app2.py

import pandas as pd
import streamlit as st


def app():
    import joblib
    st.title('SKLEARN')
    st.write('Welcome to app2 sklearn')
    st.title('Streamlit Example')
    st.write("""
    # Explore different classifier
    """)
    st.write("Titanic Dataset")

    Pclass = st.number_input('P Class', 1, 3)
    Sex = st.selectbox('Sex', ['male', 'female'])
    Age = st.number_input('Age', min_value=1, max_value=100, value=25)
    Fare = st.slider('Fare', 0, 600)
    Cabin = st.selectbox('Cabin', [0, 0.4, 0.8, 1.2, 1.6, 2, 2.4, 2.8])
    Embarked = st.selectbox('Embarked', ['S', 'C', 'Q'])
    #Title = st.selectbox('Title', ['Mr', 'Ms', 'Mrs', 'Master', 'Others'])
    #SibSp=  st.selectbox('Number of Siblings And Spouse',[0,1,2,3,4,5,8])
    #Parch= st.selectbox('Parch',[0,1,2,3,4,5,6])
    #FamilySize =  int(SibSp + Parch + 1)
    FamilySize = st.slider('Family size', 1, 11)
    
    if Sex == "male":
        Title = st.selectbox('Title', ['Mr', 'Master', 'Others'])
    else:
        Title = st.selectbox('Title', ['Ms', 'Mrs', 'Others'])

    input_dict = {
        'Pclass': Pclass,
        'Sex': Sex,
        'Age': Age,
        'Fare': Fare,
        'Cabin': Cabin,
        'Embarked': Embarked,
        'Title': Title,
        'FamilySize': FamilySize}
    input_df = pd.DataFrame([input_dict])

    dic_sex = {"male": 0, "female": 1}
    input_df["Sex"] = input_df["Sex"].map(dic_sex)


    title_mapping = {'Mr': 0, 'Ms': 1, 'Mrs': 2, 'Master': 3, 'Others': 4}
    input_df['Title'] = input_df['Title'].map(title_mapping)

    embarked_mapping = {"S": 0, "C": 1, "Q": 2}
    input_df['Embarked'] = input_df['Embarked'].map(embarked_mapping)

    #cabin_mapping = {"A": 0, "B": 0.4, "C": 0.8, "D": 1.2, "E": 1.6, "F": 2, "G": 2.4, "T": 2.8}
    #input_df['Cabin'] = input_df['Cabin'].map(cabin_mapping)

    family_mapping = {
        1: 0,
        2: 0.4,
        3: 0.8,
        4: 1.2,
        5: 1.6,
        6: 2,
        7: 2.4,
        8: 2.8,
        9: 3.2,
        10: 3.6,
        11: 4}
    input_df['FamilySize'] = input_df['FamilySize'].map(family_mapping)

    if Fare <= 17:
        input_df["Fare"] = 0
    elif (Fare > 17 & Fare <= 30):
        input_df["Fare"] = 1
    elif (Fare > 30 & Fare <= 100):
        input_df["Fare"] = 2
    elif (Fare > 100):
        input_df["Fare"] = 3

    if Age <= 16:
        input_df["Age"] = 0
    elif (Age > 16 and Age <= 25):
        input_df["Age"] = 1
    elif (Age > 25 and Age <= 35):
        input_df["Age"] = 2
    elif (Age > 35 and Age <= 45):
        input_df["Age"] = 3
    elif (Age > 45):
        input_df["Age"] = 4

    print(input_df)

    st.dataframe(input_df)

    file_upload = st.file_uploader(
        "Upload sav file for prediction", type=["sav"])

    if file_upload is not None:

        load_clf = joblib.load(file_upload)
        output = load_clf.predict(input_df)
        if output == 0:
            output = "Not survived"
        else:
             output = "Survived"

        if st.button("Predict"):
            st.success('The output is {} '.format(output))