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# Audio_ingestion_tab.py
# Description: Gradio UI for ingesting audio files into the database
#
# Imports
#
# External Imports
import gradio as gr
#
# Local Imports
from App_Function_Libraries.Audio_Files import process_audio_files
from App_Function_Libraries.DB.DB_Manager import load_preset_prompts
from App_Function_Libraries.Gradio_UI.Chat_ui import update_user_prompt
from App_Function_Libraries.Gradio_UI.Gradio_Shared import whisper_models
#
#######################################################################################################################
# Functions:
def create_audio_processing_tab():
with gr.TabItem("Audio File Transcription + Summarization"):
gr.Markdown("# Transcribe & Summarize Audio Files from URLs or Local Files!")
with gr.Row():
with gr.Column():
audio_url_input = gr.Textbox(label="Audio File URL(s)", placeholder="Enter the URL(s) of the audio file(s), one per line")
audio_file_input = gr.File(label="Upload Audio File", file_types=["audio/*"])
use_cookies_input = gr.Checkbox(label="Use cookies for authenticated download", value=False)
cookies_input = gr.Textbox(
label="Audio Download 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]
)
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="""<s>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.</s>[INST] {{ .Prompt }} [/INST]
""",
lines=3,
visible=False)
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", "Custom-OpenAI-API"],
value=None,
label="API for Summarization (Optional)"
)
api_key_input = gr.Textbox(label="API Key (if required)", placeholder="Enter your API key here", type="password")
custom_keywords_input = gr.Textbox(label="Custom Keywords", placeholder="Enter custom keywords, comma-separated")
keep_original_input = gr.Checkbox(label="Keep original audio file", value=False)
chunking_options_checkbox = gr.Checkbox(label="Show Chunking Options", value=False)
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")
use_multi_level_chunking = gr.Checkbox(label="Use Multi-level Chunking")
chunk_language = gr.Dropdown(choices=['english', 'french', 'german', 'spanish'], label="Chunking Language")
chunking_options_checkbox.change(
fn=lambda x: gr.update(visible=x),
inputs=[chunking_options_checkbox],
outputs=[chunking_options_box]
)
process_audio_button = gr.Button("Process Audio File(s)")
with gr.Column():
audio_progress_output = gr.Textbox(label="Progress")
audio_transcription_output = gr.Textbox(label="Transcription")
audio_summary_output = gr.Textbox(label="Summary")
download_transcription = gr.File(label="Download All Transcriptions as JSON")
download_summary = gr.File(label="Download All Summaries as Text")
process_audio_button.click(
fn=process_audio_files,
inputs=[audio_url_input, audio_file_input, whisper_model_input, api_name_input, api_key_input,
use_cookies_input, cookies_input, keep_original_input, custom_keywords_input, custom_prompt_input,
chunk_method, max_chunk_size, chunk_overlap, use_adaptive_chunking, use_multi_level_chunking,
chunk_language, diarize_input],
outputs=[audio_progress_output, audio_transcription_output, audio_summary_output]
)
#
# End of Audio_ingestion_tab.py
####################################################################################################################### |