Initial Query

Given a parse function like def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments for data like [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint write a merge() function that (1) calls parse and (2) merges words into sentences that end in period. Start with the first word and end with the period. Return a list of strings.

LLM Generation

Query

Given a parse function like def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments for data like [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint write a merge() function that (1) calls parse and (2) merges words into sentences that end in period. Start with the first word and end with the period. Return a list of strings.

Output

Here's a merge() function that combines the parsed transcript segments into complete sentences ending with periods: 

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def merge(content):
    # First parse the transcript into segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for _, _, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # If the text ends with a period, merge the current sentence and start a new one
        if text.endswith('.'):
            sentences.append(' '.join(current_sentence).replace('  ', ' ').strip())
            current_sentence = []
    
    # Handle any remaining words if the transcript doesn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence).replace('  ', ' ').strip())
    
    return sentences

# Example usage:
content = """[00:00:14.240 --> 00:00:14.420]   the
[00:00:14.420 --> 00:00:14.860]   robust
[00:00:14.860 --> 00:00:15.020]  ness
[00:00:15.020 --> 00:00:15.140]   of
[00:00:15.140 --> 00:00:15.500]   neural
[00:00:15.500 --> 00:00:15.870]   networks
[00:00:15.870 --> 00:00:16.200]  .
[00:00:16.200 --> 00:00:16.410]   And
[00:00:16.410 --> 00:00:16.700]   this
[00:00:16.700 --> 00:00:16.840]   is
[00:00:16.840 --> 00:00:17.200]   joint"""

sentences = merge(content)
# Result would be: ['the robust ness of neural networks.', 'And this is joint']
This merge() function: 1. Calls parse_transcript() to get the initial segments 2. Iterates through the segments, building sentences word by word 3. When it encounters a period, it joins all collected words into a sentence and adds it to the sentences list 4. Handles any remaining words if the transcript doesn't end with a period 5. Uses string joining and replacement to ensure proper spacing 6. Returns a list of complete sentences For the example input, it would produce a list with sentences like: - First sentence: "the robust ness of neural networks." - Second sentence: "And this is joint" The function handles multiple spaces and ensures clean sentence formation. Each sentence in the returned list starts with the first word after the previous period (or the start of the transcript) and ends with a period (if one exists).

Extract Code

I extracted the following code from that output: 

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def merge(content):
    # First parse the transcript into segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for _, _, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # If the text ends with a period, merge the current sentence and start a new one
        if text.endswith('.'):
            sentences.append(' '.join(current_sentence).replace('  ', ' ').strip())
            current_sentence = []
    
    # Handle any remaining words if the transcript doesn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence).replace('  ', ' ').strip())
    
    return sentences

# Example usage:
content = """[00:00:14.240 --> 00:00:14.420]   the
[00:00:14.420 --> 00:00:14.860]   robust
[00:00:14.860 --> 00:00:15.020]  ness
[00:00:15.020 --> 00:00:15.140]   of
[00:00:15.140 --> 00:00:15.500]   neural
[00:00:15.500 --> 00:00:15.870]   networks
[00:00:15.870 --> 00:00:16.200]  .
[00:00:16.200 --> 00:00:16.410]   And
[00:00:16.410 --> 00:00:16.700]   this
[00:00:16.700 --> 00:00:16.840]   is
[00:00:16.840 --> 00:00:17.200]   joint"""

sentences = merge(content)
# Result would be: ['the robust ness of neural networks.', 'And this is joint']

Run Code Interpreter

Running the following program: 

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def merge(content):
    # First parse the transcript into segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for _, _, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # If the text ends with a period, merge the current sentence and start a new one
        if text.endswith('.'):
            sentences.append(' '.join(current_sentence).replace('  ', ' ').strip())
            current_sentence = []
    
