import numpy as np
import streamlit as st

def user_input_features(test_len, points_granularity):
    data, nn = {}, {}
    st.sidebar.header('Dataset:')
    data['length'] = st.sidebar.slider('Training data length', 20, 50, 28)
    data['period'] = st.sidebar.slider('Period of the wave', 0.75, 2.0, 1.0)
    data['growth'] = st.sidebar.slider('Values growth', -0.25, 0.25, 0.0)
    data['amplitude'] = st.sidebar.slider('Amplitude', 0.25, 1.75, 1.0)
    data['amplitude_growth'] = st.sidebar.slider('Amplitude growth', -0.01, 0.1, 0.0)
    data['noise'] = st.sidebar.slider('Noise', 0.0, 1.0, 0.0)
    st.sidebar.header('Model setup')
    nn['use_lstm'] = st.sidebar.radio('Select the type of Recurrent Neuron to use', ['LSTM', 'GRU']) == 'LSTM'
    nn['r1_nodes'] = st.sidebar.slider('Number of nodes in the first RNN layer', 1, 30, 13)
    nn['r2_nodes'] = st.sidebar.slider('Number of nodes in the second RNN layer', 0, 30, 0)
    nn['fc1_nodes'] = st.sidebar.slider('Number of nodes in the fully connected RNN layer', 0, 40, 10)
    nn['steps'] = len(np.arange(0, test_len, points_granularity))

    return data, nn


def generate_wave(x_set, params):
    return np.sin(x_set / params['period']) * (1 + params['amplitude_growth'] * x_set) * params[
        'amplitude'] + x_set * params['growth'] + params['noise']*np.random.randn(len(x_set))

def local_css(file_name):
    with open(file_name) as f:
        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)