YouTube sends an annual reminder to users about its Terms of Service, Community Guidelines, and Google's Privacy Policy. The email emphasizes the impo

YouTube is sending annual reminder of its Terms of Service, Community Guidelines and Privacy Policies, read the complete email - Times of India

Pakistan faced an internet outage on Sunday evening as several social media apps registered red graphs on Downdetector, an outage monitoring website. The affected apps included YouTube, Instagram, Facebook, Twitter, PTCL and more.

Internet Outage In Pakistan: Users Complain As YouTube, Instagram, Facebook, Twitter Apps Face Disruption

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le pape du crudivorisme» récolte près de 175 000 euros

YouTube is an American online video sharing and social media platform headquartered in San Bruno, California. It is currently owned by Google, and is the second most visited website, after Google Search.

YouTube is a team-oriented, creative workplace where every single employee has a voice in the choices we make and the features we implement. We work together in small teams to design, develop, and roll out key features and products in very short time frames. Which means something you write today could be seen by millions of viewers tomorrow. And even though we’re a part of Google and enjoy all of the corporate perks, it still feels like you’re working at a startup. YouTube is headquartered in San Bruno, California, 12 miles south of San Francisco. We also have many job openings in Mountain View, and across the globe

YouTube

How to analyze comment sentiment and react automatically using AI Today, we're digging into a crucial topic for anyone who’s creating content for YouTube: How to analyze and respond to video comments quickly, using the sentiment in those comments. This tutorial is also handy for anyone who's running a YouTube channel and wants to grow engagement. Who cares about comments? We all know that the comments section on YouTube is where real magic happens. That’s where you have a chance to engage with your audience, hear their honest opinions, and build loyalty over time. Yes, nice comments are awesome. But the not-so-nice comments are important, too. they’re the ones you need to respond to the fastest. And An ideal system would work something like the one below. If you’re serious about growing your channel, you’ll want a deeper understanding of your audience. Analyzing comment sentiment gives a good gauge of the overall mood of your viewers—which helps you tailor your content. Truly, this could be a game-changer. Let's jump right in. Part 1: The tools for the job For this project, we’re using a platform called MindsDB—the platform for building custom AI. It basically connects real-time data—like tons of YouTube comments— to . It lets companies prototype and deploy AI-powered enterprise solutions quickly and at scale. AI models In this case, it acts as an orchestration layer where we bring everything together, using AI to analyze the sentiment in the comments. Today, we’ll be using models from OpenAI, which makes ChatGPT. Then we’ll set up some MindsDB automation and alerts—those will come from Slack—to help with instant responses. Here's what the workflow will look like in the end. Part 2: How to get everything set up Step 1. Connect YouTube to MindsDB To connect your YouTube account to MindsDB and be able to set up a chatbot, you need to first generate OAuth2 credentials in the Google Cloud Console and use it to connect YouTube to MindsDB. Find detailed instructions in our documentation . here Let’s name the connection mindsdb_youtube_oauth, which is referred to in the following tutorial. CREATE DATABASE mindsdb_youtube_oauth
WITH ENGINE = 'youtube',
PARAMETERS = {
    "credentials_file": "/path-to-creds-file/credentials.json"
    -- alternatively, use the credentials_url parameter
}; Once you connect YouTube to MindsDB successfully using the above statement, the mindsdb_youtube_oauth connection comes with the following tables: The comments table stores all data related to the comments left by the viewers, including when the comment was added, who added the comment, and the comment’s content. The videos table stores all data related to the videos, including title, description, and transcript. Next, we'll use OpenAI models to analyze the sentiment of the comments and generate replies. Step 2. Use an OpenAI model to analyze the sentiment of YouTube comments Before you can deploy OpenAI models in MindsDB, you should create an engine, providing an OpenAI API key as below. CREATE ML_ENGINE openai_engine
FROM openai
USING
    api_key = 'sk-xxx'; Now you can plug in any of OpenAI’s models into MindsDB using the CREATE MODEL statement and this engine. Let’s create a model that analyzes the sentiment of the comments. CREATE MODEL yt_sentiment_classifier
PREDICT sentiment
USING
    engine = 'openai_engine',
    model_name = 'gpt-4',
    prompt_template = 'describe the sentiment of the comments
                        strictly as "positive", "spam", or "negative".
                        "I love this video":positive
                        "It is not a helpful content":negative
                        "{{comment}}.":'; This model takes the {{comment}} value (from the comments table provided by the mindsdb_youtube_oauth connection created in Step 1) as input and gives the sentiment value as output. Join the comments table (from mindsdb_youtube_oauth) with the model to analyze the sentiment of the comments. Provide the channel_id value in the WHERE clause. SELECT c.comment, m.sentiment
FROM mindsdb_youtube_oauth.comments AS c
JOIN yt_sentiment_classifier AS m
WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"; Here is sample output: comment sentiment This video explains the subject so well. positive Subscribe to this channel asap. spam It didn’t help me understand the subject. negative Step 3. Use an OpenAI model to generate replies to YouTube comments Create a model to generate replies to comments. Here the model will utilize the comment content (from the comments table) and transcript of the video (from the videos table) to come up with a reply. CREATE MODEL yt_reply_model
