Update app.py

app.py

@@ -8,27 +8,26 @@
 import pickle
 
 def main():
+    # Get the dataset from the users GitHub repository
     dataset_path = "https://raw.githubusercontent.com/" + os.environ["GITHUB_REPOSITORY"] +"/master/dataset.csv"
     data = pd.read_csv(dataset_path)
     print()
     print(data.describe())
 
     x=data.iloc[:,:-1]
     y=data.iloc[:,-1]
-
-
-    column_trans = make_column_transformer((OneHotEncoder(),[-1]),remainder='passthrough')
+    column_trans = make_column_transformer((OneHotEncoder(),[-1]),remainder='passthrough') # apply encoding on output variable
     x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state=0)
 
+    #define a pipeline
     pipe = make_pipeline(column_trans,SVC())
-
-    pipe.fit(x_train,y_train)
+    pipe.fit(x_train,y_train) #training the model
     print("\nModel Training Finished")
     accuracy = pipe.score(x_test,y_test)
-
     print("\nAccuracy of the Model: "+str(accuracy*100))
+
     if pipe:
-        pickle.dump(pipe,open('model.pkl','wb'))
+        pickle.dump(pipe,open('model.pkl','wb')) # store the artifact in docker container
 
     if not os.environ["INPUT_MYINPUT"] == 'zeroinputs':
         inputs = ast.literal_eval(os.environ["INPUT_MYINPUT"])
@@ -38,11 +37,12 @@ def main():
     else:
         output = ["None"]
         print("\nUser didn't provided inputs to predict")
+
     print("\n=======================Action Completed========================")
-
-
     print(f"::set-output name=myOutput::{output[0]}")
 
+
+
 
 if __name__ == "__main__":
-    main()
+    main()