app1.py

import pandas as pd
import time
import streamlit as st
import plotly.express as px


from pycaret.classification import *
import streamlit as st
import pandas as pd


model_gr = load_model('deploy_gboost')
model_rf=load_model('deploy_rand_for')
model_lr=load_model('deploy_log_reg')


def predict(model, input_df):
    predictions_df = predict_model(estimator=model, data=input_df)
    predictions = predictions_df['Label'][0]
    return predictions


def app():

    from PIL import Image
    st.title('Streamlit Example')
    st.write("""
      # Explore different classifier 
     """)
    st.write("Titanic Dataset")
    classifier_name = st.sidebar.selectbox(
    'Select classifier',
    ('Gradient Boost', 'Random Forest', 'Logistic Regression')
    )


    st.title("Titanic Prediction App")
    Age = st.number_input('Age', min_value=1, max_value=100, value=25)
    Sex = st.selectbox('Sex', ['male', 'female'])
    Pclass= st.number_input('P Class', 1,3)
    SibSp=  st.multiselect('Number of Siblings And Spouse',[0,1,2,3,4,5,8])
    Parch= st.multiselect('Parch',[0,1,2,3,4,5,6])
    Fare=  st.slider('Fare', 0,600)
    Embarked = st.selectbox('Embarked', ['S', 'C', 'Q'])
    
    output=""

    input_dict = {'Age' : Age, 'Sex' : Sex, 'Pclass':Pclass,'SibSp':SibSp,'Parch':Parch,'Fare':Fare,'Embarked':Embarked}
    input_df = pd.DataFrame([input_dict])
    st.dataframe(input_df) 
    if st.button("Predict"):
        if classifier_name=='Gradient Boost':
            output = predict(model=model_gr, input_df=input_df)
            output = '$' + str(output)
            st.success('The output is {}'.format(output))
        elif classifier_name=='Random Forest':
            output = predict(model=model_rf, input_df=input_df)
            output = '$' + str(output)
            st.success('The output is {}'.format(output))
        else:
            output = predict(model=model_lr, input_df=input_df)
            output = '$' + str(output)
            st.success('The output is {}'.format(output))