Update app.py

app.py

@@ -17,16 +17,57 @@
 # st.write(summarizer(t1))
 
 #from transformers import AutoTokenizer, AutoModel
-import streamlit as st
+# import streamlit as st
 
 #tokenizer = AutoTokenizer.from_pretrained("llmware/industry-bert-insurance-v0.1")
 
-#model = AutoModel.from_pretrained("llmware/industry-bert-insurance-v0.1")
-# Use a pipeline as a high-level helper
-from transformers import pipeline
+# #model = AutoModel.from_pretrained("llmware/industry-bert-insurance-v0.1")
+# # Use a pipeline as a high-level helper
+# from transformers import pipeline
 
-#pipe = pipeline("feature-extraction")
+# #pipe = pipeline("feature-extraction")
    
-t=st.text_input("Enter the Text")
-pipe = pipeline("summarization")
-st.write(pipe(t))
+# t=st.text_input("Enter the Text")
+# pipe = pipeline("summarization")
+# 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("-------------------------")
+
+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))
+
+        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 !!!")