Update app.py

app.py

@@ -11,17 +11,17 @@ def predict(brakes, input_text):
 
     # preprocess the text by removing filler words
     # Define a list of filler words to remove
-    fillers = ["um", "uh", "hmm", "ha", "er", "ah"]  
+    #fillers = ["um", "uh", "hmm", "ha", "er", "ah"]  
     # Define a regular expression pattern that matches any filler word surrounded by whitespace or punctuation
-    pattern = r"(?<=\s|\b)(" + "|".join(fillers) + r")(?=\s|\b)"
+    #pattern = r"(?<=\s|\b)(" + "|".join(fillers) + r")(?=\s|\b)"
     # Use re.sub to replace the filler words with empty strings
-    clean_input_text = re.sub(pattern, "", input_text)
+    #clean_input_text = re.sub(pattern, "", input_text)
 
     # Do the punctuation restauration
     model = PunctuationModel()
-    output_text = model.restore_punctuation(clean_input_text)
+    output_text = model.restore_punctuation(input_text)
     
-    srt_file = clean_input_text
+    srt_file = input_text
     punctuated = output_text
 
     # if any of the line brake methods are implemented,