app.py

# Imports
import gradio as gr
import whisper
from pytube import YouTube
from transformers import pipeline, T5Tokenizer, T5ForConditionalGeneration
from wordcloud import WordCloud

class GradioInference:
    def __init__(self):
        
        # OpenAI's Whisper model sizes
        self.sizes = list(whisper._MODELS.keys())

        # Whisper's available languages for ASR
        self.langs = ["none"] + sorted(list(whisper.tokenizer.LANGUAGES.values()))
        
        # Default size
        self.current_size = "base"
        
        # Default model size
        self.loaded_model = whisper.load_model(self.current_size)
        
        # Initialize Pytube Object
        self.yt = None

        # Initialize summary model
        self.summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

        # Initialize VoiceLabT5 model and tokenizer
        self.keyword_model = T5ForConditionalGeneration.from_pretrained(
            "Voicelab/vlt5-base-keywords"
        )
        self.keyword_tokenizer = T5Tokenizer.from_pretrained(
            "Voicelab/vlt5-base-keywords"
        )

        # Sentiment Classifier
        self.classifier = pipeline("text-classification")

    def __call__(self, link, lang, size):
        """
        Call the Gradio Inference python class.
        This class gets access to a YouTube video using python's library Pytube and downloads its audio.
        Then it uses the Whisper model to perform Automatic Speech Recognition (i.e Speech-to-Text).
        Once the function has the transcription of the video it proccess it to obtain:
            - Summary: using Facebook's BART transformer.
            - KeyWords: using VoiceLabT5 keyword extractor.
            - Sentiment Analysis: using Hugging Face's default sentiment classifier
            - WordCloud: using the wordcloud python library.
        """
        if self.yt is None:
            self.yt = YouTube(link)
        
        # Pytube library to access to YouTube audio stream
        path = self.yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4")

        if lang == "none":
            lang = None

        if size != self.current_size:
            self.loaded_model = whisper.load_model(size)
            self.current_size = size

        # Transcribe the audio extracted from pytube
        results = self.loaded_model.transcribe(path, language=lang)

        # Perform summarization on the transcription
        transcription_summary = self.summarizer(
            results["text"], max_length=512, min_length=30, do_sample=False
        )

        # Extract keywords using VoiceLabT5
        task_prefix = "Keywords: "
        input_sequence = task_prefix + results["text"]
        input_ids = self.keyword_tokenizer(
            input_sequence, return_tensors="pt", truncation=False
        ).input_ids
        output = self.keyword_model.generate(
            input_ids, no_repeat_ngram_size=3, num_beams=4
        )
        predicted = self.keyword_tokenizer.decode(output[0], skip_special_tokens=True)
        keywords = [x.strip() for x in predicted.split(",") if x.strip()]

        # Sentiment label
        label = self.classifier(results["text"])[0]["label"]
        
        # Generate WordCloud object
        wordcloud = WordCloud().generate(results["text"])

        # WordCloud image to display
        wordcloud_image = wordcloud.to_image()

        return (
            results["text"],
            transcription_summary[0]["summary_text"],
            keywords,
            label,
            wordcloud_image,
        )

    def populate_metadata(self, link):
        """
        Access to the YouTube video title and thumbnail image to further display it
        params:
        - link: a YouTube URL.
        """
        self.yt = YouTube(link)
        return self.yt.thumbnail_url, self.yt.title

    def from_audio_input(self, lang, size, audio_file):
        """
        Call the Gradio Inference python class.
        Uses it directly the Whisper model to perform Automatic Speech Recognition (i.e Speech-to-Text).
        Once the function has the transcription of the video it proccess it to obtain:
            - Summary: using Facebook's BART transformer.
            - KeyWords: using VoiceLabT5 keyword extractor.
            - Sentiment Analysis: using Hugging Face's default sentiment classifier
            - WordCloud: using the wordcloud python library.
        """
        if lang == "none":
            lang = None

        if size != self.current_size:
            self.loaded_model = whisper.load_model(size)
            self.current_size = size

        results = self.loaded_model.transcribe(audio_file, language=lang)

        # Perform summarization on the transcription
        transcription_summary = self.summarizer(
            results["text"], max_length=512, min_length=30, do_sample=False
        )

        # Extract keywords using VoiceLabT5
        task_prefix = "Keywords: "
        input_sequence = task_prefix + results["text"]
        input_ids = self.keyword_tokenizer(
            input_sequence, return_tensors="pt", truncation=False
        ).input_ids
        output = self.keyword_model.generate(
            input_ids, no_repeat_ngram_size=3, num_beams=4
        )
        predicted = self.keyword_tokenizer.decode(output[0], skip_special_tokens=True)
        keywords = [x.strip() for x in predicted.split(",") if x.strip()]

