added files

app.py

@@ -0,0 +1,38 @@
+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")
+bw = st.number_input('Scale',value=3.05)
+
+
+
+def compute():
+    api = nflow(dim=8,latent=16,dataset=uploaded_file)
+    api.compile(optim=torch.optim.ASGD,bw=bw,lr=0.0001,wd=None)
+    api.train(iters=10000)
+    samples = np.array(api.model.sample(
+            torch.tensor(api.scaled).float()).detach())
+
+        # 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=50)
+
+    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=2):
+        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)
+    
+   
+    
+if uploaded_file is not None:
+    samples=compute()
+    st.download_button('Download generated CSV', pd.DataFrame(samples).to_csv(), 'text/csv')