save

requirements.txt

@@ -1,4 +1,5 @@
 altair
 pandas
 streamlit
-scikit-learn
+scikit-learn
+pickle

src/streamlit_app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 import pandas as pd
 from sklearn.model_selection import train_test_split
+import pickle
 
 from sklearn.metrics import accuracy_score
 from sklearn.ensemble import RandomForestClassifier
@@ -40,4 +41,14 @@ rfc.fit(x_train.values, y_train)
 
 y_pred = rfc.predict(x_test.values)
 score = accuracy_score(y_pred, y_test)
-st.write('Our accuracy score for this model is {}'.format(score))
+st.write('Our accuracy score for this model is {}'.format(score))
+
+st.subheader('Model Output to Pickle')
+
+outputfilename = 'random_forest_penguin.pickle'
+rf_pickle = open( outputfilename, 'wb')
+pickle.dump(rfc, rf_pickle)
+rf_pickle.close()
+output_pickle = open(outputfilename, 'wb')
+pickle.dump(uniques, output_pickle)
+output_pickle.close()