import streamlit as st
import json
from nn import train, predict, save_model, sigmoid

# INPUTS = [[0,0],[0,1],[1,0],[1,1]]
# OUTPUTS = [[0],[1],[1],[0]]
# EPOCHS = 1000000
# ALPHAS = 20

INPUTS = [[0,0],[0,1],[1,0],[1,1]]
OUTPUTS = [[0],[1],[1],[0]]

def runNN(epoch, alpha):
    # Train model
    modelo = train(epochs=epoch, alpha=alpha)
    print(modelo)
    # Save model to file
    # save_model(modelo, "modelo.json")
    st.download_button(
        label="Download model",
        data=json.dumps(modelo),
        file_name="modelo.json",
        mime="application/json",
    )

    for i in range(4):
        result = predict(INPUTS[i][0],INPUTS[i][1], activation=sigmoid)
        st.write("for input", INPUTS[i], "expected", OUTPUTS[i][0], "predicted", f"{result:4.4}", "which is", "correct" if round(result)==OUTPUTS[i][0] else "incorrect")
        


def sidebar():
    # Neural network controls
    st.sidebar.header('Neural Network Controls')
    st.sidebar.text('Number of epochs')
    epochs = st.sidebar.slider('Epochs', 1000, 1000000, 100000)
    st.sidebar.text('Learning rate')
    alphas = st.sidebar.slider('Alphas', 1, 100, 20)
    if st.sidebar.button('Run Neural Network'):
        runNN(epochs, alphas)


def app():
    st.title('Simple Neural Network App')
    st.write('This is the Neural Network image we are trying to implement!')
    st.image('nn.png', width=500)
    sidebar()

    st.markdown('''
    ### References
* https://www.codingame.com/playgrounds/59631/neural-network-xor-example-from-scratch-no-libs
    ''')

if __name__ == '__main__':
    app()