app.py

import streamlit as st 
import numpy as np 
import pandas as pd  
import plotly_express as px  
from sklearn import *
from lazypredict.Supervised import LazyRegressor
from lazypredict.Supervised import LazyClassifier
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import seaborn as sns
import base64
import io
import webbrowser
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_diabetes
from PIL import Image


image = Image.open('ds.jpg')
st.image(image,use_column_width=True)

def main():
    activities = ['EDA','Visualization','Regression','Classification','Documentation','About Us']
    #st.sidebar.success('Updates Coming Soon! 🌟🎉')
    option=st.sidebar.selectbox('Choose a section',activities)
    st.sidebar.markdown('''Use this section for finding useful insights about your data,and feel free to use them in your notebooks
                                             
    🎯   Version : 1.0.2  ''')
    
    
    if option == 'EDA':
        st.subheader("Explanatory Data Analysis")
        
        data=st.file_uploader("Please upload a CSV dataset ",type=['csv'])
        
        
        
        st.warning('Your dataset goes here...')
        if data is not None:
            df=pd.read_csv(data)
            st.dataframe(df)
            st.info('Some useful data insights about your data')
            if st.checkbox("Display shape"):
                r,c = df.shape
                st.write('Rows = ',r,'Columns = ',c)
                
            if st.checkbox('Display columns'):
                st.write(df.columns)
                
            if st.checkbox('Select multiple columns'):
                selected_col = st.multiselect('Select preferred columns',df.columns)
                df1 = df[selected_col]        
                st.dataframe(df1)
                     
            if st.checkbox("Head"):
                st.write(df.head())
            
            if st.checkbox('Tail'):
                st.write(df.tail())
                
            if st.checkbox('Null values'):
                st.write(df.isnull().sum())
                
            if st.checkbox('Data types'):
                st.write(df.dtypes)

            if st.checkbox('Random sample'):
                st.write(df.sample(20))
                
            if st.checkbox('Display correlations'):
                st.write(df.corr())

            if st.checkbox('Summary'):
                st.write(df.describe(include='all').T)
                
            
            
                      
    elif option == 'Visualization':
        st.subheader("Data Visualization and Graphing")
        
        st.sidebar.subheader("File Upload")

        # Setup file upload
        uploaded_file = st.sidebar.file_uploader(
                                label="Upload your CSV file. (200MB max)",
                                type=['csv'])

       
        if uploaded_file is not None:
            st.success('Your data goes here')
    

        try:
            df = pd.read_csv(uploaded_file)
        except Exception as e:
            st.warning('Data not found')
            

        
        global numeric_columns
        global non_numeric_columns
        try:
            st.write(df)
            numeric_columns = list(df.select_dtypes(['float', 'int']).columns)
            non_numeric_columns = list(df.select_dtypes(['object']).columns)
            non_numeric_columns.append(None)
            print(non_numeric_columns)
        except Exception as e:
            print(e)
            

        
        chart_select = st.sidebar.selectbox(
            label="Select the chart type",
            options=['Scatterplots', 'Lineplots', 'Histogram', 'Boxplot','Violinplot','Piechart']
        )
        
        st.info('The Graphs generated will be displayed here')

        if chart_select == 'Scatterplots':
            st.sidebar.subheader("Scatterplot Settings")
            try:
                x_values = st.sidebar.selectbox('X axis', options=numeric_columns)
                y_values = st.sidebar.selectbox('Y axis', options=numeric_columns)
                color_value = st.sidebar.selectbox("Color", options=non_numeric_columns)
                plot = px.scatter(data_frame=df, x=x_values, y=y_values, color=color_value)
                # display the chart
                st.plotly_chart(plot)
            except Exception as e:
                print(e)

        if chart_select == 'Lineplots':
            st.sidebar.subheader("Line Plot Settings")
            try:
                x_values = st.sidebar.selectbox('X axis', options=numeric_columns)
                y_values = st.sidebar.selectbox('Y axis', options=numeric_columns)
                color_value = st.sidebar.selectbox("Color", options=non_numeric_columns)
                plot = px.line(data_frame=df, x=x_values, y=y_values, color=color_value)
                st.plotly_chart(plot)
            except Exception as e:
                print(e)

