save

Dockerfile

@@ -14,6 +14,9 @@ COPY src/ ./src/
 
 RUN pip3 install -r requirements.txt
 
+# Create the outputs directory and set permissions
+RUN mkdir -p outputs && chmod 777 outputs
+
 EXPOSE 8501
 
 HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health

src/streamlit_app.py

@@ -2,6 +2,8 @@ import streamlit as st
 import pandas as pd
 from sklearn.model_selection import train_test_split
 import pickle
+import os
+import pickle
 
 from sklearn.metrics import accuracy_score
 from sklearn.ensemble import RandomForestClassifier
@@ -43,12 +45,18 @@ 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.subheader('Model Output to Pickle')
+st.subheader('Save the Model Output to Pickle')
+
+# Create output directory if it doesn't exist
+output_dir = "outputs"
+os.makedirs(output_dir, exist_ok=True)
+
+# Save the model
+model_filename = os.path.join(output_dir, "random_forest_penguin.pickle")
+with open(model_filename, "wb") as rf_pickle:
+    pickle.dump(rfc, rf_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()
+# Save the uniques or other data
+uniques_filename = os.path.join(output_dir, "uniques_data.pickle")
+with open(uniques_filename, "wb") as output_pickle:
+    pickle.dump(uniques, output_pickle)