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cycle-sync model is built using a Long Short-Term Memory (LSTM) architecture trained to predict menstrual cycle lengths and period durations based on a user’s past period history.time-series-forecastingMinMaxScaler used for feature and label scaling.1import keras
2from datetime import timedelta
3import numpy as np
4import pickle
5
6# Load the model from Hugging Face
7model = keras.saving.load_model("hf://VishSinh/cycle-sync")
8
9# Load the scalers (if needed)
10with open("feature_scaler.pkl", "rb") as f:
11 feature_scaler = pickle.load(f)
12
13with open("label_scaler.pkl", "rb") as f:
14 label_scaler = pickle.load(f)