# Video_transcription_tab.py
# Description: This file contains the code for the video transcription tab in the Gradio UI.
#
# Imports
import json
import logging
import os
#
# External Imports
import gradio as gr
import yt_dlp
from App_Function_Libraries.Confabulation_check import simplified_geval
#
# Local Imports
from App_Function_Libraries.DB.DB_Manager import load_preset_prompts, add_media_to_database
from App_Function_Libraries.Gradio_UI.Gradio_Shared import whisper_models, update_user_prompt
from App_Function_Libraries.Gradio_UI.Gradio_Shared import error_handler
from App_Function_Libraries.Summarization_General_Lib import perform_transcription, perform_summarization, \
save_transcription_and_summary
from App_Function_Libraries.Utils.Utils import convert_to_seconds, safe_read_file, format_transcription, \
create_download_directory, generate_unique_identifier, extract_text_from_segments
from App_Function_Libraries.Video_DL_Ingestion_Lib import parse_and_expand_urls, extract_metadata, download_video
#
################################################################################################################################################################
#
# Functions:
def create_video_transcription_tab():
with (gr.TabItem("Video Transcription + Summarization")):
gr.Markdown("# Transcribe & Summarize Videos from URLs")
with gr.Row():
gr.Markdown("""Follow this project at [tldw - GitHub](https://github.com/rmusser01/tldw)""")
with gr.Row():
gr.Markdown(
"""If you're wondering what all this is, please see the 'Introduction/Help' tab up above for more detailed information and how to obtain an API Key.""")
with gr.Row():
with gr.Column():
url_input = gr.Textbox(label="URL(s) (Mandatory)",
placeholder="Enter video URLs here, one per line. Supports YouTube, Vimeo, other video sites and Youtube playlists.",
lines=5)
video_file_input = gr.File(label="Upload Video File (Optional)", file_types=["video/*"])
diarize_input = gr.Checkbox(label="Enable Speaker Diarization", value=False)
whisper_model_input = gr.Dropdown(choices=whisper_models, value="medium", label="Whisper Model")
with gr.Row():
custom_prompt_checkbox = gr.Checkbox(label="Use a Custom Prompt",
value=False,
visible=True)
preset_prompt_checkbox = gr.Checkbox(label="Use a pre-set Prompt",
value=False,
visible=True)
with gr.Row():
preset_prompt = gr.Dropdown(label="Select Preset Prompt",
choices=load_preset_prompts(),
visible=False)
with gr.Row():
custom_prompt_input = gr.Textbox(label="Custom Prompt",
placeholder="Enter custom prompt here",
lines=3,
visible=False)
with gr.Row():
system_prompt_input = gr.Textbox(label="System Prompt",
value="""You are a bulleted notes specialist. [INST]```When creating comprehensive bulleted notes, you should follow these guidelines: Use multiple headings based on the referenced topics, not categories like quotes or terms. Headings should be surrounded by bold formatting and not be listed as bullet points themselves. Leave no space between headings and their corresponding list items underneath. Important terms within the content should be emphasized by setting them in bold font. Any text that ends with a colon should also be bolded. Before submitting your response, review the instructions, and make any corrections necessary to adhered to the specified format. Do not reference these instructions within the notes.``` \nBased on the content between backticks create comprehensive bulleted notes.[/INST]
**Bulleted Note Creation Guidelines**
**Headings**:
- Based on referenced topics, not categories like quotes or terms
- Surrounded by **bold** formatting
- Not listed as bullet points
- No space between headings and list items underneath
**Emphasis**:
- **Important terms** set in bold font
- **Text ending in a colon**: also bolded
**Review**:
- Ensure adherence to specified format
- Do not reference these instructions in your response.[INST] {{ .Prompt }} [/INST]
""",
lines=3,
visible=False,
interactive=True)
custom_prompt_checkbox.change(
fn=lambda x: (gr.update(visible=x), gr.update(visible=x)),
inputs=[custom_prompt_checkbox],
outputs=[custom_prompt_input, system_prompt_input]
)
preset_prompt_checkbox.change(
fn=lambda x: gr.update(visible=x),
inputs=[preset_prompt_checkbox],
outputs=[preset_prompt]