        # Sentiment label    
        label = self.classifier(results["text"])[0]["label"]
        
        # WordCloud object
        wordcloud = WordCloud().generate(
            results["text"]
        )
        wordcloud_image = wordcloud.to_image()

        return (
            results["text"],
            transcription_summary[0]["summary_text"],
            keywords,
            label,
            wordcloud_image,
        )


gio = GradioInference()
title = "Youtube Insights"
description = "Your AI-powered video analytics tool"

block = gr.Blocks()
with block as demo:
    gr.HTML(
        """
        <div style="text-align: center; max-width: 500px; margin: 0 auto;">
          <div>
            <h1>Youtube <span style="color: red;">Insights</span> 📹</h1>
          </div>
          <p style="margin-bottom: 10px; font-size: 94%">
            Your AI-powered video analytics tool
          </p>
        </div>
        """
    )
    with gr.Group():
        with gr.Tab("From YouTube"):
            with gr.Box():
                with gr.Row().style(equal_height=True):
                    size = gr.Dropdown(
                        label="Model Size", choices=gio.sizes, value="base"
                    )
                    lang = gr.Dropdown(
                        label="Language (Optional)", choices=gio.langs, value="none"
                    )
                link = gr.Textbox(
                    label="YouTube Link", placeholder="Enter YouTube link..."
                )
                title = gr.Label(label="Video Title")
                with gr.Row().style(equal_height=True):
                    img = gr.Image(label="Thumbnail")
                    text = gr.Textbox(
                        label="Transcription",
                        placeholder="Transcription Output...",
                        lines=10,
                    ).style(show_copy_button=True, container=True)
                with gr.Row().style(equal_height=True):
                    summary = gr.Textbox(
                        label="Summary", placeholder="Summary Output...", lines=5
                    ).style(show_copy_button=True, container=True)
                    keywords = gr.Textbox(
                        label="Keywords", placeholder="Keywords Output...", lines=5
                    ).style(show_copy_button=True, container=True)
                    label = gr.Label(label="Sentiment Analysis")
                    wordcloud_image = gr.Image()
                with gr.Row().style(equal_height=True):
                    clear = gr.ClearButton(
                        [link, title, img, text, summary, keywords, label, wordcloud_image], scale=1
                    )
                    btn = gr.Button("Get video insights", variant="primary", scale=1)
                btn.click(
                    gio,
                    inputs=[link, lang, size],
                    outputs=[text, summary, keywords, label, wordcloud_image],
                )
                if link:
                    link.change(gio.populate_metadata, inputs=[link], outputs=[img, title])

        with gr.Tab("From Audio file"):
            with gr.Box():
                with gr.Row().style(equal_height=True):
                    size = gr.Dropdown(
                        label="Model Size", choices=gio.sizes, value="base"
                    )
                    lang = gr.Dropdown(
                        label="Language (Optional)", choices=gio.langs, value="none"
                    )
                audio_file = gr.Audio(type="filepath")
                with gr.Row().style(equal_height=True):
                    text = gr.Textbox(
                        label="Transcription",
                        placeholder="Transcription Output...",
                        lines=10,
                    ).style(show_copy_button=True, container=False)
                with gr.Row().style(equal_height=True):
                    summary = gr.Textbox(
                        label="Summary", placeholder="Summary Output", lines=5
                    )
                    keywords = gr.Textbox(
                        label="Keywords", placeholder="Keywords Output", lines=5
                    )
                    label = gr.Label(label="Sentiment Analysis")
                    wordcloud_image = gr.Image()
                with gr.Row().style(equal_height=True):
                    clear = gr.ClearButton([audio_file,text, summary, keywords, label, wordcloud_image], scale=1)
                    btn = gr.Button(
                        "Get video insights", variant="primary", scale=1
                    )
                btn.click(
                    gio.from_audio_input,
                    inputs=[lang, size, audio_file],
                    outputs=[text, summary, keywords, label, wordcloud_image],
                )


with block:
    gr.Markdown("### Video Examples")
    gr.Examples(["https://www.youtube.com/shorts/xDNzz8yAH7I"], inputs=link)

    gr.Markdown("About the app:")

    with gr.Accordion("What is YouTube Insights?", open=False):
        gr.Markdown(
            "YouTube Insights is a tool developed with academic purposes only, that creates summaries, keywords and sentiments analysis based on YouTube videos or user audio files."
        )

    with gr.Accordion("How does it work?", open=False):
        gr.Markdown(
            "Works by using OpenAI's Whisper, BART for summarization and VoiceLabT5 for Keyword Extraction."
        )

    gr.HTML(
        """
        <div style="text-align: center; max-width: 500px; margin: 0 auto;">
          <p style="margin-bottom: 10px; font-size: 96%">
            2023 Master in Big Data & Data Science - Universidad Complutense de Madrid
          </p>
        </div>
        """
    )

demo.launch()