Tabular Classification
Scikit-learn
Joblib
ml-lab
scikit-learn
predictive-maintenance
time-series-classification
iot
synthetic-data
Eval Results (legacy)
Instructions to use shalev396/elevator-maintenance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use shalev396/elevator-maintenance with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("shalev396/elevator-maintenance", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
File size: 780 Bytes
897d513 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | """Hugging Face Inference Endpoints entry point — deploy this repo as a CPU/GPU API.
Request body: {"inputs": <CSV text | list of row dicts | {column: [values]}>} with >= 60 minutely rows of
the 11 sensor columns (optional `timestamp` column). Response: {"failure": p, "healthy": 1 - p}.
"""
import sys
from pathlib import Path
HERE = Path(__file__).resolve().parent
sys.path.insert(0, str(HERE))
import model as M # noqa: E402
class EndpointHandler:
def __init__(self, path: str = ""):
self.predictor = M.load(path or HERE, "cuda" if M.cuda_available() else "cpu")
def __call__(self, data: dict):
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None) or {}
return self.predictor.predict(inputs, **parameters)
|