app.py

import gradio as gr
from typing import Any
import math

import torch
from transformers import pipeline
from diffusers import StableDiffusionPipeline
from TTS.api import TTS
import whisper

import utils
from youtubeaudioextractor import PytubeAudioExtractor
from transcriber import SpanishTranscriber, WhisperTranscriber
from textprocessor import TextProcessor
from videocreator import VideoCreator
from share_btn import community_icon_html, loading_icon_html, share_js

MAX_NUM_WORDS = 20000
MAX_CHUNK_LENGTH = 1000

spanish_transcribe_model = "juancopi81/whisper-medium-es"
languages = {"Spanish": "es", "English": "en"}

device = "cuda" if torch.cuda.is_available() else "cpu"
device_dict = {"cuda": 0, "cpu": -1}
dtype = torch.float16 if device == "cuda" else torch.float32

# Detect if code is running in Colab
is_colab = utils.is_google_colab()
colab_instruction = "" if is_colab else """
<p>You can skip the queue using Colab: 
<a href="">
<img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p>"""
device_print = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"

# Initialize components
audio_extractor = PytubeAudioExtractor()
es_transcription_pipe = pipeline(
    task="automatic-speech-recognition",
    model=spanish_transcribe_model,
    chunk_length_s=30,
    device=device_dict[device],
)
es_transcription_pipe.model.config.forced_decoder_ids = es_transcription_pipe.tokenizer.get_decoder_prompt_ids(language="es",
                                                                                                               task="transcribe")
es_audio_transcriber = SpanishTranscriber(es_transcription_pipe)

en_transcription_pipe = whisper.load_model("base")

en_audio_transcriber = WhisperTranscriber(en_transcription_pipe)

openai_model = "text-davinci-003"
text_processor = TextProcessor(openai_model)

image_model_id = "runwayml/stable-diffusion-v1-5"
image_pipeline = StableDiffusionPipeline.from_pretrained(image_model_id,
                                                         torch_dtype=dtype,
                                                         revision="fp16")

image_pipeline = image_pipeline.to(device)

es_vo_model_name = TTS.list_models()[22]
en_vo_model_name = TTS.list_models()[8]
# Init TTS
es_tts = TTS(es_vo_model_name)
en_tts = TTS(en_vo_model_name)

def datapipeline(url: str, 
                 video_language: str,
                 summary_language: str,
                 video_styles: str) -> Any:
    audio_path_file = audio_extractor.extract(url)
    print(f"Audio file created at: {audio_path_file}")
    # Select transcriber
    if video_language == "Spanish":
        audio_transcriber = es_audio_transcriber
    elif video_language == "English":
        audio_transcriber = en_audio_transcriber
    else:
        return "Language not supported"
    if summary_language == "Spanish":
        video_creator = VideoCreator(es_tts, image_pipeline)
    elif summary_language == "English":
        video_creator = VideoCreator(en_tts, image_pipeline)
    else:
        return "Language not supported"
    transcribed_text = audio_transcriber.transcribe(audio_path_file)
    print("Audio transcription ready!")
    # Get total number of words in text
    num_words_transcription = len(transcribed_text.split())

    if num_words_transcription > MAX_NUM_WORDS:
        print("to add return here")

    if num_words_transcription > MAX_CHUNK_LENGTH:
        num_chunks = math.ceil(num_words_transcription / MAX_CHUNK_LENGTH)
        num_words_per_chunk = num_words_transcription // num_chunks
        chunks = utils.splitter(num_words_per_chunk, transcribed_text)
        json_scenes = {}
        for chunk in chunks:
            if len(chunk.split()) > 50:
                max_key = max(json_scenes.keys(), default=0)
                chunk_scenes = text_processor.get_json_scenes(chunk,
                                                              summary_language)
                chunk_scenes = {k+max_key: v for k, v in chunk_scenes.items()}
                json_scenes.update(chunk_scenes)
    else:
        json_scenes = text_processor.get_json_scenes(transcribed_text,
                                                     summary_language)

    print("Scenes ready")
    video = video_creator.create_video(json_scenes, video_styles)
    print("Video at", video)
    return video, video

css = """
        a {
            color: inherit;
            text-decoration: underline;
        }
        .gradio-container {
            font-family: 'IBM Plex Sans', sans-serif;
        }
        .gr-button {
            color: white;
            border-color: #000000;
            background: #000000;
        }
        input[type='range'] {
            accent-color: #000000;
        }
        .dark input[type='range'] {
            accent-color: #dfdfdf;
        }
        .container {
            max-width: 730px;
            margin: auto;
            padding-top: 1.5rem;
        }
        #gallery {
            min-height: 22rem;
            margin-bottom: 15px;
            margin-left: auto;
            margin-right: auto;
            border-bottom-right-radius: .5rem !important;
            border-bottom-left-radius: .5rem !important;
        }
        #gallery>div>.h-full {
            min-height: 20rem;
        }
        .details:hover {
            text-decoration: underline;
        }
        .gr-button {
            white-space: nowrap;
        }
        .gr-button:focus {
            border-color: rgb(147 197 253 / var(--tw-border-opacity));
            outline: none;
            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
            --tw-border-opacity: 1;
            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
            --tw-ring-opacity: .5;
        }
        #advanced-btn {
            font-size: .7rem !important;
            line-height: 19px;
            margin-top: 12px;
            margin-bottom: 12px;
            padding: 2px 8px;
            border-radius: 14px !important;
        }
        #advanced-options {
            margin-bottom: 20px;
        }
        .footer {
            margin-bottom: 45px;
            margin-top: 35px;
            text-align: center;
            border-bottom: 1px solid #e5e5e5;
        }
        .footer>p {
            font-size: .8rem;
            display: inline-block;
            padding: 0 10px;
            transform: translateY(10px);
            background: white;
        }
        .dark .footer {
            border-color: #303030;
        }
        .dark .footer>p {
            background: #0b0f19;
        }
        .acknowledgments h4{
            margin: 1.25em 0 .25em 0;
            font-weight: bold;
            font-size: 115%;
        }
        #container-advanced-btns{
            display: flex;
            flex-wrap: wrap;
            justify-content: space-between;
            align-items: center;
        }
        .animate-spin {
            animation: spin 1s linear infinite;
        }
        @keyframes spin {
            from {
                transform: rotate(0deg);
            }
            to {
                transform: rotate(360deg);
            }
        }
        #share-btn-container {
            display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
        }
        #share-btn {
            all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
        }
        #share-btn * {
            all: unset;
        }
        .gr-form{
            flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
        }
        #prompt-container{
            gap: 0;
        }
        #generated_id{
            min-height: 700px
        }
        #setting_id{
          margin-bottom: 12px;
          text-align: center;
          font-weight: 900;
        }
"""
block = gr.Blocks(css=css)

