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import json
import re
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
########################### Chapter rendering functions ###########################
def get_chapters(paragraphs, table_of_content):
chapters = []
for i in range(len(table_of_content)):
if i < len(table_of_content) - 1:
chapter = {'num_chapter': i,
'title': table_of_content[i]['title'],
'start_paragraph_number': table_of_content[i]['start_paragraph_number'],
'end_paragraph_number': table_of_content[i + 1]['start_paragraph_number'],
'start_time': paragraphs[table_of_content[i]['start_paragraph_number']]['start_time'],
'end_time': paragraphs[table_of_content[i + 1]['start_paragraph_number']]['start_time'],
}
else:
chapter = {'num_chapter': i,
'title': table_of_content[i]['title'],
'start_paragraph_number': table_of_content[i]['start_paragraph_number'],
'end_paragraph_number': len(paragraphs),
'start_time': paragraphs[table_of_content[i]['start_paragraph_number']]['start_time'],
'end_time': paragraphs[-1]['start_time'],
}
paragraphs_chapter = [paragraphs[j]['paragraph_text'] for j in
range(chapter['start_paragraph_number'], chapter['end_paragraph_number'])]
paragraph_timestamps_chapter = [paragraphs[j]['start_time'] for j in
range(chapter['start_paragraph_number'], chapter['end_paragraph_number'])]
chapter['paragraphs'] = paragraphs_chapter
chapter['paragraph_timestamps'] = paragraph_timestamps_chapter
chapters.append(chapter)
return chapters
def convert_seconds_to_hms(seconds):
# Calculate hours, minutes, and remaining seconds
hours = seconds // 3600
minutes = (seconds % 3600) // 60
remaining_seconds = seconds % 60
# Format the result as HH:MM:SS
return f"{hours:02}:{minutes:02}:{remaining_seconds:02}"
def toc_to_html(chapters):
toc_html = "<h1>Video chapters</h1><p>\n"
for chapter in chapters:
num_chapter = chapter['num_chapter']
title = chapter['title']
from_to = convert_seconds_to_hms(int(chapter['start_time'])) + " - "
toc_html += f"""{from_to}<a href = "#{num_chapter}" >{num_chapter+1} - {title}</a><br>\n"""
return toc_html
def section_to_html(section_json_data):
formatted_section = ""
paragraphs = section_json_data['paragraphs']
paragraphs_timestamp_hms = [convert_seconds_to_hms(int(section_json_data['paragraph_timestamps'][i])) for i in range(len(paragraphs))]
for i, (paragraph, paragraph_timestamp_hms) in enumerate(zip(paragraphs, paragraphs_timestamp_hms)):
formatted_section += f"""
<div class="row mb-4">
<div class="col-md-1">
{paragraph_timestamp_hms}
</div>
<div class="col-md-11">
<p>{paragraph}</p>
</div>
</div>"""
num_section = section_json_data['num_chapter']
from_to = "From "+convert_seconds_to_hms(int(section_json_data['start_time'])) + " to " + convert_seconds_to_hms(
int(section_json_data['end_time']))
title = f"{section_json_data['title']}"
title_link = f"""<div class="transcript-title-icon" " id="{num_section}">{num_section+1} - {title}</div>"""
summary_section = f"""
<a id="{num_section}"><h2 id="{num_section}">{title_link}</h2></a>
{from_to}
<p>
<div class="summary-section">
<div class="summary-text" >
{formatted_section}
</div>
</div>
"""
return summary_section
def get_result_as_html(chapters, video_id):
video_embed = f"""
<iframe width="100%" height="400" src="https://www.youtube.com/embed/{video_id}" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
"""
toc = toc_to_html(chapters)
edited_transcript = f"""
<h1>Structured transcript</h1>
<p>
"""
for i in range(len(chapters)):
chapter_json_data = chapters[i]
edited_transcript += section_to_html(chapter_json_data)
result_as_html = f"""
<link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet">
<div class="container mt-4">
<div class="content">
{video_embed}
</div>
<p>
<div class="content">
{toc}
</div>
<p>
<div class="content">
{edited_transcript}
</div>
</div>"""
return result_as_html
def get_transcript_as_text(transcript):
temp_list = [convert_seconds_to_hms(int(s['start']))+' '+s['text'] for s in transcript]
transcript_as_text = '\n'.join(temp_list)
return transcript_as_text
def load_transcript(video_id):
file_name = f"examples/{video_id}_transcript.json"
with open(file_name, 'r') as file:
transcript = json.load(file)
transcript_as_text = get_transcript_as_text(transcript)
return transcript_as_text
def load_json_chapters(video_id):
file_name = f"examples/{video_id}.json"
with open(file_name, 'r') as file:
chapters = json.load(file)
return chapters
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