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import streamlit as st
import torch
from normflows import nflow
import numpy as np
import seaborn as sns
import pandas as pd
uploaded_file = st.file_uploader("Choose original dataset")
col1,col2,col3 = st.columns(3)
bw = col1.number_input('Scale',value=3.05)
wd = col2.number_input('Weight Decay',value=0.0002)
iters = col3.number_input('Iterations',value=400)
def compute(dim):
api = nflow(dim=dim,latent=16,dataset=uploaded_file)
api.compile(optim=torch.optim.ASGD,bw=bw,lr=0.0001,wd=wd)
my_bar = st.progress(0)
for idx in api.train(iters=iters):
my_bar.progress(idx[0]/iters)
my_bar.progress(100)
samples = np.delete(np.array(api.model.sample(torch.tensor(api.scaled).float()).detach()),np.argmin(np.array(api.model.sample(torch.tensor(api.scaled).float()).detach()),axis=0),0)
# samples = np.delete(samples,np.argmax(samples,axis=0),0)
# fig, ax = plt.subplots()
g = sns.jointplot(x=samples[:, 0], y=samples[:, 1], kind='kde',cmap=sns.color_palette("Blues", as_cmap=True),fill=True,label='Gaussian KDE',levels=1000)
w = sns.scatterplot(x=api.scaled[:,0],y=api.scaled[:,1],ax=g.ax_joint,c='orange',marker='+',s=100,label='Real')
st.pyplot(w.get_figure())
def random_normal_samples(n, dim=3):
return torch.zeros(n, dim).normal_(mean=0, std=1)
samples = np.array(api.model.sample(torch.tensor(random_normal_samples(1000,api.scaled.shape[-1])).float()).detach())
return api.scaler.inverse_transform(samples)
with st.form('login_form'):
st.write('Token for generation:')
token = st.text_input('Token')
submit = st.form_submit_button('Submit')
if token in st.secrets['tokens'] and submit:
if uploaded_file is not None:
dims = len(uploaded_file.getvalue().decode("utf-8").split('\n')[0].split(','))-1
samples=compute(dims)
st.download_button('Download generated CSV', pd.DataFrame(samples).to_csv(), 'text/csv')
elif not uploaded_file:
st.write('Upload your file')
else:
st.markdown('## :red[You dont have access]')
st.markdown('Buy tokens here: [@advprop](https://adprop.t.me)') |