PREDICT reply
USING
    engine = 'openai_engine',
    model_name = 'gpt-4',
    prompt_template = 'briefly respond to the youtube comment "{{comment}}";
                       as a context, use the video transcript "{{transcript}}"'; This model takes the {{comment}} value (from the comments table) and the {{transcript}} value (from the videos table) as input and gives the reply value as output. Join the comments and videos tables on their common column being the video_id column. Then, join it with the sentiment classifier model and the reply generator model. This is how you can accomplish the chaining of models in MindsDB. (Before executing the below command, add at least one new comment to the channel, so that it can fulfill the LAST keyword condition.) SELECT c.comment AS comment,
       m1.sentiment AS sentiment,
       m2.reply AS reply
FROM mindsdb_youtube_oauth.comments c
LEFT JOIN mindsdb_youtube_oauth.videos v
ON c.video_id = v.video_id
JOIN yt_sentiment_classifier AS m1
JOIN yt_reply_model AS m2
WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
AND v.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
AND c.published_at > LAST; Please note that for long videos the transcript will be long and that may result in the OpenAI token limit being exceeded. Alternatively, to pass long transcript values in the prompt to the model, you can use the feature of MindsDB. RAG Here is a sample output: comment sentiment reply This video explains the subject so well. positive Thank you for your comment. We’re glad you enjoyed the video. It didn’t help me understand the subject. negative Thank you for your comment. Please let us know your feedback on what could be improved. The above query joins the comments and videos tables on their common column, which is the video_id column. The reason for joining these tables is to provide input to both the models—the sentiment classifier model requires the comment column from the comments table, and the reply generator model requires the transcript column from the videos table. Next, the two models are joined using the custom JOIN statement that does not require any ON clause condition because each model is provided with the required input from the data tables. You can select the predictions made by the models as shown in the SELECT clause. Finally, we define the channel_id value in the WHERE clause. The condition that uses the LAST keyword on the published_at column enables you to fetch only the recently added comments. Now that the sentiment of comments is analyzed and replies are generated, let’s add some automation. Step 4. Post replies to positive comments This is the first component of the job that we’ll create in Step 7. Here is how you can insert replies to recent comments whose sentiment has been classified as positive: (Before executing the below command, add at least one new (positive) comment to the channel, so that it can fulfill the LAST keyword condition.) INSERT INTO mindsdb_youtube_oauth.comments (comment_id, reply)
  SELECT c.comment_id AS comment_id,
         m2.reply AS reply
  FROM mindsdb_youtube_oauth.comments c
  LEFT JOIN mindsdb_youtube_oauth.videos v
  ON c.video_id = v.video_id
  JOIN yt_sentiment_classifier AS m1
  JOIN yt_reply_model AS m2
  WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
  AND v.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
  AND c.published_at > LAST
  AND m1.sentiment = 'positive'; The LAST keyword ensures that only the newly added comments are returned by the query. Step 5. Notify about spam comments This is the second component of the job that we’ll create in Step 7. Let’s automate notifications to Slack about spam comments. A notification will include all details about the comment, its sentiment (predicted by the model in Step 2), and a sample reply (generated by the model in Step 3). Set up your Slack channel and connect it to MindsDB following . this instruction CREATE DATABASE yt_slack
WITH
  ENGINE = 'slack',
  PARAMETERS = {
      "token": "xoxb-xxx"
    }; Test the connection by inserting a message into the Slack channel. INSERT INTO yt_slack.channels (channel, text)
VALUES("channel-name", "Test message."); Here is how you can send notifications to Slack about recent comments whose sentiment has been classified as spam: (Before executing the below command, add at least one new (spam) comment to the channel, so that it can fulfill the LAST keyword condition.) INSERT INTO yt_slack.channels (channel, text)
    SELECT "channel-name" AS channel,
           concat('Video: https://www.youtube.com/watch?v=', c.video_id, chr(10),
                  'Spam Comment: https://www.youtube.com/watch?v=', c.video_id, 
                        '&lc=', c.comment_id, chr(10),
                  'Author: ', c.display_name, chr(10),
                  'Comment: ', c.comment) AS text
    FROM mindsdb_youtube_oauth.comments c
    LEFT JOIN mindsdb_youtube_oauth.videos v
    ON c.video_id = v.video_id
    JOIN yt_sentiment_classifier AS m
    WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
    AND v.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
    AND c.published_at > LAST
    AND m.sentiment = 'spam'; The LAST keyword ensures that only the newly added comments are returned by the query. Step 6. Notify about negative comments This is the third component of the job that we’ll create in Step 7. Now, what about comments with negative sentiment? As these require analysis and thoughtful answers, let’s automate notifications to Slack about negative comments. A notification will include all details about the comment, its sentiment (predicted by the model in Step 2), and a sample reply (generated by the model in Step 3). Set up your Slack channel and connect it to MindsDB following . this instruction CREATE DATABASE yt_slack
WITH
  ENGINE = 'slack',
  PARAMETERS = {
      "token": "xoxb-xxx"
    }; Test the connection by inserting a message into the Slack channel. INSERT INTO yt_slack.channels (channel, text)