with block as demo:
    gr.HTML(
        f"""
            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
              <div
                style="
                  display: inline-flex;
                  align-items: center;
                  gap: 0.8rem;
                  font-size: 1.75rem;
                "
              >
                <h1 style="font-weight: 900; margin-bottom: 7px;">
                  YouTube to Video Summary
                </h1>
              </div>
              <p style="margin-bottom: 10px; font-size: 94%">
                Enter the URL of a YouTube video (in Spanish or English) and you'll recieve a video with an illustraded summary (in Spanish or English, it works as translator).
                It works for audio books, history lessons, etc. Try it out with a short video (less than 4 minutes). SEE SOME EXAMPLES AT THE BOTTOM.
              </p>
              <p style="margin-bottom: 10px; font-size: 94%">
                Running on <b>{device_print}</b>
              </p>
              </p>
                <p style="margin-bottom: 10px; font-size: 94%">
                You can buy me a coffee to support this space: 
                <span style="display: flex;align-items: center;justify-content: center;height: 30px;">
                <a href="https://www.buymeacoffee.com/juancopi81j">
                <img src="https://badgen.net/badge/icon/Buy%20Me%20A%20Coffee?icon=buymeacoffee&label" alt="Buy me a coffee"></a>.
                </span>
              </p>
            </div>
        """
    )
    with gr.Group():
        with gr.Box():
            with gr.Row(elem_id="setting_id").style(mobile_collapse=False, equal_height=True):
              gr.HTML("<h1>Settings</h1>")
            with gr.Row():
              with gr.Column():
                video_language = gr.Radio(choices=["Spanish", "English"],
                                          label="Language of your input video:",
                                          value="Spanish")
              with gr.Column():
                summary_language = gr.Radio(choices=["Spanish", "English"],
                                            label="Language of your output video:",
                                            value="Spanish")
            with gr.Row():
              video_styles = gr.Textbox(label="(OPTIONAL) Enter the styles for your ouput video",
                                        value="",
                                        placeholder="illustration, highly detailed, digital painting, concept art, matte, art by wlop and artgerm and greg rutkowski and alphonse mucha, masterpiece")
    with gr.Group():
        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):

                url = gr.Textbox(
                    label="Enter the URL of the YouTubeVideo",
                    show_label=False,
                    max_lines=1,
                    placeholder="YouTube URL",
                    elem_id="prompt-in"
                ).style(
                    border=(True, False, True, True),
                    rounded=(True, False, False, True),
                    container=False,
                )
                btn = gr.Button("Run").style(
                    margin=False,
                    rounded=(False, True, True, False),
                )
        video_output = gr.Video(elem_id="output-video")
        file_output = gr.File()
        
        btn.click(datapipeline,
                  inputs=[url,
                          video_language,
                          summary_language,
                          video_styles],
                  outputs=[video_output, file_output])

    with gr.Group(elem_id="share-btn-container"):
            community_icon = gr.HTML(community_icon_html)
            loading_icon = gr.HTML(loading_icon_html)
            share_button = gr.Button("Share to community", elem_id="share-btn")
    share_button.click(None, [], [], _js=share_js)
    gr.Examples(
        examples=[["https://www.youtube.com/watch?v=c0i5016pB2Y", "English", "Spanish", "oil on painting"],
                  ["https://www.youtube.com/watch?v=Hk5evm1NgzA", "Spanish", "English", "trending on artstation pixiv makoto shinkai"],
                  ["https://www.youtube.com/watch?v=sRmmQBBln9Q", "Spanish", "Spanish", "Hyper real, 4k"],
                  ["https://www.youtube.com/watch?v=qz4Wc48KITA", "Spanish", "English", "detailed art by kay nielsen and walter crane, illustration style, watercolor"]],
        inputs=[url, video_language, summary_language, video_styles]
    )
    gr.HTML(
            """
                <div class="footer">
                    <p>This demos is part of the Whisper Sprint (Dec. 2022).</a>
                    </p>
                </div>
           """
        )
    gr.Markdown('''
      [![Twitter Follow](https://img.shields.io/twitter/follow/juancopi81?style=social)](https://twitter.com/juancopi81)
      ![visitors](https://visitor-badge.glitch.me/badge?page_id=Juancopi81.yt-illustraded-summary)
    ''')

if not is_colab:
    demo.queue(concurrency_count=1)
demo.launch(debug=is_colab, share=is_colab)