    # Handle any remaining words if the transcript doesn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence).replace('  ', ' ').strip())
    
    return sentences

# Example usage:
content = """[00:00:14.240 --> 00:00:14.420]   the
[00:00:14.420 --> 00:00:14.860]   robust
[00:00:14.860 --> 00:00:15.020]  ness
[00:00:15.020 --> 00:00:15.140]   of
[00:00:15.140 --> 00:00:15.500]   neural
[00:00:15.500 --> 00:00:15.870]   networks
[00:00:15.870 --> 00:00:16.200]  .
[00:00:16.200 --> 00:00:16.410]   And
[00:00:16.410 --> 00:00:16.700]   this
[00:00:16.700 --> 00:00:16.840]   is
[00:00:16.840 --> 00:00:17.200]   joint"""

sentences = merge(content)
# Result would be: ['the robust ness of neural networks.', 'And this is joint']




def parse_transcript(content):
    transcript_segments = []
    pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\]  (.*)'
    matches = re.findall(pattern, content)

    for start_time, end_time, text in matches:
        start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds()
        end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds()
        transcript_segments.append((start_seconds, end_seconds, text))

    return transcript_segments


answer = merge("[00:00:14.240 --> 00:00:14.420]   the\n[00:00:14.420 --> 00:00:14.860]   robust\n[00:00:14.860 --> 00:00:15.020]  ness\n[00:00:15.020 --> 00:00:15.140]   of\n[00:00:15.140 --> 00:00:15.500]   neural\n[00:00:15.500 --> 00:00:15.870]   networks\n[00:00:15.870 --> 00:00:16.200]  .\n[00:00:16.200 --> 00:00:16.410]   And\n[00:00:16.410 --> 00:00:16.700]   this\n[00:00:16.700 --> 00:00:16.840]   is\n[00:00:16.840 --> 00:00:17.200]   joint\n")
expected = [' the robustness of neural networks.', ' And this is joint']
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 37, in <module> sentences = merge(content) ^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 4, in merge segments = parse_transcript(content) ^^^^^^^^^^^^^^^^ NameError: name 'parse_transcript' is not defined

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Initial Query

Given a parse function like def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments for data like [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint write a merge() function that (1) calls parse and (2) merges words into sentences that end in period. Start with the first word and end with the period. Return a list of strings.

LLM Generation

Query

Given a parse function like def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments for data like [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint write a merge() function that (1) calls parse and (2) merges words into sentences that end in period. Start with the first word and end with the period. Return a list of strings.

Output

Here's a merge() function that combines words into sentences ending with periods: 

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def merge(content):
    # Get the parsed segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for start, end, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # Check if text ends with period
        if text.endswith('.'):
            # Join all words in current sentence and add to sentences list
            sentences.append(' '.join(current_sentence))
            # Reset current sentence
            current_sentence = []
    
    # Handle any remaining words if content doesn't end with period
    if current_sentence:
        sentences.append(' '.join(current_sentence))
        
    return sentences
For your example input, this would produce: 
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['the robust ness of neural networks.']
The function: 1. Calls parse_transcript() to get the time-aligned segments 2. Iterates through the segments, building up sentences word by word 3. When it encounters a period, it joins all collected words with spaces and adds them to the sentences list 4. Clears the current sentence buffer after completing each sentence 5. Has a final check to handle any remaining words if the content doesn't end with a period Some notes: - It strips whitespace from each text segment to clean up the output - It preserves the period at the end of sentences - If there are words after the last period, they'll be included as a final sentence - It maintains the original ordering of words from the input If you had more content like: 
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[...] networks.
[...] And this is joint
It would return: 
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['the robust ness of neural networks.', 'And this is joint']

Extract Code

I extracted the following code from that output: 

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def parse_transcript(content):
    # Add any parsing logic needed for your transcript format
    # Should return list of tuples with (start_time, end_time, text)
    pass

def merge(content):
    # Get the parsed segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for start, end, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # Check if text ends with period 
        if text.endswith('.'):
            # Join all words in current sentence and add to sentences list
            sentences.append(' '.join(current_sentence))
            # Reset current sentence
            current_sentence = []
    
    # Handle any remaining words if content doesn't end with period
    if current_sentence:
        sentences.append(' '.join(current_sentence))
        
    return sentences

Run Code Interpreter

Running the following program: 

