Update app.py

app.py

@@ -32,42 +32,42 @@
 # st.write(pipe(t))
 
 
-import pandas as pd
-import numpy as np
-from ydata_synthetic.synthesizers.regular import RegularSynthesizer
-from ydata_synthetic.synthesizers import ModelParameters, TrainParameters
-import streamlit as st 
-from os import getcwd
-text_file=st.file_uploader("Upload the Data File")
-st.write("-------------------------")
+# import pandas as pd
+# import numpy as np
+# from ydata_synthetic.synthesizers.regular import RegularSynthesizer
+# from ydata_synthetic.synthesizers import ModelParameters, TrainParameters
+# import streamlit as st 
+# from os import getcwd
+# text_file=st.file_uploader("Upload the Data File")
+# st.write("-------------------------")
 
-if text_file is not None:
-    df=pd.read_csv(text_file)
-    dd_list=df.columns
-    cat_cols=st.multiselect("Select the Categorical Columns",dd_list)
-    num_cols=st.multiselect("Select the Numerical Columns",dd_list)
-    Output_file=st.text_input('Enter Output File Name')
-    s=st.number_input('Enter the Sample Size',1000)
-    OP=Output_file + '.csv'
-    sub=st.button('Submit')
-    if sub:
-        batch_size = 50
-        epochs = 3
-        learning_rate = 2e-4
-        beta_1 = 0.5
-        beta_2 = 0.9
+# if text_file is not None:
+#     df=pd.read_csv(text_file)
+#     dd_list=df.columns
+#     cat_cols=st.multiselect("Select the Categorical Columns",dd_list)
+#     num_cols=st.multiselect("Select the Numerical Columns",dd_list)
+#     Output_file=st.text_input('Enter Output File Name')
+#     s=st.number_input('Enter the Sample Size',1000)
+#     OP=Output_file + '.csv'
+#     sub=st.button('Submit')
+#     if sub:
+#         batch_size = 50
+#         epochs = 3
+#         learning_rate = 2e-4
+#         beta_1 = 0.5
+#         beta_2 = 0.9
 
-        ctgan_args = ModelParameters(batch_size=batch_size,
-                                    lr=learning_rate,
-                                    betas=(beta_1, beta_2))
+#         ctgan_args = ModelParameters(batch_size=batch_size,
+#                                     lr=learning_rate,
+#                                     betas=(beta_1, beta_2))
 
-        train_args = TrainParameters(epochs=epochs)
-        synth = RegularSynthesizer(modelname='ctgan', model_parameters=ctgan_args)
-        synth.fit(data=df, train_arguments=train_args, num_cols=num_cols, cat_cols=cat_cols)
-        df_syn = synth.sample(s)
-        df_syn.to_csv(OP)
-        c=getcwd()    
-        c=c + '/' + OP
-        with open(c,"rb") as file:
-            st.download_button(label=':blue[Download]',data=file,file_name=OP,mime="image/png") 
-        st.success("Thanks for using the app !!!")
+#         train_args = TrainParameters(epochs=epochs)
+#         synth = RegularSynthesizer(modelname='ctgan', model_parameters=ctgan_args)
+#         synth.fit(data=df, train_arguments=train_args, num_cols=num_cols, cat_cols=cat_cols)
+#         df_syn = synth.sample(s)
+#         df_syn.to_csv(OP)
+#         c=getcwd()    
+#         c=c + '/' + OP
+#         with open(c,"rb") as file:
+#             st.download_button(label=':blue[Download]',data=file,file_name=OP,mime="image/png") 
+#         st.success("Thanks for using the app !!!")