Solution: Plot Evaluation Metrics using Streamlit

import streamlit as st
from  sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC

def getClassifier(classifier):
    if classifier == 'SVM':
        c = st.sidebar.slider(label='Choose value of C' , min_value=0.0001, max_value=10.0)
        model = SVC(C=c)
    elif classifier == 'KNN':
        neighbors = st.sidebar.slider(label='Choose Number of Neighbors',min_value=1,max_value=20)
        model = KNeighborsClassifier(n_neighbors = neighbors)
    else:
        max_depth = st.sidebar.slider('max_depth', 2, 10)
        n_estimators = st.sidebar.slider('n_estimators', 1, 100)
        model = RandomForestClassifier(max_depth = max_depth , n_estimators= n_estimators,random_state= 1)
    return model


st.title("Classifiers in Action")

# Description
st.text("Breast Cancer Dataset")

#sidebar
sideBar = st.sidebar
classifier = sideBar.selectbox('Which Classifier do you want to use?',('SVM' , 'KNN' , 'Random Forest'))
getClassifier(classifier)
# The below line will create a multiselect option for evaluation metrics.

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