import tensorflow as tf
from huggingface_hub import snapshot_download, hf_hub_download
import joblib

# Method 1: Download complete forecaster objects (Recommended)
lstm_forecaster_path = hf_hub_download(repo_id="abdullah-daoud/fintech-neural-forecasters", filename="lstm_forecaster.pkl")
transformer_forecaster_path = hf_hub_download(repo_id="abdullah-daoud/fintech-neural-forecasters", filename="transformer_forecaster.pkl")

# Load complete forecasters
lstm_forecaster = joblib.load(lstm_forecaster_path)
transformer_forecaster = joblib.load(transformer_forecaster_path)

# Make predictions
lstm_predictions = lstm_forecaster.predict(steps=5)
transformer_predictions = transformer_forecaster.predict(steps=5)

# Method 2: Download individual model files
repo_path = snapshot_download(repo_id="abdullah-daoud/fintech-neural-forecasters")

# Load individual TensorFlow models
lstm_model = tf.keras.models.load_model(f"{repo_path}/lstm_model")
transformer_model = tf.keras.models.load_model(f"{repo_path}/transformer_model")

# Load scalers if available
try:
    lstm_scaler = joblib.load(f"{repo_path}/lstm_model/scaler.pkl")
    transformer_scaler = joblib.load(f"{repo_path}/transformer_model/scaler.pkl")
except FileNotFoundError:
    print("Scalers not found - models may handle scaling internally")

Requirements

tensorflow>=2.13.0
numpy>=1.24.3
pandas>=2.0.3
scikit-learn>=1.3.0

Citation

@software{fintech_datagen_2025,
  title={FinTech DataGen: Complete Financial Forecasting Application},
  author={FinTech DataGen Team},
  year={2025},
  url={https://github.com/your_username/fintech-datagen}
}