init

app.py

@@ -237,7 +237,7 @@ def clean_up_logs():
             os.remove(f)
 
 
-def run(num1, num2, num3, num4, num5, random_seed, submit):
+def run(num1, num2, num3, num4, num5, random_seed, mode):
     sel_num = [num1, num2, num3, num4, num5]
     sel_num = [int(num / 100 * NUM_PER_BUCKET) for num in sel_num]
     torch.manual_seed(int(random_seed))
@@ -246,18 +246,18 @@ def run(num1, num2, num3, num4, num5, random_seed, submit):
     training_df = make_dataframe(all_x, all_y)
 
     clean_up_logs()
-    if submit == 0:
+    if mode == 0:
         visualize(training_df, df_oracle, 'training_data.png')
-    if submit == 1:
+    if mode == 1:
         regress(train_loader, training_df)
         make_video()
-    if submit == 0:
+    if mode == 0:
         text = "Press \"Start Regressing\" if your are happy with the training data. Regression takes ~10s."
     else:
         text = "Press \"Prepare Training Data\" before moving the sliders. You may also change the random seed."
-    training_data_plot = 'training_data.png' if submit == 0 else None
-    output = 'regression_result.png'.format(NUM_EPOCHS) if submit == 1 else None
-    video = "movie.mp4" if submit == 1 else None
+    training_data_plot = 'training_data.png' if mode == 0 else None
+    output = 'regression_result.png'.format(NUM_EPOCHS) if mode == 1 else None
+    video = "movie.mp4" if mode == 1 else None
     return training_data_plot, output, video, text