)
def update_prompts(preset_name):
prompts = update_user_prompt(preset_name)
return (
gr.update(value=prompts["user_prompt"], visible=True),
gr.update(value=prompts["system_prompt"], visible=True)
)
preset_prompt.change(
update_prompts,
inputs=preset_prompt,
outputs=[custom_prompt_input, system_prompt_input]
)
api_name_input = gr.Dropdown(
choices=[None, "Local-LLM", "OpenAI", "Anthropic", "Cohere", "Groq", "DeepSeek", "Mistral",
"OpenRouter",
"Llama.cpp", "Kobold", "Ooba", "Tabbyapi", "VLLM", "ollama", "HuggingFace"],
value=None, label="API Name (Mandatory)")
api_key_input = gr.Textbox(label="API Key (Mandatory)", placeholder="Enter your API key here",
type="password")
keywords_input = gr.Textbox(label="Keywords", placeholder="Enter keywords here (comma-separated)",
value="default,no_keyword_set")
batch_size_input = gr.Slider(minimum=1, maximum=10, value=1, step=1,
label="Batch Size (Number of videos to process simultaneously)")
timestamp_option = gr.Radio(choices=["Include Timestamps", "Exclude Timestamps"],
value="Include Timestamps", label="Timestamp Option")
keep_original_video = gr.Checkbox(label="Keep Original Video", value=False)
# First, create a checkbox to toggle the chunking options
chunking_options_checkbox = gr.Checkbox(label="Show Chunking Options", value=False)
summarize_recursively = gr.Checkbox(label="Enable Recursive Summarization", value=False)
use_cookies_input = gr.Checkbox(label="Use cookies for authenticated download", value=False)
use_time_input = gr.Checkbox(label="Use Start and End Time", value=False)
confab_checkbox = gr.Checkbox(label="Perform Confabulation Check of Summary", value=False)
with gr.Row(visible=False) as time_input_box:
gr.Markdown("### Start and End time")
with gr.Column():
start_time_input = gr.Textbox(label="Start Time (Optional)",
placeholder="e.g., 1:30 or 90 (in seconds)")
end_time_input = gr.Textbox(label="End Time (Optional)",
placeholder="e.g., 5:45 or 345 (in seconds)")
use_time_input.change(
fn=lambda x: gr.update(visible=x),
inputs=[use_time_input],
outputs=[time_input_box]
)
cookies_input = gr.Textbox(
label="User Session Cookies",
placeholder="Paste your cookies here (JSON format)",
lines=3,
visible=False
)
use_cookies_input.change(
fn=lambda x: gr.update(visible=x),
inputs=[use_cookies_input],
outputs=[cookies_input]
)
# Then, create a Box to group the chunking options
with gr.Row(visible=False) as chunking_options_box:
gr.Markdown("### Chunking Options")
with gr.Column():
chunk_method = gr.Dropdown(choices=['words', 'sentences', 'paragraphs', 'tokens'],
label="Chunking Method")
max_chunk_size = gr.Slider(minimum=100, maximum=1000, value=300, step=50,
label="Max Chunk Size")
chunk_overlap = gr.Slider(minimum=0, maximum=100, value=0, step=10, label="Chunk Overlap")
use_adaptive_chunking = gr.Checkbox(
label="Use Adaptive Chunking (Adjust chunking based on text complexity)")
use_multi_level_chunking = gr.Checkbox(label="Use Multi-level Chunking")
chunk_language = gr.Dropdown(choices=['english', 'french', 'german', 'spanish'],
label="Chunking Language")
# Add JavaScript to toggle the visibility of the chunking options box
chunking_options_checkbox.change(
fn=lambda x: gr.update(visible=x),
inputs=[chunking_options_checkbox],
outputs=[chunking_options_box]
)
process_button = gr.Button("Process Videos")
with gr.Column():
progress_output = gr.Textbox(label="Progress")
error_output = gr.Textbox(label="Errors", visible=False)
results_output = gr.HTML(label="Results")
confabulation_output = gr.Textbox(label="Confabulation Check Results", visible=False)
download_transcription = gr.File(label="Download All Transcriptions as JSON")
download_summary = gr.File(label="Download All Summaries as Text")
@error_handler
def process_videos_with_error_handling(inputs, start_time, end_time, diarize, whisper_model,
custom_prompt_checkbox, custom_prompt, chunking_options_checkbox,
chunk_method, max_chunk_size, chunk_overlap, use_adaptive_chunking,
use_multi_level_chunking, chunk_language, api_name,
api_key, keywords, use_cookies, cookies, batch_size,
timestamp_option, keep_original_video, summarize_recursively,
progress: gr.Progress = gr.Progress()) -> tuple:
try:
logging.info("Entering process_videos_with_error_handling")
