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 mushroom is editable or poisionous? ")
    st.sidebar.markdown("Are your mushroom is editable or poisionous? ")

    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


    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', 'poisionous']
    st.sidebar.subheader("Chosse Classifiers")
    classifier = st.sidebar.selectbox("Classifier", ("Support Vector Machine(SVM)", "Logostics 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='kernal')
        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 == "Logostics 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')

        # kernel = st.sidebar.radio("kernel", ("rbf", "linear"), key='kernal')
        # 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("Logistics 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)

    #Random Forest
    if classifier == "Random Forest":
        st.sidebar.subheader("Model Hyperparameters")
        n_estimators = st.sidebar.number_input("The number of trees in the forest", 100, 500, step=10,key='n_estmators')
        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 builoding trees", ('True','False'),key='bootstrap')
        # 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')

        # kernel = st.sidebar.radio("kernel", ("rbf", "linear"), key='kernal')
        # 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("Random Forest")
            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 data Set (Classifications)")
        st.write(df)


if __name__ == '__main__':
    main()