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def parse_transcript(content):
    # Add any parsing logic needed for your transcript format
    # Should return list of tuples with (start_time, end_time, text)
    pass

def merge(content):
    # Get the parsed segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for start, end, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # Check if text ends with period 
        if text.endswith('.'):
            # Join all words in current sentence and add to sentences list
            sentences.append(' '.join(current_sentence))
            # Reset current sentence
            current_sentence = []
    
    # Handle any remaining words if content doesn't end with period
    if current_sentence:
        sentences.append(' '.join(current_sentence))
        
    return sentences




def parse_transcript(content):
    transcript_segments = []
    pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\]  (.*)'
    matches = re.findall(pattern, content)

    for start_time, end_time, text in matches:
        start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds()
        end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds()
        transcript_segments.append((start_seconds, end_seconds, text))

    return transcript_segments


answer = merge("[00:00:14.240 --> 00:00:14.420]   the\n[00:00:14.420 --> 00:00:14.860]   robust\n[00:00:14.860 --> 00:00:15.020]  ness\n[00:00:15.020 --> 00:00:15.140]   of\n[00:00:15.140 --> 00:00:15.500]   neural\n[00:00:15.500 --> 00:00:15.870]   networks\n[00:00:15.870 --> 00:00:16.200]  .\n[00:00:16.200 --> 00:00:16.410]   And\n[00:00:16.410 --> 00:00:16.700]   this\n[00:00:16.700 --> 00:00:16.840]   is\n[00:00:16.840 --> 00:00:17.200]   joint\n")
expected = [' the robustness of neural networks.', ' And this is joint']
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 47, in <module> answer = merge("[00:00:14.240 --> 00:00:14.420] the\n[00:00:14.420 --> 00:00:14.860] robust\n[00:00:14.860 --> 00:00:15.020] ness\n[00:00:15.020 --> 00:00:15.140] of\n[00:00:15.140 --> 00:00:15.500] neural\n[00:00:15.500 --> 00:00:15.870] networks\n[00:00:15.870 --> 00:00:16.200] .\n[00:00:16.200 --> 00:00:16.410] And\n[00:00:16.410 --> 00:00:16.700] this\n[00:00:16.700 --> 00:00:16.840] is\n[00:00:16.840 --> 00:00:17.200] joint\n") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 9, in merge segments = parse_transcript(content) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 37, in parse_transcript matches = re.findall(pattern, content) ^^ NameError: name 're' is not defined. Did you forget to import 're'?

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Initial Query

Given a parse function like def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments for data like [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint write a merge() function that (1) calls parse and (2) merges words into sentences that end in period. Start with the first word and end with the period. Return a list of strings.

LLM Generation

Query

Given a parse function like def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments for data like [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint write a merge() function that (1) calls parse and (2) merges words into sentences that end in period. Start with the first word and end with the period. Return a list of strings.

Output

Here's a merge() function that combines words into sentences ending with periods: 

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def merge(content):
    # Get the parsed segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for _, _, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # If we find a period, join the current sentence and start a new one
        if text.endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []
    
    # Handle any remaining words if the text doesn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence))
        
    return sentences
For your example input, the function would: 1. First parse the transcript into segments using the parse_transcript() function 2. Combine words until it hits a period 3. Return a list of complete sentences For the sample data you provided, it would output: 
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['the robust ness of neural networks.']
If you want to test it with more sentences, here's how it would handle multiple sentences: 
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# Example usage with multiple sentences:
text = """
[00:00:14.240 --> 00:00:14.420]   the
[00:00:14.420 --> 00:00:14.860]   robust
[00:00:14.860 --> 00:00:15.020]  ness
[00:00:15.020 --> 00:00:15.140]   of
[00:00:15.140 --> 00:00:15.500]   neural
[00:00:15.500 --> 00:00:15.870]   networks
[00:00:15.870 --> 00:00:16.200]  .
[00:00:16.200 --> 00:00:16.410]   And
[00:00:16.410 --> 00:00:16.700]   this
[00:00:16.700 --> 00:00:16.840]   is
[00:00:16.840 --> 00:00:17.200]   great."""