        if chart_select == 'Histogram':
            st.sidebar.subheader("Histogram Settings")
            try:
                x = st.sidebar.selectbox('Feature', options=numeric_columns)
                bin_size = st.sidebar.slider("Number of Bins", min_value=10,
                                            max_value=100, value=40)
                color_value = st.sidebar.selectbox("Color", options=non_numeric_columns)
                plot = px.histogram(x=x, data_frame=df, color=color_value)
                st.plotly_chart(plot)
            except Exception as e:
                print(e)

        if chart_select == 'Boxplot':
            st.sidebar.subheader("Boxplot Settings")
            try:
                y = st.sidebar.selectbox("Y axis", options=numeric_columns)
                x = st.sidebar.selectbox("X axis", options=non_numeric_columns)
                color_value = st.sidebar.selectbox("Color", options=non_numeric_columns)
                plot = px.box(data_frame=df, y=y, x=x, color=color_value)
                st.plotly_chart(plot)
            except Exception as e:
                print(e)

        if chart_select == 'Piechart':
            st.sidebar.subheader("Piechart Settings")
            try:
                x_values = st.sidebar.selectbox('X axis', options=numeric_columns)
                y_values = st.sidebar.selectbox('Y axis', options=non_numeric_columns)
                plot = px.pie(data_frame=df, values=x_values, names=y_values)
                st.plotly_chart(plot)
                
            except Exception as e:
                print(e)
        
        if chart_select == 'Violinplot':
            st.sidebar.subheader("Violin Plot Settings")
            try:
                x_values = st.sidebar.selectbox('X axis', options=numeric_columns)
                y_values = st.sidebar.selectbox('Y axis', options=numeric_columns)
                color_value = st.sidebar.selectbox("Color", options=non_numeric_columns)
                plot = px.violin(data_frame=df, x=x_values, y=y_values, color=color_value)
                st.plotly_chart(plot)
            except Exception as e:
                print(e)
    
    elif option == 'Regression':
        st.subheader("Regression ML Model Builder")
  
        # Model building
        def build_model(df):
            l = len(df)
                
            #df = df.iloc[:100]
            X = df.iloc[:,:-1] # Using all column except for the last column as X
            Y = df.iloc[:,-1] # Selecting the last column as Y
        

            st.markdown('**1.2. Dataset dimension**')
            st.write('X (Independent Axis)')
            st.info(X.shape)
            st.write('Y (Dependent Axis)')
            st.info(Y.shape)

            st.markdown('**1.3. Variable details**:')
            st.write('X variable (first few are shown)')
            st.info(list(X.columns[:int(l/5)]))
            st.write('Y variable')
            st.info(Y.name)

            # Build lazy model
            X_train, X_test, Y_train, Y_test = train_test_split(X, Y,test_size = split_size,random_state = seed_number)
            reg = LazyRegressor(verbose=0,ignore_warnings=False, custom_metric=None)
            models_train,predictions_train = reg.fit(X_train, X_train, Y_train, Y_train)
            models_test,predictions_test = reg.fit(X_train, X_test, Y_train, Y_test)

            st.subheader('2.Model Performance Plot (Training Set)')

            st.write('Training set')
            st.write(predictions_train)
            st.markdown(filedownload(predictions_train,'training.csv'), unsafe_allow_html=True)

            st.write('Test set')
            st.write(predictions_test)
            st.markdown(filedownload(predictions_test,'test.csv'), unsafe_allow_html=True)

            st.subheader('3.Model Performance Plot(Test set)')


