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")
    st.sidebar.title("Binary Classification Web App")
    st.markdown("Are your mushrooms edible or posinous")
    st.sidebar.markdown("Are your mushrooms edible or posinous")

#unless the name of function or input ot argument change,
#we can simply cash the output to disk and use the output from disk
#important for large dataset
    @st.cache(persist=True)
    def load_data():
        data=pd.read_csv("/home/rhyme/Desktop/Project/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

#metrics to understand the evaluation 
    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()
            #we need high recall for model

        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','poisinous']

    st.sidebar.subheader('choose Classifier')
    classifier=st.sidebar.selectbox("Classifier", ("Support Vector Machine (SVM)","Logistic Regression","Random Forest"))
    #once the selection of classifier is seletced then only hyperparamters will be shown 

    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 ="linear")
        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")
            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 Hyperparameters")
        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')

#Ask user what evaluation metrics you want to see plotted out on web app

        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")
            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 Hyperparameters")
        n_estimators=st.sidebar.number_input("The numbers of tress in the forest", 100, 5000, steps=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')
#Ask user what evaluation metrics you want to see plotted out on web app

        metrics=st.sidebar.multiselect("what metrics to plot?",('Confusion matrix','ROC Curve','Precision-Recall Curve'))

        if st.sidebar.button("Classify",key='classify'):
            st.subheader("RandomForest")
            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: ", 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)

           
#Adding checkbox to sidebar

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

if __name__ == '__main__':
    main()