app.py

import streamlit as st
import pandas as pd
import numpy as np
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import plot_confusion_matrix, plot_roc_curve, plot_precision_recall_curve
from sklearn.metrics import precision_score, recall_score


def main():
    st.title("Binary Classification Web App")
    st.sidebar.title("Binary Classification Web App")
    st.markdown("Are your mushrooms edible or poisonous?")
    st.sidebar.markdown("Are your mushrooms edible or poisonous?")

    # Streamlit decorator that caches the output and use it to rerun
    @st.cache(persist=True)
    def load_data():
        data = pd.read_csv(
            r"C:\Users\Utkarsh.DESKTOP-6FKGAET\Utkarsh\Projects\Classification-Algorithms-Visualiser\Mushrooms.csv")
        label = LabelEncoder()
        for col in data.columns:
            data[col] = label.fit_transform(data[col])
        return data

    @st.cache(persist=True)
    def split(df):
        y = df.type
        x = df.drop(columns=['type'])
        x_train, x_test, y_train, y_test = train_test_split(
            x, y, test_size=0.3, random_state=0)
        return x_train, x_test, y_train, y_test

    def plot_metrics(metrics_list):
        if 'Confusion Matrix' in metrics_list:
            st.subheader("Confusion Matrix")
            plot_confusion_matrix(model, x_test, y_test,
                                  display_labels=class_names)
            st.pyplot()

        if 'ROC Curve' in metrics_list:
            st.subheader("ROC Curve")
            plot_roc_curve(model, x_test, y_test)
            st.pyplot()

        if 'Precision-Recall Curve' in metrics_list:
            st.subheader('Precision-Recall Curve')
            plot_precision_recall_curve(model, x_test, y_test)
            st.pyplot()

    df = load_data()
    x_train, x_test, y_train, y_test = split(df)
    class_names = ['edible', 'poisonous']
    st.sidebar.subheader("Choose Classifier")
    classifier = st.sidebar.selectbox(
        "Classifier", ("Support Vector Machine (SVM)", "Logistic Regression", "Random Forest"))

    if classifier == 'Support Vector Machine (SVM)':
        st.sidebar.subheader("Model Hyperparameteres")
        C = st.sidebar.number_input(
            "C (Regularization parameter)", 0.01, 10.0, step=0.01, key='C')
        kernel = st.sidebar.radio("Kernel", ("rbf", "linear"), key='kernel')
        gamma = st.sidebar.radio(
            "Gamma (Kernel Coefficient)", ("scale", "auto"), key='gamma')

        metrics = st.sidebar.multiselect(
            "Metrics to plot", ('Confusion Matrix', 'ROC Curve', 'Precision-Recall Curve'))

        if st.sidebar.button("Classify", key='classify'):
            st.subheader("Support Vector Machine (SVM) Results")
            model = SVC(C=C, kernel=kernel, gamma=gamma)
            model.fit(x_train, y_train)
            accuracy = model.score(x_test, y_test)
            y_pred = model.predict(x_test)
            st.write("Accuracy: ", accuracy.round(2))
            st.write("Precision: ", precision_score(
                y_test, y_pred, labels=class_names).round(2))
            st.write("Recall: ", recall_score(
                y_test, y_pred, labels=class_names).round(2))
            plot_metrics(metrics)

    if classifier == 'Logistic Regression':
        st.sidebar.subheader("Model Hyperparameteres")
        C = st.sidebar.number_input(
            "C (Regularization parameter)", 0.01, 10.0, step=0.01, key='C')
        max_iter = st.sidebar.slider(
            "Maximum number of iterations", 100, 500, key='max_iter')

        metrics = st.sidebar.multiselect(
            "Metrics to plot", ('Confusion Matrix', 'ROC Curve', 'Precision-Recall Curve'))

        if st.sidebar.button("Classify", key='classify'):
            st.subheader("Logistic Regression Results")
            model = LogisticRegression(C=C, max_iter=max_iter)
            model.fit(x_train, y_train)
            accuracy = model.score(x_test, y_test)
            y_pred = model.predict(x_test)
            st.write("Accuracy: ", accuracy.round(2))
            st.write("Precision: ", precision_score(
                y_test, y_pred, labels=class_names).round(2))
            st.write("Recall: ", recall_score(
                y_test, y_pred, labels=class_names).round(2))
            plot_metrics(metrics)

    if classifier == 'Random Forest':
        st.sidebar.subheader("Model Hyperparameteres")
        n_estimators = st.sidebar.number_input(
            "Number of trees in forest", 100, 5000, step=10, key='n_estimators')
        max_depth = st.sidebar.number_input(
            "Maximum depth of the tree", 1, 20, step=1, key='max_depth')
        bootstrap = st.sidebar.radio(
            "Bootstrap samples when building the trees", ('True', 'False'), key='bootstrap')

        metrics = st.sidebar.multiselect(
            "Metrics to plot", ('Confusion Matrix', 'ROC Curve', 'Precision-Recall Curve'))

        if st.sidebar.button("Classify", key='classify'):
            st.subheader("Random Forest Results")
            model = RandomForestClassifier(
                n_estimators=n_estimators, max_depth=max_depth, bootstrap=bootstrap)
            model.fit(x_train, y_train)
            accuracy = model.score(x_test, y_test)
            y_pred = model.predict(x_test)
            st.write("Accuracy: ", accuracy.round(2))
            st.write("Precision: ", precision_score(
                y_test, y_pred, labels=class_names).round(2))
            st.write("Recall: ", recall_score(
                y_test, y_pred, labels=class_names).round(2))
            plot_metrics(metrics)

    if st.sidebar.checkbox("Show raw data", False):
        st.subheader("Mushroom Dataset (Classification)")
        st.write(df)


if __name__ == '__main__':
    main()