logging.info(f"Received inputs: {inputs}")
if not inputs:
raise ValueError("No inputs provided")
logging.debug("Input(s) is(are) valid")
# Ensure batch_size is an integer
try:
batch_size = int(batch_size)
except (ValueError, TypeError):
batch_size = 1 # Default to processing one video at a time if invalid
# Separate URLs and local files
urls = [input for input in inputs if
isinstance(input, str) and input.startswith(('http://', 'https://'))]
local_files = [input for input in inputs if
isinstance(input, str) and not input.startswith(('http://', 'https://'))]
# Parse and expand URLs if there are any
expanded_urls = parse_and_expand_urls(urls) if urls else []
valid_local_files = []
invalid_local_files = []
for file_path in local_files:
if os.path.exists(file_path):
valid_local_files.append(file_path)
else:
invalid_local_files.append(file_path)
error_message = f"Local file not found: {file_path}"
logging.error(error_message)
if invalid_local_files:
logging.warning(f"Found {len(invalid_local_files)} invalid local file paths")
# FIXME - Add more complete error handling for invalid local files
all_inputs = expanded_urls + valid_local_files
logging.info(f"Total valid inputs to process: {len(all_inputs)} "
f"({len(expanded_urls)} URLs, {len(valid_local_files)} local files)")
all_inputs = expanded_urls + local_files
logging.info(f"Total inputs to process: {len(all_inputs)}")
results = []
errors = []
results_html = ""
all_transcriptions = {}
all_summaries = ""
for i in range(0, len(all_inputs), batch_size):
batch = all_inputs[i:i + batch_size]
batch_results = []
for input_item in batch:
try:
start_seconds = convert_to_seconds(start_time)
end_seconds = convert_to_seconds(end_time) if end_time else None
logging.info(f"Attempting to extract metadata for {input_item}")
if input_item.startswith(('http://', 'https://')):
logging.info(f"Attempting to extract metadata for URL: {input_item}")
video_metadata = extract_metadata(input_item, use_cookies, cookies)
if not video_metadata:
raise ValueError(f"Failed to extract metadata for {input_item}")
else:
logging.info(f"Processing local file: {input_item}")
video_metadata = {"title": os.path.basename(input_item), "url": input_item}
chunk_options = {
'method': chunk_method,
'max_size': max_chunk_size,
'overlap': chunk_overlap,
'adaptive': use_adaptive_chunking,
'multi_level': use_multi_level_chunking,
'language': chunk_language
} if chunking_options_checkbox else None
if custom_prompt_checkbox:
custom_prompt = custom_prompt
else:
custom_prompt = ("""
You are a bulleted notes specialist. [INST]```When creating comprehensive bulleted notes, you should follow these guidelines: Use multiple headings based on the referenced topics, not categories like quotes or terms. Headings should be surrounded by bold formatting and not be listed as bullet points themselves. Leave no space between headings and their corresponding list items underneath. Important terms within the content should be emphasized by setting them in bold font. Any text that ends with a colon should also be bolded. Before submitting your response, review the instructions, and make any corrections necessary to adhered to the specified format. Do not reference these instructions within the notes.``` \nBased on the content between backticks create comprehensive bulleted notes.[/INST]
**Bulleted Note Creation Guidelines**
**Headings**:
- Based on referenced topics, not categories like quotes or terms
- Surrounded by **bold** formatting
- Not listed as bullet points
- No space between headings and list items underneath
**Emphasis**:
- **Important terms** set in bold font
- **Text ending in a colon**: also bolded
**Review**:
- Ensure adherence to specified format
- Do not reference these instructions in your response.[INST] {{ .Prompt }} [/INST]
""")
logging.debug("Gradio_Related.py: process_url_with_metadata being called")
result = process_url_with_metadata(
input_item, 2, whisper_model,
custom_prompt,
start_seconds, api_name, api_key,
False, False, False, False, 0.01, None, keywords, None, diarize,
end_time=end_seconds,
include_timestamps=(timestamp_option == "Include Timestamps"),
metadata=video_metadata,
use_chunking=chunking_options_checkbox,
chunk_options=chunk_options,
keep_original_video=keep_original_video,