VALUES("channel-name", "Test message."); Here is how you can send notifications to Slack about recent comments whose sentiment has been classified as negative: (Before executing the below command, add at least one new (negative) comment to the channel, so that it can fulfill the LAST keyword condition.) INSERT INTO yt_slack.channels (channel, text)
    SELECT "channel-name" AS channel,
           concat('Video: https://www.youtube.com/watch?v=', c.video_id, chr(10),
                  'Negative Comment: https://www.youtube.com/watch?v=', c.video_id, 
                        '&lc=', c.comment_id, chr(10),
                  'Author: ', c.display_name, chr(10),
                  'Comment: ', c.comment, chr(10),
                  'Sample reply: ', m2.reply) AS text
    FROM mindsdb_youtube_oauth.comments c
    LEFT JOIN mindsdb_youtube_oauth.videos v
    ON c.video_id = v.video_id
    JOIN yt_sentiment_classifier AS m
    JOIN yt_reply_model AS m2
    WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
    AND v.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
    AND c.published_at > LAST
    AND m.sentiment = 'negative'; The LAST keyword ensures that only the newly added comments are returned by the query. Step 7. Automate replies to positive comments and notifications about negative and spam comments Let’s create a job that will reply to recent positive comments, send notifications about recent negative comments with draft responses, and send notifications about recent spam comments. To do that, we need to connect a database to MindsDB in order to save the recent comments into a table. (The reason for doing so is that every run of a query that includes the condition with the LAST keyword returns only the newly added comments, which haven’t been returned in the previous run of such a query. To be able to do different actions for positive and negative comments, we need to save the output of the query with LAST. To save a batch of recently added comments returned by the query with LAST, we need a database table. Learn more about the .) LAST keyword here You can connect a database to MindsDB using the CREATE DATABASE statement and providing specific parameters. Choose one of the on how to connect it to MindsDB. supported databases and follow the instructions Here, we connect a PostgreSQL database as below: CREATE DATABASE psql_datasource
WITH ENGINE = 'postgres',
PARAMETERS = {
    "host": "host-name-or-ip-address",
    "port": 5432,
    "database": "database_name",
    "schema": "schema_name",
    "user": "postgres",
    "password": "password"
}; To create a table and insert into it the output of the query with LAST, use this command: CREATE OR REPLACE TABLE psql_datasource.recent_comments (
      SELECT *
      FROM mindsdb_youtube_oauth.comments c
      LEFT JOIN mindsdb_youtube_oauth.videos v
      ON c.video_id = v.video_id
      WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
      AND v.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
      AND c.published_at > LAST
); Now, it’s time to create a job that automates steps 4, 5, and 6. CREATE JOB youtube_chatbot (