# Output would be:
# ['the robust ness of neural networks.', 'And this is great']
The function: 1. Strips whitespace from each text segment 2. Accumulates words into current_sentence until it finds a period 3. When it finds a period, joins the accumulated words with spaces and adds the complete sentence to the sentences list 4. Continues this process until all segments are processed 5. If there are any remaining words that don't end in a period, it adds them as a final sentence

Extract Code

I extracted the following code from that output: 

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def merge(content):
    # Get the parsed segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for _, _, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # If we find a period, join the current sentence and start a new one
        if text.endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []
    
    # Handle any remaining words if the text doesn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence))
        
    return sentences

Run Code Interpreter

Running the following program: 

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def merge(content):
    # Get the parsed segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for _, _, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # If we find a period, join the current sentence and start a new one
        if text.endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []
    
    # Handle any remaining words if the text doesn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence))
        
    return sentences




def parse_transcript(content):
    transcript_segments = []
    pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\]  (.*)'
    matches = re.findall(pattern, content)

    for start_time, end_time, text in matches:
        start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds()
        end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds()
        transcript_segments.append((start_seconds, end_seconds, text))

    return transcript_segments


answer = merge("[00:00:14.240 --> 00:00:14.420]   the\n[00:00:14.420 --> 00:00:14.860]   robust\n[00:00:14.860 --> 00:00:15.020]  ness\n[00:00:15.020 --> 00:00:15.140]   of\n[00:00:15.140 --> 00:00:15.500]   neural\n[00:00:15.500 --> 00:00:15.870]   networks\n[00:00:15.870 --> 00:00:16.200]  .\n[00:00:16.200 --> 00:00:16.410]   And\n[00:00:16.410 --> 00:00:16.700]   this\n[00:00:16.700 --> 00:00:16.840]   is\n[00:00:16.840 --> 00:00:17.200]   joint\n")
expected = [' the robustness of neural networks.', ' And this is joint']
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 40, in <module> answer = merge("[00:00:14.240 --> 00:00:14.420] the\n[00:00:14.420 --> 00:00:14.860] robust\n[00:00:14.860 --> 00:00:15.020] ness\n[00:00:15.020 --> 00:00:15.140] of\n[00:00:15.140 --> 00:00:15.500] neural\n[00:00:15.500 --> 00:00:15.870] networks\n[00:00:15.870 --> 00:00:16.200] .\n[00:00:16.200 --> 00:00:16.410] And\n[00:00:16.410 --> 00:00:16.700] this\n[00:00:16.700 --> 00:00:16.840] is\n[00:00:16.840 --> 00:00:17.200] joint\n") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 4, in merge segments = parse_transcript(content) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 30, in parse_transcript matches = re.findall(pattern, content) ^^ NameError: name 're' is not defined. Did you forget to import 're'?

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Initial Query

Given a parse function like def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments for data like [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint write a merge() function that (1) calls parse and (2) merges words into sentences that end in period. Start with the first word and end with the period. Return a list of strings.

LLM Generation

Query

Given a parse function like def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments for data like [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint write a merge() function that (1) calls parse and (2) merges words into sentences that end in period. Start with the first word and end with the period. Return a list of strings.

Output

Here's a merge() function that combines words into sentences ending with periods: 

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def merge(content):
    # First parse the transcript into segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for _, _, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # If we find a period, join the current sentence and start a new one
        if text.endswith('.'):
            sentences.append(' '.join(current_sentence).strip())
            current_sentence = []
    
    # Handle any remaining words if the text doesn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence).strip())
        
    return sentences

# For your example, this would output:
# ["the robust ness of neural networks."]
# ["And this is joint"]
This function: 1. Calls parse_transcript() to get the time-aligned segments 2. Iterates through the segments, accumulating words into current_sentence 3. When it encounters a period, joins the accumulated words with spaces and adds them to the sentences list 4. Clears current_sentence to start collecting the next sentence 5. At the end, if there are any remaining words that don't end in a period, it adds them as a final sentence For your example input, it would combine: - "the robust ness of neural networks." into one sentence - "And this is joint" into another sentence The function handles: - Multiple sentences in the input - Sentences that don't end with periods (keeping them as incomplete sentences) - Proper spacing between words - Maintaining the original text while just combining the segments Note that this assumes that periods are used as sentence terminators and appear as separate segments in your input (as shown in your example where "." is its own segment).