current_whisper_model=whisper_model,
)
if result[0] is None:
error_message = "Processing failed without specific error"
batch_results.append(
(input_item, error_message, "Error", video_metadata, None, None))
errors.append(f"Error processing {input_item}: {error_message}")
else:
url, transcription, summary, json_file, summary_file, result_metadata = result
if transcription is None:
error_message = f"Processing failed for {input_item}: Transcription is None"
batch_results.append(
(input_item, error_message, "Error", result_metadata, None, None))
errors.append(error_message)
else:
batch_results.append(
(input_item, transcription, "Success", result_metadata, json_file,
summary_file))
except Exception as e:
error_message = f"Error processing {input_item}: {str(e)}"
logging.error(error_message, exc_info=True)
batch_results.append((input_item, error_message, "Error", {}, None, None))
errors.append(error_message)
results.extend(batch_results)
logging.debug(f"Processed {len(batch_results)} videos in batch")
if isinstance(progress, gr.Progress):
progress((i + len(batch)) / len(all_inputs),
f"Processed {i + len(batch)}/{len(all_inputs)} videos")
# Generate HTML for results
logging.debug(f"Generating HTML for {len(results)} results")
for url, transcription, status, metadata, json_file, summary_file in results:
if status == "Success":
title = metadata.get('title', 'Unknown Title')
# Check if transcription is a string (which it should be now)
if isinstance(transcription, str):
# Split the transcription into metadata and actual transcription
parts = transcription.split('\n\n', 1)
if len(parts) == 2:
metadata_text, transcription_text = parts
else:
metadata_text = "Metadata not found"
transcription_text = transcription
else:
metadata_text = "Metadata format error"
transcription_text = "Transcription format error"
summary = safe_read_file(summary_file) if summary_file else "No summary available"
# FIXME - Add to other functions that generate HTML
# Format the transcription
formatted_transcription = format_transcription(transcription_text)
# Format the summary
formatted_summary = format_transcription(summary)
results_html += f"""
"""
# Save all transcriptions and summaries to files
logging.debug("Saving all transcriptions and summaries to files")
with open('all_transcriptions.json', 'w', encoding='utf-8') as f:
json.dump(all_transcriptions, f, indent=2, ensure_ascii=False)
with open('all_summaries.txt', 'w', encoding='utf-8') as f:
f.write(all_summaries)
error_summary = "\n".join(errors) if errors else "No errors occurred."
total_inputs = len(all_inputs)
return (
f"Processed {total_inputs} videos. {len(errors)} errors occurred.",
error_summary,
results_html,
'all_transcriptions.json',
'all_summaries.txt'
)
except Exception as e:
logging.error(f"Unexpected error in process_videos_with_error_handling: {str(e)}", exc_info=True)
return (
f"An unexpected error occurred: {str(e)}",
str(e),
"
Unexpected Error
" + str(e) + "
",
None,
None
)
def process_videos_wrapper(url_input, video_file, start_time, end_time, diarize, whisper_model,
custom_prompt_checkbox, custom_prompt, chunking_options_checkbox,
chunk_method, max_chunk_size, chunk_overlap, use_adaptive_chunking,
use_multi_level_chunking, chunk_language, summarize_recursively, api_name,
api_key, keywords, use_cookies, cookies, batch_size,
timestamp_option, keep_original_video, confab_checkbox):
global result
try:
logging.info("process_videos_wrapper(): process_videos_wrapper called")
# Define file paths
transcriptions_file = os.path.join('all_transcriptions.json')
summaries_file = os.path.join('all_summaries.txt')
# Delete existing files if they exist
for file_path in [transcriptions_file, summaries_file]:
try:
if os.path.exists(file_path):
os.remove(file_path)
logging.info(f"Deleted existing file: {file_path}")
except Exception as e:
logging.warning(f"Failed to delete file {file_path}: {str(e)}")
# Handle both URL input and file upload
inputs = []
if url_input:
inputs.extend([url.strip() for url in url_input.split('\n') if url.strip()])
if video_file is not None:
# Assuming video_file is a file object with a 'name' attribute
inputs.append(video_file.name)
if not inputs:
raise ValueError("No input provided. Please enter URLs or upload a video file.")