  -- save the recently added comments into a table

  CREATE OR REPLACE TABLE psql_datasource.recent_comments (
    SELECT *
    FROM mindsdb_youtube_oauth.comments c
    LEFT JOIN mindsdb_youtube_oauth.videos v
    ON c.video_id = v.video_id
    WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
    AND v.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
    AND c.published_at > LAST
  );

  -- automate replies for positive comments (from Step 4)

  INSERT INTO mindsdb_youtube_oauth.comments (comment_id, reply)
    SELECT c.comment_id AS comment_id,
           m2.reply AS reply
    FROM psql_datasource.recent_comments AS c
    JOIN yt_sentiment_classifier AS m
    JOIN yt_reply_model AS m2
    WHERE m.sentiment = 'positive';

   -- automate notifications about spam comments (from Step 5)

   INSERT INTO yt_slack.channels (channel, text)
     SELECT "channel-name" AS channel,
            concat('Video: https://www.youtube.com/watch?v=', c.video_id, chr(10),
                   'Spam Comment: https://www.youtube.com/watch?v=', c.video_id, 
                         '&lc=', c.comment_id, chr(10),
                   'Author: ', c.display_name, chr(10),
                   'Comment: ', c.comment) AS text
     FROM psql_datasource.recent_comments AS c
     JOIN yt_sentiment_classifier AS m
     WHERE m.sentiment = 'spam';

  -- automate notifications about negative comments (from Step 6)

  INSERT INTO yt_slack.channels (channel, text)
    SELECT "channel-name" AS channel,
           concat('Video: https://www.youtube.com/watch?v=', c.video_id, chr(10),
                  'Negative Comment: https://www.youtube.com/watch?v=', c.video_id, 
                        '&lc=', c.comment_id, chr(10),
                  'Author: ', c.display_name, chr(10),
                  'Comment: ', c.comment, chr(10),
                  'Sample reply: ', m2.reply) AS text
    FROM psql_datasource.recent_comments AS c
    JOIN yt_sentiment_classifier AS m
    JOIN yt_reply_model AS m2
    WHERE m.sentiment = 'negative';
)
EVERY 10 minutes; This job (re)creates the recent_comments table and stores only the recently added comments in it. This table is joined with the models to reply to only positive comments. Next, this table is joined with the sentiment classifier model to send notifications about spam comments. And finally, this table is again joined with both models to send notifications about negative comments with draft responses. Please note that for large workloads of comments data, it may be required to increase the . YouTube quota What a custom YouTube chatbot means With the above steps, you can connect YouTube to MindsDB to access your channel’s comments, analyze them with the help of AI, and automate replies to the recently added comments (or send notifications about the recently added comments). In this tutorial, we've shown how to set up a chatbot for a YouTube channel defined in the WHERE clause while querying the comments and videos table. You can customize this chatbot to your use case, utilizing other integrations and features provided by MindsDB. Why analyzing sentiment is so important Analyzing to determine whether it’s positive or negative is gold when it comes to understanding your audience's emotions. language in comments Believe it or not—negative comments, while not fun, are not the enemy. They're an opportunity. They give you valuable feedback that helps you grow as a creator or as a brand. Some of the most dedicated viewers start as critics. When you respond quickly and gracefully to their nastiest comments (which shows that you value their feedback), you really can turn critics into loyal fans. Remember, it's not about proving anyone wrong; it's about responding fast (in real-time, which is possible with MindsDB) and building a connection. Also published . here

