Upload app2.py

app2.py

@@ -0,0 +1,107 @@
+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))