import numpy as np
import streamlit as st
import torch

import disvae
import transforms as trans

@st.cache_resource
def load_decode_function():
    P_MODEL = "models/btcvae_celeba"

    sorter = trans.LatentSorter(disvae.get_kl_dict(P_MODEL))
    vae = disvae.load_model(P_MODEL)

    _dec = trans.sequential_function(
        sorter.inv,
        vae.decoder
    )

    def decode(latent):
        with torch.no_grad():
            return trans.np_sample(_dec)(latent)
    return decode

# GUI -----------------------------------------------------------

decode = load_decode_function()

latent_vector = np.array([st.slider(f"L{l}",min_value=-3.0,max_value=3.0,value=0.0) for l in range(3)])
latent_vector = np.concatenate([latent_vector,np.zeros(7)],axis=0)

value = decode(latent_vector)

value = np.swapaxes(np.swapaxes(value, 0, 2), 0, 1)

st.image(value, use_column_width="always")