AI-Driven YouTube Comment Management: The Good, Bad, and Ugly

- you know you can't resist clicking. Even if it's just for a moment before rapidly closing, we've all succumbed to curiosity about those tempting live videos on , and . Love it or hate it, live streaming has become ubiquitous online entertainment we can scarcely avoid. Live streaming YouTube Twitch, TikTok On these platforms, content creators from all over the world upload daily videos for us. At first, most videos used to be personal blogs or even `how to` videos. Nowadays, they focus on many interesting things, such as gaming and even slot streaming. Twitch and YouTube are the pioneers of slot streaming Twitch pioneered low-latency video game streaming, leveraging advanced codecs and protocols to minimize delay. Streamers naturally spotlighted slots and table games as gaming grew to include casino titles. This interactivity captivates viewers through multilayered outcomes generated by intricate random number generators and game logic algorithms. Others might want to learn new tactics they wish to use themselves afterward. However, the reasons aren’t that important. People just like watching other people play games. offers a similar experience, allowing creators to edit and improve their videos. Its live section will also enable players to stream in real time while interacting with their viewers. YouTube 's latest slot streaming report reveals dominates market share with of hours viewed. follows closely with a share as the longtime first mover. However, upstart grew rapidly at year-over-year, now representing of streaming hours. Even TikTok entered the space in force, securing an 8% share despite its non-native streaming format. CasinoAlpha Twitch 49% YouTube 36% Kick 200% 7% The new joiners that make waves: Kick and TikTok While and are the undisputed leaders in terms of streaming, platforms like and are also making their presence noticed. Kick’s friendly interface and its focus on gaming is quickly gaining popularity among young users. Twitch YouTube TikTok Kick , while it’s known for its short content, is also experimenting with . This upgrade can truly transform the platform into one of the new favorite places for slot and casino streamers. TikTok live streaming Slots, streaming, and programming: an unexpected synergy For programmers and tech enthusiasts, slot streaming offers . The tech behind it is so complex! Everything is fascinating for someone tech-savvy, from random numbers generators to the algorithms that ensure the games are fair. more than just entertainment At the same time, the fact that everything is now integrated with streaming technology and live chat solutions makes everything even more impressive and interesting. How does AI and ML stand in all this Behind the scenes, and power nearly all aspects of the slot streaming ecosystem - from game design to user customization to predictive analytics. artificial intelligence machine learning Game developers apply to iteratively playtest titles, optimizing mechanics, visuals, audio, and narratives. As games are launched, AI agents track metrics like player sessions and spending to optimize them constantly. Also, computer vision algorithms can analyze game videos for emotional reactions and highlight reels. neural networks recommendation engines analyze viewer histories on the user front to serve personalized, new stream suggestions. Natural language processing extracts chat emotion and intent to auto-moderate and identify influencers. Machine learning For technologists, unraveling the intricate and infusing slot streaming offers endless explanations. The interplay of gaming, entertainment technology, data science, and automation blurs the lines between humans and machines. Devoted devs are developing streaming algorithms, further advancing the industry's explosive growth. AI ML Educational aspects of slot streaming If we ignore the entertaining aspect of , we can focus on its educational features. Streaming slots and casino games offer us new perspectives on game mechanics and even help us learn interesting new strategies. slot streaming These strategies can prove to be useful in real life if we know how to choose the right ones. For instance, we can focus on how the streamer manages their bankroll. That skill is useful in our daily lives; thus, it’s worth learning. It’s not just about streaming; it’s about creating and growing online communities. and , in general, are not just a way to pass the time anymore. It has become a great method for people to meet new people and even find friends. We might not realize it, but when we’re talking on a channel full of people who share the same interests as us, it’s impossible not to meet at least someone who we can talk to. Slot streaming gaming streaming The truth is that some of us are not as social and are not able to make new friends effortlessly. But that’s the beauty of these platforms: all types of people join them. Eventually, we all find people who are like us. We just have to join a community that we like, and the right geek will find us, that’s for sure!

Why is Slot Streaming Becoming So Popular on YouTube and Twitch?

কেন স্লট স্ট্রিমিং ইউটিউব এবং টুইচ-এ এত জনপ্রিয় হয়ে উঠছে?

Warum wird Slot-Streaming auf YouTube und Twitch so beliebt?

¿Por qué la transmisión de tragamonedas se está volviendo tan popular en YouTube y Twitch?

Pourquoi le streaming de machines à sous devient-il si populaire sur YouTube et Twitch ?

स्लॉट स्ट्रीमिंग यूट्यूब और ट्विच पर इतनी लोकप्रिय क्यों हो रही है?

YouTube や Twitch でスロット ストリーミングが人気になっているのはなぜですか?

