app.py

import gradio as gr
import os

from youtube_transcript_api import YouTubeTranscriptApi

import utils

from openai import OpenAI
from groq import Groq

from dotenv import load_dotenv
load_dotenv()
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

#import importlib
#importlib.reload(utils)

def get_llm_client_and_model(llm_model):
    if llm_model == "llama3-8b":
        llm_client = Groq(api_key=GROQ_API_KEY)
        llm_model = 'llama3-8b-8192'

    elif llm_model == "gpt-4o-mini":
        llm_client = OpenAI(api_key=OPENAI_API_KEY)
        llm_model = 'gpt-4o-mini-2024-07-18'

    return llm_client, llm_model


def gradio_process_video(video_id,
                         model_format_transcript, model_toc,
                         chunk_size_format_transcript, chunk_size_toc,
                         progress=gr.Progress()):
    if video_id in ["ErnWZxJovaM"]:
        chapters = utils.load_json_chapters(video_id)

    else:

        transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=["en"])

        chunk_size_format_transcript = int(chunk_size_format_transcript)

        llm_client_format_transcript, llm_model_format_transcript = \
            get_llm_client_and_model(model_format_transcript)

        paragraphs, nb_input_tokens, nb_output_tokens, price = \
            utils.transcript_to_paragraphs(transcript, \
                                           llm_client_format_transcript, llm_model_format_transcript, \
                                           chunk_size=chunk_size_format_transcript, progress=progress)

        paragraphs = utils.add_timestamps_to_paragraphs(transcript, paragraphs, num_words=50)

        chunk_size_toc = int(chunk_size_toc)

        llm_client_get_toc, llm_model_get_toc = \
            get_llm_client_and_model(model_toc)

        json_toc, nb_input_tokens, nb_output_tokens, price = \
            utils.paragraphs_to_toc(paragraphs, \
                                    llm_client_get_toc, llm_model_get_toc, \
                                    chunk_size=chunk_size_toc)

        chapters = utils.get_chapters(paragraphs, json_toc)

    output_html = utils.get_result_as_html(chapters, video_id)

    return {output_processing: str(output_html),
            gv_output: output_html}


def gradio_process_video(video_id,
                         model_format_transcript, model_toc,
                         chunk_size_format_transcript, chunk_size_toc,
                         progress=gr.Progress()):
    if video_id in ["ErnWZxJovaM"]:
        chapters = utils.load_json_chapters(video_id)

    else:

        transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=["en"])

        chunk_size_format_transcript = int(chunk_size_format_transcript)

        llm_client_format_transcript, llm_model_format_transcript = \
            get_llm_client_and_model(model_format_transcript)

        paragraphs, nb_input_tokens, nb_output_tokens, price = \
            utils.transcript_to_paragraphs(transcript, \
                                           llm_client_format_transcript, llm_model_format_transcript, \
                                           chunk_size=chunk_size_format_transcript, progress=progress)

        paragraphs = utils.add_timestamps_to_paragraphs(transcript, paragraphs, num_words=50)

        chunk_size_toc = int(chunk_size_toc)

        llm_client_get_toc, llm_model_get_toc = \
            get_llm_client_and_model(model_toc)

        json_toc, nb_input_tokens, nb_output_tokens, price = \
            utils.paragraphs_to_toc(paragraphs, \
                                    llm_client_get_toc, llm_model_get_toc, \
                                    chunk_size=chunk_size_toc)

        chapters = utils.get_chapters(paragraphs, json_toc)

    output_html = utils.get_result_as_html(chapters, video_id)

    return {output_processing: str(output_html),
            gv_output: output_html}


# %%
css = """
.content {
    padding: 20px;
    max-width: 800px;
    margin: 0 auto;
    background-color: #ffffff;
    box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
    border-radius: 8px;
}
"""

example_video_id = "ErnWZxJovaM"
example_chapters = utils.load_json_chapters(example_video_id)
example_output_html = utils.get_result_as_html(example_chapters, example_video_id)

with (gr.Blocks(css=css) as app):
    gr.HTML("<div align='center'><h1 class='header'>Demo: Automatic video chaptering with LLMs and TF-IDF</h1></div>")
    gr.HTML("<div align='center'><h3 class='header'>From raw transcript to structured document</h3></div>")
    gr.HTML("<hr>")
    gr.Markdown("""This demo relies on 
                - Groq's Llama 3 8B for transcript preprocessing
                - OpenAI's GPT-4o-mini for chaptering. Note: Using GPT-4o-mini for transcript preprocessing will improve results, but takes longer (around 2/3 minutes for a one-hour video)

                The following YouTube video ID are already preprocessed (copy and paste ID in box below): 

                - `ErnWZxJovaM`: [MIT course](https://www.youtube.com/watch?v=ErnWZxJovaM)
                - `EuC1GWhQdKE`: [Anthropic](https://www.youtube.com/watch?v=EuC1GWhQdKE)

                Check the [Medium article]() for more details"""
                )

    gv_transcript = gr.State()

    video_id_input = gr.Textbox(label="Enter YouTube Video ID", value="EuC1GWhQdKE")

    with gr.Accordion("Set parameters", open=False):
        with gr.Row():
            with gr.Column(scale=1):
                model_format_transcript = gr.Dropdown(
                    [("LLama 3 8B (Groq)", "llama3-8b"), ("GPT-4o-mini (OpenAI)", "gpt-4o-mini")],
                    label="Transcript preprocessing", value="llama3-8b", interactive=True)
                chunk_size_format_transcript = gr.Textbox(label="Preprocessing chunk size", value=2000)
            with gr.Column(scale=1):
                model_toc = gr.Dropdown([("LLama 3 8B (Groq)", "llama3-8b"), ("GPT-4o-mini (OpenAI)", "gpt-4o-mini")],
                                        label="Chaptering", value="gpt-4o-mini", interactive=True)
                chunk_size_toc = gr.Textbox(label="Chaptering chunk size", value=30)
            with gr.Column(scale=1):
                api_key_openai = gr.Textbox(label="OpenAI API Key", value="xxx")
                api_key_groq = gr.Textbox(label="Groq API Key", value="xxx")

    processing_button = gr.Button("Process transcript")

    gv_output = gr.State()

    gr.HTML("<hr>")

    output_processing = gr.HTML(label="Output processing", value=example_output_html)

    processing_button.click(gradio_process_video,
                            inputs=[video_id_input,
                                    model_format_transcript, model_toc,
                                    chunk_size_format_transcript, chunk_size_toc],
                            outputs=[output_processing, gv_output])

    # gr.HTML(result_as_html)

app.launch(debug=True, width="100%")