Create app.py

app.py

@@ -0,0 +1,109 @@
+import os
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from ydata_synthetic.synthesizers.regular import RegularSynthesizer
+from ydata_synthetic.synthesizers import ModelParameters, TrainParameters
+
+st.set_page_config(layout="wide",initial_sidebar_state="auto")
+os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'
+def run():
+    #global data_synn
+    st.sidebar.image('YData_logo.svg')
+    st.title('Generate synthetic data for a tabular classification dataset using [ydata-synthetic](https://github.com/ydataai/ydata-synthetic)')
+    st.markdown('This streamlit application can generate synthetic data for your dataset. Please read all the instructions in the sidebar before you start the process.')
+    data = st.file_uploader('Upload a preprocessed dataset in csv format')
+    st.sidebar.title('About')
+    st.sidebar.markdown('[ydata-synthetic](https://github.com/ydataai/ydata-synthetic) is an open-source library and is used to generate synthetic data mimicking the real world data.')
+    st.sidebar.header('What is synthetic data?')
+    st.sidebar.markdown('Synthetic data is artificially generated data that is not collected from real world events. It replicates the statistical components of real data without containing any identifiable information, ensuring individuals privacy.')
+    st.sidebar.header('Why Synthetic Data?')
+    st.sidebar.markdown('''Synthetic data can be used for many applications:
+- Privacy
+- Remove bias
+- Balance datasets
+- Augment datasets''')
+
+
+    st.sidebar.header('Steps to follow')
+    st.sidebar.markdown('''
+- Upload any preprocessed tabular classification dataset.
+- Choose the parameters in the adjacent window appropriately.
+- Since this is a demo, please choose less number of epochs for quick completion of training.
+- After choosing all parameters, Click the button under the parameters to start training.
+- After the training is complete, you will see a graph comparing both real data set and synthetic dataset. Categorical columns are used to compare.
+- You will also see a button to download your synthetic dataset. Click that button to download your dataset.''')
+
+    st.sidebar.markdown('''[![Repo](https://badgen.net/badge/icon/GitHub?icon=github&label)](https://github.com/ydataai/ydata-synthetic)''',unsafe_allow_html=True)
+
+    @st.cache
+    def train(df):
+        #models_dir = './cache'
+        gan_args = ModelParameters(batch_size=batch_size,
+                           lr=learning_rate*0.001,
+                           betas=(beta_1, beta_2),
+                           noise_dim=noise_dim,
+                           layers_dim=layer_dim)
+
+        train_args = TrainParameters(epochs=epochs,
+                             sample_interval=log_step)
+        synthesizer = RegularSynthesizer(modelname = model, model_parameters = gan_args, n_discriminator=3)
+        synthesizer.fit(data, train_args, num_cols, cat_cols)
+        synthesizer.save('data_synth.pkl')
+        synthesizer = model.load('data_synth.pkl')
+        data_syn = synthesizer.sample(samples)
+        return data_syn
+    @st.cache
+    def convert_df(df):
+        return df.to_csv().encode('utf-8')
+    if data is not None:
+        data = pd.read_csv(data)
+        data.dropna(inplace=True)
+        st.header('Choose the parameters!!')
+        col1, col2, col3,col4 = st.columns(4)
+        with col1:
+            model = st.selectbox('Choose the GAN model', ['cgan','wgangp'],key=1)
+            if model=='cgan':
+                model=cgan
+            else:
+                model = wgangp
+            num_cols = st.multiselect('Choose the numerical columns', data.columns,key=1)
+            cat_cols = st.multiselect('Choose categorical columns', [x for x in data.columns if x not in num_cols], key=2)
+
+        with col2:
+            noise_dim = st.number_input('Select noise dimension', 0,200,128,1)
+            layer_dim = st.number_input('Select the layer dimension', 0,200,128,1)
+            batch_size = st.number_input('Select batch size', 0,500, 500,1)
+
+        with col3:
+            log_step = st.number_input('Select sample interval', 0,200,100,1)
+            epochs = st.number_input('Select the number of epochs',0,50,2,1)
+            learning_rate = st.number_input('Select learning rate(x1e-3', 0.01, 0.1, 0.05, 0.01)
+
+        with col4:
+            beta_1 = st.slider('Select first beta co-efficient', 0.0, 1.0, 0.5)
+            beta_2 = st.slider('Select second beta co-efficient', 0.0, 1.0, 0.9)
+            samples = st.number_input('Select the number of synthetic samples to be generated', 0, 400000, step=1000)
+    if st.button('Click here to start the training process'):
+        if data is not None:
+            st.write('Model Training is in progress. It may take a few minutes. Please wait for a while.')
+            data_synn = train(data)
+            st.success('Synthetic dataset with the given number of samples is generated!!')
+            st.subheader('Real Data vs Synthetic Data')
+            f , axes =  plt.subplots(len(cat_cols),2, figsize=(20,25))
+            f.suptitle('Real data vs Synthetic data')
+            for i, j in enumerate(cat_cols):
+                sns.countplot(x=j, data=data, ax = axes[i,0])
+                sns.countplot(x=j, data=data_synn, ax = axes[i,1])
+            st.pyplot(f)
+            st.download_button(
+            label="Download data as CSV",
+            data=convert_df(data_synn),
+            file_name='data_syn.csv',
+            mime='text/csv')
+            st.balloons()
+        else:
+            st.write('Upload a dataset to train!!')
+if __name__== '__main__':
+    run()