"""This modules contains data about visualisation page"""

# Import necessary modules

import warnings

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.metrics import plot_confusion_matrix

from sklearn import tree

import streamlit as st

# Import necessary functions from web_functions

from web_functions import train_model

def app(df, X, y):

    """This function create the visualisation page"""

    # Remove the warnings

    warnings.filterwarnings('ignore')

    st.set_option('deprecation.showPyplotGlobalUse', False)

    # Set the page title

    st.title("Visualise the Parkinson's Prediction")

    # Create a checkbox to show correlation heatmap

    if st.checkbox("Show the correlation heatmap"):

        st.subheader("Correlation Heatmap")

        fig = plt.figure(figsize = (10, 6))

        ax = sns.heatmap(df.iloc[:, 1:].corr(), annot = True)   # Creating an object of seaborn axis and storing it in 'ax' variable

        bottom, top = ax.get_ylim()                             # Getting the top and bottom margin limits.

        ax.set_ylim(bottom + 0.5, top - 0.5)                    # Increasing the bottom and decreasing the top margins respectively.

        st.pyplot(fig)

    if st.checkbox("Show Scatter Plot"):

        figure, axis = plt.subplots(2, 2,figsize=(15,10))

        sns.scatterplot(ax=axis[0,0],data=df,x='AVFF',y='MAVFF',hue='status')

        axis[0, 0].set_title("Oversampling Minority Scatter")

        sns.countplot(ax=axis[0, 1],x="status", data=df)

        axis[0, 1].set_title("Oversampling Minority Count")

        sns.scatterplot(ax=axis[1, 0],data=df,x='AVFF',y='MAVFF',hue='status')

        axis[1, 0].set_title("Undersampling Majority Scatter")

        sns.countplot(ax=axis[1, 1],x="status", data=df)

        axis[1, 1].set_title("Undersampling Majority Count")

        st.pyplot()

    if st.checkbox("Display Boxplot"):

        fig, ax = plt.subplots(figsize=(15,5))

        df.boxplot(['AVFF', 'MAVFF', 'MIVFF','HNR'],ax=ax)

        st.pyplot()

    if st.checkbox("Show Sample Results"):

        safe = (df['status'] == 0).sum()

        prone = (df['status'] == 1).sum()

        data = [safe,prone]

        labels = ['Safe', 'Prone']

        colors = sns.color_palette('pastel')[0:7]

        plt.pie(data, labels = labels, colors = colors, autopct='%.0f%%')

        st.pyplot()

    if st.checkbox("Plot confusion matrix"):

        model, score = train_model(X, y)

        plt.figure(figsize = (10, 6))

        plot_confusion_matrix(model, X, y, values_format='d')

        st.pyplot()

    if st.checkbox("Plot Decision Tree"):

        model, score = train_model(X, y)

        # Export decision tree in dot format and store in 'dot_data' variable.

        dot_data = tree.export_graphviz(

            decision_tree=model, max_depth=3, out_file=None, filled=True, rounded=True,

            feature_names=X.columns, class_names=['0', '1']

        )

        # Plot the decision tree using the 'graphviz_chart' function of the 'streamlit' module.

        st.graphviz_chart(dot_data)