            with st.markdown('**R-squared**'):
                # Tall
                predictions_test["R-Squared"] = [0 if i < 0 else i for i in predictions_test["R-Squared"] ]
                plt.figure(figsize=(3, 9))
                sns.set_theme(style="darkgrid")
                ax1 = sns.barplot(y=predictions_test.index, x="R-Squared", data=predictions_test)
                ax1.set(xlim=(0, 1))
            st.markdown(imagedownload(plt,'plot-r2-tall.pdf'), unsafe_allow_html=True)
                # Wide
            plt.figure(figsize=(12, 3))
            sns.set_theme(style="darkgrid")
            ax1 = sns.barplot(x=predictions_test.index, y="R-Squared", data=predictions_test)
            ax1.set(ylim=(0, 1))
            plt.xticks(rotation=90)
            st.pyplot(plt)
            st.markdown(imagedownload(plt,'plot-r2-wide.pdf'), unsafe_allow_html=True)

            with st.markdown('**RMSE (capped at l/2)**'):
                # Tall
                predictions_test["RMSE"] = [(l/2) if i > (l/2) else i for i in predictions_test["RMSE"] ]
                plt.figure(figsize=(3, 9))
                sns.set_theme(style="darkgrid")
                ax2 = sns.barplot(y=predictions_test.index, x="RMSE", data=predictions_test)
            st.markdown(imagedownload(plt,'plot-rmse-tall.pdf'), unsafe_allow_html=True)
                # Wide
            plt.figure(figsize=(12, 3))
            sns.set_theme(style="darkgrid")
            ax2 = sns.barplot(x=predictions_test.index, y="RMSE", data=predictions_test)
            plt.xticks(rotation=90)
            st.pyplot(plt)
            st.markdown(imagedownload(plt,'plot-rmse-wide.pdf'), unsafe_allow_html=True)

            with st.markdown('**Calculation time**'):
                # Tall
                predictions_test["Time Taken"] = [0 if i < 0 else i for i in predictions_test["Time Taken"] ]
                plt.figure(figsize=(3, 9))
                sns.set_theme(style="darkgrid")
                ax3 = sns.barplot(y=predictions_test.index, x="Time Taken", data=predictions_test)
            st.markdown(imagedownload(plt,'plot-calculation-time-tall.pdf'), unsafe_allow_html=True)
                # Wide
            plt.figure(figsize=(9, 3))
            sns.set_theme(style="darkgrid")
            ax3 = sns.barplot(x=predictions_test.index, y="Time Taken", data=predictions_test)
            plt.xticks(rotation=90)
            st.pyplot(plt)
            st.markdown(imagedownload(plt,'plot-calculation-time-wide.pdf'), unsafe_allow_html=True)


        def filedownload(df, filename):
            csv = df.to_csv(index=False)
            b64 = base64.b64encode(csv.encode()).decode()  # strings <-> bytes conversions
            href = f'<a href="data:file/csv;base64,{b64}" download={filename}>Download {filename} File</a>'
            return href

        def imagedownload(plt, filename):
            s = io.BytesIO()
            plt.savefig(s, format='pdf', bbox_inches='tight')
            plt.close()
            b64 = base64.b64encode(s.getvalue()).decode()  # strings <-> bytes conversions
            href = f'<a href="data:image/png;base64,{b64}" download={filename}>Download {filename} File</a>'
            return href



        with st.sidebar.header('File Uploader Section'):
            uploaded_file = st.sidebar.file_uploader("Upload an input as CSV file", type=["csv"])
            


        with st.sidebar.header('Set the optimization parameters\n (Grab the slider and set to any suitable point)'):
            
            split_size = st.sidebar.slider('Data split ratio (in fraction):', 0.0, 1.0, 0.7, 0.01)
            seed_number = st.sidebar.slider('Set the random-seed-value :', 0, 1, 100, 5)
            
        with st.sidebar.header('Project made by:'):
            st.write("Made by: MAINAK CHAUDHURI")
                
        #---------------------------------#

        st.subheader('Dataset display')
        