YouTube와 Twitch에서 슬롯 스트리밍이 인기를 끄는 이유는 무엇입니까?

Por que o streaming de slots está se tornando tão popular no YouTube e no Twitch?

Почему стриминг игровых автоматов становится настолько популярным на YouTube и Twitch?

Slot Yayını YouTube ve Twitch'te Neden Bu Kadar Popüler Hale Geliyor?

Tại sao Slot Streaming lại trở nên phổ biến trên YouTube và Twitch?

为什么老虎机流媒体在 YouTube 和 Twitch 上如此受欢迎？

124 Stories To Learn About Youtube

How to Access YouTube in Restricted Areas — 10 Methods

Cómo acceder a YouTube en áreas restringidas: 10 métodos

Comment accéder à YouTube dans les zones restreintes – 10 méthodes

प्रतिबंधित क्षेत्रों में YouTube तक कैसे पहुंचें - 10 तरीके

制限エリアで YouTube にアクセスする方法 — 10 の方法

Como acessar o YouTube em áreas restritas – 10 métodos

Cách truy cập YouTube ở khu vực hạn chế - 10 phương pháp

如何在受限区域访问 YouTube — 10 种方法

YouTube makes it easy to copy-paste a video embed. LOVE THAT! But, the default embed isn’t responsive, so it is guaranteed to look shit wherever you embed it. I’ve drug this snippet around the internet with me for years. /* youtube embed wrapper */
[data-responsive-youtube-container] {
  position: relative;
  width: 100%;
  height: 0;
  padding-bottom: 56.25%;
  margin: 1em 0;
}

[data-responsive-youtube-container] > iframe {
  position: absolute;
  top: 0;
  left: 0;
  width: 100%;
  height: 100%;
} I adapted it to use , my data attributes specification of . But I didn’t write the styles. I hate magic numbers like , and I would have tried to avoid it until giving up 😆 AVO 🥑 CSS BEM 56.25% Usage Copy a YouTube embed from the share sheet. Paste that embed in an HTML or markdown file. Wrap it in a block-element container with the selector. data-responsive-youtube-container Enjoy container-aware responsive videos! Embed responsively! Bonus This works with layouts as well. Around , you’ll see this paired with a flex and grid layout. chan.dev Help Me Attribute This If you know where I originally swiped this from, let me know so I can give the author credit here. I’m on Twitter and Discord. @chantastic

A Guide on How to Embed YouTube Videos Responsively

This is by (source: 05-23-2023). Halm founded DAOHQ. Twitter thread Emmet Halm @ehalm_ https://twitter.com/ehalm_/status/1660914850107883520?s=46&t=hLXLvA2wSOmlARX4ZS1fvA&embedable=true&thread=true The lead image for this article was generated by HackerNoon's via the prompt "YouTube Chatbot” AI Image Generator

Train a Chatbot on an Entire YouTube Channel for Free!

This is by (source: 05-26-2023). Alie shares tips, prompts, and tutorials about AI. Twitter thread Alie Jules @saana_ai https://twitter.com/saana_ai/status/1661812626450698240?s=20&embedable=true&thread=true The lead image for this article was generated by HackerNoon's via the prompt "Monetize AI on YouTube” AI Image Generator

How to Monetize and Make Money With AI on YouTube

Recently, while watching an interview on YouTube, I came up with the idea of optimizing the process by reading through the transcripts. As an evolution of this idea, I started finding a way to get a summary of the video. After some research, I built a simple app that creates a YouTube video summary using OpenAI GPT model. In this article, I'll show you how you can create your own app like this. Requirements Before diving into the code, make sure that you have the following: Python 3 installed on your machine OpenAI API key (you can get a free trial at ) https://platform.openai.com/ Installed Python packages: youtube-transcript-api openai Let’s write some code 1. Extracting ID from the link First, let's extract the video ID from the provided YouTube link: import re


def extract_youtube_video_id(url: str) -> str | None:
    """
    Extract the video ID from the URL
    https://www.youtube.com/watch?v=XXX -> XXX
    https://youtu.be/XXX -> XXX
    """
    found = re.search(r"(?:youtu\.be\/|watch\?v=)([\w-]+)", url)
    if found:
        return found.group(1)
    return None 2. Getting a video transcript Next, we'll write a function to fetch a video's transcript using library: youtube-transcript-api from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled


