app.py

import numpy as np
import streamlit as st
import torch

import disvae
import transforms as trans

P_MODEL = "models/btcvae_celeba"

# Decode Funktion --------------------------------------------------
sorter = trans.LatentSorter(disvae.get_kl_dict(P_MODEL))
vae = disvae.load_model(P_MODEL)
scaler = trans.MinMaxScaler(_min=torch.tensor([1.3]),_max=torch.tensor([4.0]),min_norm=0.3,max_norm=0.6)
imaging = trans.SumField()

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

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

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

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)# * 255

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

# x = st.slider("Select a value")
# st.write(x, "squared is", x * x)