        

        if uploaded_file is not None:
            df = pd.read_csv(uploaded_file)
            st.markdown('**Snap of the dataset**')
            st.write(df)
            build_model(df)
        else:
            st.info('Upload a file')
            st.info('OR')
            if st.button('Use preloaded data instead'):
                st.info("Dataset used : Pima diabetes")

            
                diabetes = load_diabetes()
            
                X = pd.DataFrame(diabetes.data, columns=diabetes.feature_names).loc[:100] 
                Y = pd.Series(diabetes.target, name='response').loc[:100] 
                df = pd.concat( [X,Y], axis=1 )

                st.markdown('Displaying results form a sample preloaded data :')
                st.write(df.head(5))

                build_model(df)
                
    elif option == 'Classification':
        st.subheader("Classifier ML Model Builder")
        
        def build_model(df):
            l = len(df)
                
            #df = df.iloc[:100]
            X = df.iloc[:,:-1] # Using all column except for the last column as X
            Y = df.iloc[:,-1] # Selecting the last column as Y
        

            st.markdown('**1.2. Dataset dimension**')
            st.write('X (Independent Axis)')
            st.info(X.shape)
            st.write('Y (Dependent Axis)')
            st.info(Y.shape)

            st.markdown('**1.3. Variable details**:')
            st.write('X variable (first few are shown)')
            st.info(list(X.columns[:int(l/5)]))
            st.write('Y variable')
            st.info(Y.name)

            # Build lazy model
            X_train, X_test, Y_train, Y_test = train_test_split(X, Y,test_size = split_size,random_state = seed_number)
            clf = LazyClassifier(verbose=0,ignore_warnings=False, custom_metric=None)
            models_train,predictions_train = clf.fit(X_train, X_train, Y_train, Y_train)
            models_test,predictions_test = clf.fit(X_train, X_test, Y_train, Y_test)

            st.subheader('2.Model Performance Plot (Training Set)')

            st.write('Training set')
            st.write(predictions_train)
            st.markdown(filedownload(predictions_train,'training.csv'), unsafe_allow_html=True)

            st.write('Test set')
            st.write(predictions_test)
            st.markdown(filedownload(predictions_test,'test.csv'), unsafe_allow_html=True)

            st.subheader('3.Model Performance Plot(Test set)')


            with st.markdown('**Accuracy**'):
                # Tall
                predictions_test["Accuracy"] = [0 if i < 0 else i for i in predictions_test["Accuracy"] ]
                plt.figure(figsize=(5, 12))
                sns.set_theme(style="darkgrid")
                ax1 = sns.barplot(y=predictions_test.index, x="Accuracy", data=predictions_test)
                ax1.set(xlim=(0, 1))
            st.markdown(imagedownload(plt,'plot-r2-tall.pdf'), unsafe_allow_html=True)
                # Wide
            plt.figure(figsize=(12, 5))
            sns.set_theme(style="darkgrid")
            ax1 = sns.barplot(x=predictions_test.index, y="Accuracy", data=predictions_test)
            ax1.set(ylim=(0, 1))
            plt.xticks(rotation=90)
            st.pyplot(plt)
            st.markdown(imagedownload(plt,'plot-r2-wide.pdf'), unsafe_allow_html=True)

            

        def filedownload(df, filename):
            csv = df.to_csv(index=False)
            b64 = base64.b64encode(csv.encode()).decode()  # strings <-> bytes conversions
            href = f'<a href="data:file/csv;base64,{b64}" download={filename}>Download {filename} File</a>'
            return href

        def imagedownload(plt, filename):
            s = io.BytesIO()
            plt.savefig(s, format='pdf', bbox_inches='tight')
            plt.close()
            b64 = base64.b64encode(s.getvalue()).decode()  # strings <-> bytes conversions
            href = f'<a href="data:image/png;base64,{b64}" download={filename}>Download {filename} File</a>'
            return href



        with st.sidebar.header('File Uploader Section'):
            uploaded_file = st.sidebar.file_uploader("Upload an input as CSV file", type=["csv"])
            


        with st.sidebar.header('Set the optimization parameters\n (Grab the slider and set to any suitable point)'):
            
            split_size = st.sidebar.slider('Data split ratio (in fraction):', 0.0, 1.0, 0.7, 0.01)
            seed_number = st.sidebar.slider('Set the random-seed-value :', 0, 1, 100, 5)
            
        with st.sidebar.header('Project made by:'):
            st.write("Made by: MAINAK CHAUDHURI")
                
