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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)) | |