Binary-Classification-Web-App/app.py at main · Avik-Das-567/Binary-Classification-Web-App · GitHub

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import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
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 (ConfusionMatrixDisplay, RocCurveDisplay, PrecisionRecallDisplay, 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? 🍄")

    @st.cache_data(persist = True)
    def load_data():
        data = pd.read_csv('dataset/mushrooms.csv')
        label = LabelEncoder()
        for col in data.columns:
            data[col] = label.fit_transform(data[col])
        return data

    @st.cache_data(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")
            fig, ax = plt.subplots()
            ConfusionMatrixDisplay.from_estimator(model, x_test, y_test, display_labels=class_names, ax = ax)
            st.pyplot(fig)

        if 'ROC Curve' in metrics_list:
            st.subheader("ROC Curve")
            fig, ax = plt.subplots()
            RocCurveDisplay.from_estimator(model, x_test, y_test, ax = ax)
            st.pyplot(fig)

        if 'Precision-Recall Curve' in metrics_list:
            st.subheader("Precision-Recall Curve")
            fig, ax = plt.subplots()
            PrecisionRecallDisplay.from_estimator(model, x_test, y_test, ax = ax)
            st.pyplot(fig)

    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 Hyperparameters")
        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("What 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: ", round(accuracy, 2))
            st.write("Precision: ", round(precision_score(y_test, y_pred, labels = class_names), 2))
            st.write("Recall: ", round(recall_score(y_test, y_pred, labels = class_names), 2))
            plot_metrics(metrics)

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

        metrics = st.sidebar.multiselect("What 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: ", round(accuracy, 2))
            st.write("Precision: ", round(precision_score(y_test, y_pred, labels = class_names), 2))
            st.write("Recall: ", round(recall_score(y_test, y_pred, labels = class_names), 2))
            plot_metrics(metrics)

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

        metrics = st.sidebar.multiselect("What 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, n_jobs = -1)
            model.fit(x_train, y_train)
            accuracy = model.score(x_test, y_test)
            y_pred = model.predict(x_test)
            st.write("Accuracy: ", round(accuracy, 2))
            st.write("Precision: ", round(precision_score(y_test, y_pred, labels = class_names), 2))
            st.write("Recall: ", round(recall_score(y_test, y_pred, labels = class_names), 2))
            plot_metrics(metrics)

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

if __name__ == '__main__':
    main()