result = process_videos_with_error_handling(
inputs, start_time, end_time, diarize, whisper_model,
custom_prompt_checkbox, custom_prompt, chunking_options_checkbox,
chunk_method, max_chunk_size, chunk_overlap, use_adaptive_chunking,
use_multi_level_chunking, chunk_language, api_name,
api_key, keywords, use_cookies, cookies, batch_size,
timestamp_option, keep_original_video, summarize_recursively
)
confabulation_result = None
if confab_checkbox:
logging.info("Confabulation check enabled")
# Assuming result[1] contains the transcript and result[2] contains the summary
confabulation_result = simplified_geval(result[1], result[2], api_name, api_key)
logging.info(f"Simplified G-Eval result: {confabulation_result}")
# Ensure that result is a tuple with 5 elements
if not isinstance(result, tuple) or len(result) != 5:
raise ValueError(
f"process_videos_wrapper(): Expected 5 outputs, but got {len(result) if isinstance(result, tuple) else 1}")
# Return the confabulation result along with other outputs
return (*result, confabulation_result)
except Exception as e:
logging.error(f"process_videos_wrapper(): Error in process_videos_wrapper: {str(e)}", exc_info=True)
# Return a tuple with 6 elements in case of any error (including None for simple_geval_result)
return (
f"process_videos_wrapper(): An error occurred: {str(e)}", # progress_output
str(e), # error_output
f"
Error: {str(e)}
", # results_output
None, # download_transcription
None, # download_summary
None # simple_geval_result
)
# FIXME - remove dead args for process_url_with_metadata
@error_handler
def process_url_with_metadata(input_item, num_speakers, whisper_model, custom_prompt, offset, api_name,
api_key,
vad_filter, download_video_flag, download_audio, rolling_summarization,
detail_level, question_box, keywords, local_file_path, diarize, end_time=None,
include_timestamps=True, metadata=None, use_chunking=False,
chunk_options=None, keep_original_video=False, current_whisper_model="Blank"):
try:
logging.info(f"Starting process_url_metadata for URL: {input_item}")
# Create download path
download_path = create_download_directory("Video_Downloads")
logging.info(f"Download path created at: {download_path}")
# Initialize info_dict
info_dict = {}
# Handle URL or local file
if os.path.isfile(input_item):
video_file_path = input_item
unique_id = generate_unique_identifier(input_item)
# Extract basic info from local file
info_dict = {
'webpage_url': unique_id,
'title': os.path.basename(input_item),
'description': "Local file",
'channel_url': None,
'duration': None,
'channel': None,
'uploader': None,
'upload_date': None
}
else:
# Extract video information
with yt_dlp.YoutubeDL({'quiet': True}) as ydl:
try:
full_info = ydl.extract_info(input_item, download=False)
# Create a safe subset of info to log
safe_info = {
'title': full_info.get('title', 'No title'),
'duration': full_info.get('duration', 'Unknown duration'),
'upload_date': full_info.get('upload_date', 'Unknown upload date'),
'uploader': full_info.get('uploader', 'Unknown uploader'),
'view_count': full_info.get('view_count', 'Unknown view count')
}
logging.debug(f"Full info extracted for {input_item}: {safe_info}")
except Exception as e:
logging.error(f"Error extracting video info: {str(e)}")
return None, None, None, None, None, None
# Filter the required metadata
if full_info:
info_dict = {
'webpage_url': full_info.get('webpage_url', input_item),
'title': full_info.get('title'),
'description': full_info.get('description'),
'channel_url': full_info.get('channel_url'),
'duration': full_info.get('duration'),
'channel': full_info.get('channel'),
'uploader': full_info.get('uploader'),
'upload_date': full_info.get('upload_date')
}
logging.debug(f"Filtered info_dict: {info_dict}")
else:
logging.error("Failed to extract video information")
return None, None, None, None, None, None
# Download video/audio
logging.info("Downloading video/audio...")
video_file_path = download_video(input_item, download_path, full_info, download_video_flag,
current_whisper_model="Blank")
if not video_file_path:
logging.error(f"Failed to download video/audio from {input_item}")
return None, None, None, None, None, None
logging.info(f"Processing file: {video_file_path}")
# Perform transcription
logging.info("Starting transcription...")
audio_file_path, segments = perform_transcription(video_file_path, offset, whisper_model,
vad_filter, diarize)
if audio_file_path is None or segments is None:
logging.error("Transcription failed or segments not available.")