        #---------------------------------#

        st.subheader('Dataset display')
        
        

        if uploaded_file is not None:
            df = pd.read_csv(uploaded_file)
            st.markdown('**Snap of the dataset**')
            st.write(df)
            build_model(df)
        else:
            st.info('Upload a file')
            st.info('OR')
            if st.button('Use preloaded data instead'):
                st.info("Dataset used : Pima diabetes")

            
                diabetes = load_diabetes()
            
                X = pd.DataFrame(diabetes.data, columns=diabetes.feature_names).loc[:100] 
                Y = pd.Series(diabetes.target, name='response').loc[:100] 
                df = pd.concat( [X,Y], axis=1 )

                st.markdown('Displaying results form a sample preloaded data :')
                st.write(df.head(5))

                build_model(df)
        
    elif option == 'Documentation':
        st.subheader("How to use Notebooker Pro")
        st.markdown('''The notebooker pro is a user-friendly software designed to help you make a good data science notebook in few steps.
Well, notebooker pro will not be making a notebook for you, but will provide you with all the data insights that you 
will need to put in your kernel. The notebooker pro has been provided with 4 major sections:

i.  **EDA (Explanatory Data Analysis)**  --> used to find important data and statistical insights from the uploaded files

ii. **Visualization** --> Used to perform data visualization with 5 basic important types of graphs

iii.**Regression** --> Loops through **30** different regression models and returns the complexity statistics of the result
		   of regression modelling for your dataset for chosen seed values and size. The only thing to keep in
		   mind while using this is that, the data must be fitting with a regression modelling. Datasets used
		   for classification algorithm might generate vague results. So use a proper dataset.
		   **[eg.: do not use iris,cancer,penguins etc. classifier dataset]**

iv. **Classification** --> Loops through **30** different classification models and returns the complexity statistics of the result
		   of classification modelling for your dataset for chosen seed values and size. The only thing to keep in
		   mind while using this is that, the data must be fitting with a classification modelling. Datasets used
		   for non-classification algorithm might generate vague results. So use a proper dataset.
		   

**Features:**

**Upload file** => Upload only csv files.

**Data split**  => This is a linear slidebar, that will let you choose split ratio between 0 to 1

**Random seed** => Helps to randomize the data in training and testing data samples. 
	       You may change to get the best accuracy of for a particular model.
	       ''')
        
    elif option == 'About Us':
        st.subheader("About Us 😊")
        st.markdown('''This web application is made by Mainak Chaudhuri. He is a Computer Science and Engineering student of the SRM University, studying in the second year of B.Tech. The main idea of this application is to help beginners and data science enthusiasts chalk out a plan for preparing a good data science notebook, for college projects, online courses or to add in their portfolio. This application accepts a dataset from the user and displays useful insights about the data. Additionally, it also helps the user visualize the data, choose the best supervised machine learning model (regression & classifaction handled separately) and decide the best suit depending on the dataset size,split and seed values which can be set by the user with the help of the side panel. This application claims to be the first of it's kind ever developed till date by a single developer and also has a serving history and positive reports from 180+ users.
                    
                    
     👉   N.B. : This application is an intellectual property of Mainak Chaudhuri and hence holds a reserved copyright. Any form of illegal immitation of graphics, contents or documentation without prior permission of the owner if proved, can result in legal actions against the plagiarist.''')
        
        st.success('For more info, feel free to contact @ : ')
        url = 'https://www.linkedin.com/in/mainak-chaudhuri-127898176/'

        if st.button('Mainak Chaudhuri'):
            webbrowser.open_new_tab(url)
        
if __name__ == '__main__':
    main()