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app.py

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+import streamlit as st
+from utils import PrepProcesor, columns 
+
+import numpy as np
+import pandas as pd
+import joblib
+
+model = joblib.load('xgbpipe.joblib')
+st.title('Will you survive if you were among Titanic passengers or not :ship:')
+# PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+passengerid = st.text_input("Input Passenger ID", '8585') 
+pclass = st.selectbox("Choose class", [1,2,3])
+name  = st.text_input("Input Passenger Name", 'Soheil Tehranipour')
+sex = st.select_slider("Choose sex", ['male','female'])
+age = st.slider("Choose age",0,100)
+sibsp = st.slider("Choose siblings",0,10)
+parch = st.slider("Choose parch",0,10)
+ticket = st.text_input("Input Ticket Number", "8585") 
+fare = st.number_input("Input Fare Price", 0,1000)
+cabin = st.text_input("Input Cabin", "C52") 
+embarked = st.select_slider("Did they Embark?", ['S','C','Q'])
+
+def predict(): 
+    row = np.array([passengerid,pclass,name,sex,age,sibsp,parch,ticket,fare,cabin,embarked]) 
+    X = pd.DataFrame([row], columns = columns)
+    prediction = model.predict(X)
+    if prediction[0] == 1: 
+        st.success('Passenger Survived :thumbsup:')
+    else: 
+        st.error('Passenger did not Survive :thumbsdown:') 
+
+trigger = st.button('Predict', on_click=predict)
+

requirements.txt

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+altair==4.2.0
+attrs==22.1.0
+blinker==1.5
+cachetools==5.2.0
+charset-normalizer==2.1.1
+click==8.1.3
+colorama==0.4.6
+commonmark==0.9.1
+contourpy==1.0.6
+cycler==0.11.0
+decorator==5.1.1
+entrypoints==0.4
+fonttools==4.38.0
+gitdb==4.0.10
+GitPython==3.1.29
+idna==3.4
+importlib-metadata==5.1.0
+Jinja2==3.1.2
+joblib==1.2.0
+jsonschema==4.17.3
+kiwisolver==1.4.4
+MarkupSafe==2.1.1
+matplotlib==3.6.2
+numpy==1.23.5
+packaging==21.3
+pandas==1.5.2
+Pillow==9.3.0
+protobuf==3.20.3
+pyarrow==10.0.1
+pydeck==0.8.0
+Pygments==2.13.0
+Pympler==1.0.1
+pyparsing==3.0.9
+pyrsistent==0.19.2
+python-dateutil==2.8.2
+pytz==2022.6
+pytz-deprecation-shim==0.1.0.post0
+requests==2.28.1
+rich==12.6.0
+scikit-learn==1.1.3
+scipy==1.9.3
+seaborn==0.12.1
+semver==2.13.0
+six==1.16.0
+smmap==5.0.0
+streamlit==1.11.1
+threadpoolctl==3.1.0
+toml==0.10.2
+toolz==0.12.0
+tornado==6.2
+typing_extensions==4.4.0
+tzdata==2022.7
+tzlocal==4.2
+urllib3==1.26.13
+validators==0.20.0
+watchdog==2.2.0
+wincertstore==0.2
+xgboost==1.5.0
+zipp==3.11.0

utils.py

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+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.impute import SimpleImputer 
+import re
+
+class PrepProcesor(BaseEstimator, TransformerMixin): 
+    def fit(self, X, y=None): 
+        self.ageImputer = SimpleImputer()
+        self.ageImputer.fit(X[['Age']])        
+        return self 
+        
+    def transform(self, X, y=None):
+        X['Age'] = self.ageImputer.transform(X[['Age']])
+        X['CabinClass'] = X['Cabin'].fillna('M').apply(lambda x: str(x).replace(" ", "")).apply(lambda x: re.sub(r'[^a-zA-Z]', '', x))
+        X['CabinNumber'] = X['Cabin'].fillna('M').apply(lambda x: str(x).replace(" ", "")).apply(lambda x: re.sub(r'[^0-9]', '', x)).replace('', 0) 
+        X['Embarked'] = X['Embarked'].fillna('M')
+        X = X.drop(['PassengerId', 'Name', 'Ticket','Cabin'], axis=1)
+        return X
+
+columns = ['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch','Ticket', 'Fare', 'Cabin', 'Embarked']

xgbpipe.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8117b0f8e5f9c01468c6e2bdf6e6115f00cdaf2ad407244292a88dfdd72a9807
+size 275462