return None, None, None, None, None, None
logging.info(f"Transcription completed. Number of segments: {len(segments)}")
# Add metadata to segments
segments_with_metadata = {
"metadata": info_dict,
"segments": segments
}
# Save segments with metadata to JSON file
segments_json_path = os.path.splitext(audio_file_path)[0] + ".segments.json"
with open(segments_json_path, 'w') as f:
json.dump(segments_with_metadata, f, indent=2)
# Delete the .wav file after successful transcription
files_to_delete = [audio_file_path]
for file_path in files_to_delete:
if file_path and os.path.exists(file_path):
try:
os.remove(file_path)
logging.info(f"Successfully deleted file: {file_path}")
except Exception as e:
logging.warning(f"Failed to delete file {file_path}: {str(e)}")
# Delete the mp4 file after successful transcription if not keeping original audio
# Modify the file deletion logic to respect keep_original_video
if not keep_original_video:
files_to_delete = [audio_file_path, video_file_path]
for file_path in files_to_delete:
if file_path and os.path.exists(file_path):
try:
os.remove(file_path)
logging.info(f"Successfully deleted file: {file_path}")
except Exception as e:
logging.warning(f"Failed to delete file {file_path}: {str(e)}")
else:
logging.info(f"Keeping original video file: {video_file_path}")
logging.info(f"Keeping original audio file: {audio_file_path}")
# Process segments based on the timestamp option
if not include_timestamps:
segments = [{'Text': segment['Text']} for segment in segments]
logging.info(f"Segments processed for timestamp inclusion: {segments}")
# Extract text from segments
transcription_text = extract_text_from_segments(segments)
if transcription_text.startswith("Error:"):
logging.error(f"Failed to extract transcription: {transcription_text}")
return None, None, None, None, None, None
# Use transcription_text instead of segments for further processing
full_text_with_metadata = f"{json.dumps(info_dict, indent=2)}\n\n{transcription_text}"
logging.debug(f"Full text with metadata extracted: {full_text_with_metadata[:100]}...")
# Perform summarization if API is provided
summary_text = None
if api_name:
# API key resolution handled at base of function if none provided
api_key = api_key if api_key else None
logging.info(f"Starting summarization with {api_name}...")
summary_text = perform_summarization(api_name, full_text_with_metadata, custom_prompt, api_key)
if summary_text is None:
logging.error("Summarization failed.")
return None, None, None, None, None, None
logging.debug(f"Summarization completed: {summary_text[:100]}...")
# Save transcription and summary
logging.info("Saving transcription and summary...")
download_path = create_download_directory("Audio_Processing")
json_file_path, summary_file_path = save_transcription_and_summary(full_text_with_metadata,
summary_text,
download_path, info_dict)
logging.info(f"Transcription saved to: {json_file_path}")
logging.info(f"Summary saved to: {summary_file_path}")
# Prepare keywords for database
if isinstance(keywords, str):
keywords_list = [kw.strip() for kw in keywords.split(',') if kw.strip()]
elif isinstance(keywords, (list, tuple)):
keywords_list = keywords
else:
keywords_list = []
logging.info(f"Keywords prepared: {keywords_list}")
# Add to database
logging.info("Adding to database...")
add_media_to_database(info_dict['webpage_url'], info_dict, full_text_with_metadata, summary_text,
keywords_list, custom_prompt, whisper_model)
logging.info(f"Media added to database: {info_dict['webpage_url']}")
return info_dict[
'webpage_url'], full_text_with_metadata, summary_text, json_file_path, summary_file_path, info_dict
except Exception as e:
logging.error(f"Error in process_url_with_metadata: {str(e)}", exc_info=True)
return None, None, None, None, None, None
def toggle_confabulation_output(checkbox_value):
return gr.update(visible=checkbox_value)
confab_checkbox.change(
fn=toggle_confabulation_output,
inputs=[confab_checkbox],
outputs=[confabulation_output]
)
process_button.click(
fn=process_videos_wrapper,
inputs=[
url_input, video_file_input, start_time_input, end_time_input, diarize_input, whisper_model_input,
custom_prompt_checkbox, custom_prompt_input, chunking_options_checkbox,
chunk_method, max_chunk_size, chunk_overlap, use_adaptive_chunking,
use_multi_level_chunking, chunk_language, summarize_recursively, api_name_input, api_key_input,
keywords_input, use_cookies_input, cookies_input, batch_size_input,
timestamp_option, keep_original_video, confab_checkbox
],
outputs=[progress_output, error_output, results_output, download_transcription, download_summary, confabulation_output]
)