Extract Code

I extracted the following code from that output: 

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def merge(content):
    # First parse the transcript into segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for _, _, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # If we find a period, join the current sentence and start a new one
        if text.endswith('.'):
            sentences.append(' '.join(current_sentence).strip())
            current_sentence = []
    
    # Handle any remaining words if the text doesn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence).strip())
        
    return sentences

# For your example, this would output:
# ["the robust ness of neural networks."]
# ["And this is joint"]

Run Code Interpreter

Running the following program: 

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def merge(content):
    # First parse the transcript into segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for _, _, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # If we find a period, join the current sentence and start a new one
        if text.endswith('.'):
            sentences.append(' '.join(current_sentence).strip())
            current_sentence = []
    
    # Handle any remaining words if the text doesn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence).strip())
        
    return sentences

# For your example, this would output:
# ["the robust ness of neural networks."]
# ["And this is joint"]




def parse_transcript(content):
    transcript_segments = []
    pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\]  (.*)'
    matches = re.findall(pattern, content)

    for start_time, end_time, text in matches:
        start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds()
        end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds()
        transcript_segments.append((start_seconds, end_seconds, text))

    return transcript_segments


answer = merge("[00:00:14.240 --> 00:00:14.420]   the\n[00:00:14.420 --> 00:00:14.860]   robust\n[00:00:14.860 --> 00:00:15.020]  ness\n[00:00:15.020 --> 00:00:15.140]   of\n[00:00:15.140 --> 00:00:15.500]   neural\n[00:00:15.500 --> 00:00:15.870]   networks\n[00:00:15.870 --> 00:00:16.200]  .\n[00:00:16.200 --> 00:00:16.410]   And\n[00:00:16.410 --> 00:00:16.700]   this\n[00:00:16.700 --> 00:00:16.840]   is\n[00:00:16.840 --> 00:00:17.200]   joint\n")
expected = [' the robustness of neural networks.', ' And this is joint']
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 44, in <module> answer = merge("[00:00:14.240 --> 00:00:14.420] the\n[00:00:14.420 --> 00:00:14.860] robust\n[00:00:14.860 --> 00:00:15.020] ness\n[00:00:15.020 --> 00:00:15.140] of\n[00:00:15.140 --> 00:00:15.500] neural\n[00:00:15.500 --> 00:00:15.870] networks\n[00:00:15.870 --> 00:00:16.200] .\n[00:00:16.200 --> 00:00:16.410] And\n[00:00:16.410 --> 00:00:16.700] this\n[00:00:16.700 --> 00:00:16.840] is\n[00:00:16.840 --> 00:00:17.200] joint\n") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 4, in merge segments = parse_transcript(content) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 34, in parse_transcript matches = re.findall(pattern, content) ^^ NameError: name 're' is not defined. Did you forget to import 're'?

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Initial Query

Given a parse function like def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments for data like [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint write a merge() function that (1) calls parse and (2) merges words into sentences that end in period. Start with the first word and end with the period. Return a list of strings.

LLM Generation

Query

Given a parse function like def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments for data like [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint write a merge() function that (1) calls parse and (2) merges words into sentences that end in period. Start with the first word and end with the period. Return a list of strings.

Output

Here's a merge() function that combines words into sentences ending with periods: 

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def merge(content):
    # Get the parsed segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for _, _, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # If we find a period, join the current sentence and start a new one
        if text.endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []
    
    # Handle any remaining words if the transcript doesn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence))
    
    return sentences
For your example input, this would produce: 
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["the robust ness of neural networks."]
["And this is joint"]
The function: 1. Calls parse_transcript() to get the time-aligned segments 2. Iterates through the segments, building up sentences word by word 3. When it encounters a period, it joins all accumulated words with spaces and adds them to the sentences list 4. Clears the current_sentence buffer to start collecting the next sentence 5. Finally, handles any remaining words if the transcript doesn't end with a period A few notes: - The function strips whitespace from each word to avoid extra spaces - It preserves the period at the end of sentences - If the input doesn't end with a period, it will still return any remaining words as a sentence - The timestamps are ignored since we only need the text for merging You could use it like this: 
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content = """[00:00:14.240 --> 00:00:14.420]   the
[00:00:14.420 --> 00:00:14.860]   robust
[00:00:14.860 --> 00:00:15.020]  ness
[00:00:15.020 --> 00:00:15.140]   of
[00:00:15.140 --> 00:00:15.500]   neural
[00:00:15.500 --> 00:00:15.870]   networks
[00:00:15.870 --> 00:00:16.200]  .
[00:00:16.200 --> 00:00:16.410]   And
[00:00:16.410 --> 00:00:16.700]   this
[00:00:16.700 --> 00:00:16.840]   is
[00:00:16.840 --> 00:00:17.200]   joint"""