def get_video_transcript(video_id: str) -> str | None:
    """
    Fetch the transcript of the provided YouTube video
    """
    try:
        transcript = YouTubeTranscriptApi.get_transcript(video_id)
    except TranscriptsDisabled:
        # The video doesn't have a transcript
        return None

    text = " ".join([line["text"] for line in transcript])
    return text 3. Getting summary with GPT-3 Now that we have the video transcript, we can use OpenAI GPT-3 to generate a summary: def generate_summary(text: str) -> str:
    """
    Generate a summary of the provided text using OpenAI API
    """
    # Initialize the OpenAI API client
    openai.api_key = os.environ["OPENAI_API_KEY"]

    # Use GPT to generate a summary
    instructions = "Please summarize the provided text"
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": instructions},
            {"role": "user", "content": text}
        ],
        temperature=0.2,
        n=1,
        max_tokens=200,
        presence_penalty=0,
        frequency_penalty=0.1,
    )

    # Return the generated summary
    return response.choices[0].message.content.strip() Let me go through each parameter: The AI model to be used is instead of because it performs similarly but is 10 times cheaper. When GPT-4 is publicly released, you can easily replace the model with a better one. model: gpt-3.5-turbo text-davinci-003 : This is an array, where each item (message) has a (either , , or ) and (the text message itself). The first message with the role should contain instructions for the AI to guide it. messages role system user assistant content system It’s a number between 0 and 2. It uses for sampling. The higher value (0.8 and more) you use, the more random outcome becomes. Otherwise, a lower value (around 0.2) makes it more focused and predictable. temperature: : Number of chat choices to generate for each input. In this case, we need only one. n : The maximum number of to generate in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length. Let's use 200 tokens for a more concise summary. max_tokens tokens : Number between -2.0 and 2.0. Positive values penalize new tokens based on , increasing the model's likelihood to talk about new topics. presence_penalty whether they appear in the text so far : Number between -2.0 and 2.0. Positive values penalize new tokens based on , decreasing the model's likelihood to repeat the same line verbatim. frequency_penalty their existing frequency in the text so far To lightly reduce the number of repetitive samples, use penalty coefficients between 0.1 and 1. If you want to strongly reduce repetition, you can increase the coefficients up to 2, but be aware that this can worsen the samples. Negative values can be used to increase the chance of repetition. More details you can find in the . documentation Putting It All Together Finally, let's create a function to get all three steps done and run it: def summarize_youtube_video(video_url: str) -> str:
    """
    Summarize the provided YouTube video
    """
    # Extract the video ID from the URL
    video_id = extract_youtube_video_id(video_url)

    # Fetch the video transcript
    transcript = get_video_transcript(video_id)

    # If no transcript is found, return an error message
    if not transcript:
        return f"No English transcript found " \
               f"for this video: {video_url}"

    # Generate the summary
    summary = generate_summary(transcript)

    # Return the summary
    return summary


if __name__ == '__main__':
    url = "https://www.youtube.com/watch?v=D1R-jKKp3NA"
    print(summarize_youtube_video(url)) Here is an example of video summary generated by GPT-3: Steve Jobs Stanford Commencement Speech 2005 Steve Jobs gave a commencement speech at a university where he shared three stories from his life. The first story was about dropping out of college and how it led him to take a calligraphy class, which later influenced the design of the Macintosh computer. The second story was about getting fired from Apple, which allowed him to start over and create successful companies like Pixar. The third story was about his experience with cancer and how it taught him to live each day as if it were his last. He encouraged the graduates to find what they love, not settle, and to have the courage to follow their hearts and intuition. He ended his speech with the message "stay hungry, stay foolish.” Isn't it awesome? 😎 Conclusion Using OpenAI GPT can be a great way to generate summaries of YouTube videos. However, this is just one example. This approach can be used for all sources of information that can be represented as text. It saves time and helps you quickly get the key points. Imagine how helpful it could be with articles or conference calls! Useful links All mentioned code in one place OpenAI Documentation OpenAI Cookbook

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YOUTUBE

youtube.com

10K+ employees

Since 2005

COMPANY RANKING

YOUTUBE

EVERGREEN INDEX #20

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