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