ipc.json").read_text())
self.clf = IpcClassifier(
cfg["version"],
lang=cfg["lang"],
model=cfg.get("embedder") or cfg["model"],
root=root,
)
_ = self.clf.index # load eagerly so the first request is fast
_ = self.clf.embedder
def __call__(self, data: dict[str, Any]) -> list[dict] | list[list[dict]]:
inputs = data.get("inputs", "")
params = {k: v for k, v in (data.get("parameters") or {}).items() if k in ALLOWED_PARAMS}
if isinstance(inputs, str):
return self._one(inputs, params)
return [self._one(text, params) for text in inputs]
def _one(self, text: str, params: dict[str, Any]) -> list[dict]:
return [
{
"symbol": m.pretty,
"canonical": m.symbol,
"level": m.level,
"depth": m.depth,
"score": round(m.score, 4),
"similarity": round(m.similarity, 4),
"title": m.title,
"path": m.text,
}
for m in self.clf.classify(text, **params)
]
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