sentences = merge(content)

Extract Code

I extracted the following code from that output: 

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def parse_transcript(content):
    segments = []
    for line in content.strip().split('\n'):
        # Extract the timestamp and text
        timestamp = line[1:24]  # [00:00:14.240 --> 00:00:14.420]
        text = line[24:].strip()
        
        # Parse start and end times
        start_time = timestamp[0:12]
        end_time = timestamp[17:29]
        
        segments.append((start_time, end_time, text))
        
    return segments

def merge(content):
    # Get the parsed segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for _, _, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # If we find a period, join the current sentence and start a new one
        if text.endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []
    
    # Handle any remaining words if the transcript doesn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence))
    
    return sentences

Run Code Interpreter

Running the following program: 

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def parse_transcript(content):
    segments = []
    for line in content.strip().split('\n'):
        # Extract the timestamp and text
        timestamp = line[1:24]  # [00:00:14.240 --> 00:00:14.420]
        text = line[24:].strip()
        
        # Parse start and end times
        start_time = timestamp[0:12]
        end_time = timestamp[17:29]
        
        segments.append((start_time, end_time, text))
        
    return segments

def merge(content):
    # Get the parsed segments
    segments = parse_transcript(content)
    
    sentences = []
    current_sentence = []
    
    for _, _, text in segments:
        text = text.strip()
        current_sentence.append(text)
        
        # If we find a period, join the current sentence and start a new one
        if text.endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []
    
    # Handle any remaining words if the transcript doesn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence))
    
    return sentences




def parse_transcript(content):
    transcript_segments = []
    pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\]  (.*)'
    matches = re.findall(pattern, content)

    for start_time, end_time, text in matches:
        start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds()
        end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds()
        transcript_segments.append((start_seconds, end_seconds, text))

    return transcript_segments


answer = merge("[00:00:14.240 --> 00:00:14.420]   the\n[00:00:14.420 --> 00:00:14.860]   robust\n[00:00:14.860 --> 00:00:15.020]  ness\n[00:00:15.020 --> 00:00:15.140]   of\n[00:00:15.140 --> 00:00:15.500]   neural\n[00:00:15.500 --> 00:00:15.870]   networks\n[00:00:15.870 --> 00:00:16.200]  .\n[00:00:16.200 --> 00:00:16.410]   And\n[00:00:16.410 --> 00:00:16.700]   this\n[00:00:16.700 --> 00:00:16.840]   is\n[00:00:16.840 --> 00:00:17.200]   joint\n")
expected = [' the robustness of neural networks.', ' And this is joint']
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 55, in <module> answer = merge("[00:00:14.240 --> 00:00:14.420] the\n[00:00:14.420 --> 00:00:14.860] robust\n[00:00:14.860 --> 00:00:15.020] ness\n[00:00:15.020 --> 00:00:15.140] of\n[00:00:15.140 --> 00:00:15.500] neural\n[00:00:15.500 --> 00:00:15.870] networks\n[00:00:15.870 --> 00:00:16.200] .\n[00:00:16.200 --> 00:00:16.410] And\n[00:00:16.410 --> 00:00:16.700] this\n[00:00:16.700 --> 00:00:16.840] is\n[00:00:16.840 --> 00:00:17.200] joint\n") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 19, in merge segments = parse_transcript(content) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 45, in parse_transcript matches = re.findall(pattern, content) ^^ NameError: name 're' is not defined. Did you forget to import 're'?

Substring Evaluation

Testing if the previous output contains the string All tests passed: False