Instructions to use shalev396/eu-stock-forecasting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use shalev396/eu-stock-forecasting with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://shalev396/eu-stock-forecasting") - Notebooks
- Google Colab
- Kaggle
File size: 1,045 Bytes
1042da0 | 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 | """Hugging Face Inference Endpoints entry point — deploy this repo as a CPU API.
Request body: {"inputs": {"cutoff": "1997-10-01", "horizon": 30, "index": "DAX"}}
(also accepted: {"inputs": "1997-10-01", "parameters": {"horizon": 30, "index": "DAX"}}
or {"inputs": ["1997-10-01", 30, "DAX"]}). Response: the `Predictor.predict` JSON dict.
"""
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 {}
if isinstance(inputs, dict):
return self.predictor.predict(**inputs, **parameters)
if isinstance(inputs, (list, tuple)):
return self.predictor.predict(*inputs, **parameters)
return self.predictor.predict(inputs, **parameters)
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