Sentiment_analysis_Youtube_comments.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jelius47/Data_sci_w_jelius/blob/main/Sentiment_analysis_Youtube_comments.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# YouTube Comments Summarization with OpenAI\n",
        "\n",
        "In this notebook we harness the power of AI to deeply analyze YouTube comments and video transcripts.\n",
        "\n",
        "1. Fetch Video Transcripts: Automatically retrieve the textual content of YouTube videos using the youtube-transcript-api.\n",
        "2. Extract and Analyze Comments: Use the YouTube Data API to gather viewer comments from specified videos for further analysis.\n",
        "3. Summarize and Link Comments to Transcripts: Apply OpenAI's GPT-3 model to summarize comments and correlate them with video transcripts, extracting meaningful insights that highlight viewer engagement and feedback.\n",
        "4. Perform Sentiment Analysis: Utilize Hugging Face’s sentiment-analysis model to classify comments into positive, negative, or neutral categories, providing a quantitative measure of audience sentiment.\n",
        "5. Visualize Data: Display sentiment analysis results using pie charts to give a visual representation of audience reactions.\n",
        "\n"
      ],
      "metadata": {
        "id": "8fawIZUoFUgf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup\n",
        "First, let's import necessary libraries and define our API keys and youtube video id.\n",
        "\n",
        "Save the following three tokens in the secrets to be able to run this notebook:\n",
        "\n",
        "1.   YT_APIKEY - Google API Key\n",
        "2.   OPENAI_API_KEY - OpenAI API Key\n",
        "\n"
      ],
      "metadata": {
        "id": "IufJRgfhiO_G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install openai\n",
        "\n",
        "from openai import OpenAI\n",
        "from google.colab import userdata\n",
        "\n",
        "# Instantiate OpenAI client (ensure your OpenAI API key is set in your environment variables or pass directly here).\n",
        "client = OpenAI(\n",
        "    api_key=userdata.get('OpenAi_API')\n",
        ")\n",
        "\n",
        "VIDEOID = 'i-txsBoTJtI'\n",
        "GOOGLE_APIKEY = userdata.get('Youtbe_API')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQuidlStGK7G",
        "outputId": "ca8b6c4d-79fe-41eb-d5ee-62fe804b5fd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting openai\n",
            "  Downloading openai-1.25.2-py3-none-any.whl (312 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m312.9/312.9 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting httpcore==1.* (from httpx<1,>=0.23.0->openai)\n",
            "  Downloading httpcore-1.0.5-py3-none-any.whl (77 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting h11<0.15,>=0.13 (from httpcore==1.*->httpx<1,>=0.23.0->openai)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Installing collected packages: h11, httpcore, httpx, openai\n",
            "Successfully installed h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 openai-1.25.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fetching Youtube Video Transcription"
      ],
      "metadata": {
        "id": "NFGnKVHXia61"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install youtube-transcript-api\n",
        "\n",
        "from youtube_transcript_api import YouTubeTranscriptApi\n",
        "\n",
        "def get_video_transcript(video_id):\n",
        "  try:\n",
        "      # Fetching the transcript\n",
        "      transcript = YouTubeTranscriptApi.get_transcript(video_id,  languages=['hi', 'en'])\n",
        "\n",
        "      # Combine all text entries into a single string\n",
        "      full_transcript = \" \".join(entry['text'] for entry in transcript)\n",
        "      return full_transcript\n",
        "  except Exception as e:\n",
        "      print(f\"Failed to fetch transcript: {e}\")\n",
        "      return \"\"\n",
        "\n",
        "def get_transcript_summary(transcript):\n",
        "  response = client.chat.completions.create(\n",
        "        model=\"gpt-3.5-turbo\",\n",
        "        messages=[\n",
        "            {\"role\": \"system\", \"content\": \"Provide a detailed summary of the given youtube video transcript.\"},\n",
        "            {\"role\": \"user\", \"content\": transcript}\n",
        "        ]\n",
        "    )\n",
        "  return response.choices[0].message.content\n",
        "\n",
        "video_transcript = get_video_transcript(VIDEOID)\n",
        "transcript_summary = get_transcript_summary(video_transcript)\n",
        "print(transcript_summary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhEBnzacYWFk",
        "outputId": "2389f8ff-8ac4-4f51-9c09-0061635333a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting youtube-transcript-api\n",
            "  Downloading youtube_transcript_api-0.6.2-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from youtube-transcript-api) (2.31.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->youtube-transcript-api) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->youtube-transcript-api) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->youtube-transcript-api) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->youtube-transcript-api) (2024.2.2)\n",
            "Installing collected packages: youtube-transcript-api\n",
            "Successfully installed youtube-transcript-api-0.6.2\n",
            "The video is a tutorial by David Andre on how to build AI agents using the Llama Free model even with a low-performance computer and minimal programming knowledge. The tutorial walks through the process of setting up the environment using AMA to run the models locally, VS Code for coding, and Gro for optimal performance. The speaker demonstrates the speed of the models, showing 216 tokens per second. Different models like Llama 370b and the open-source model are compared, emphasizing the importance of leveraging AI technology. The tutorial encourages viewers to take the first step in building AI agents and provides a Workshop and community link for further learning. The speaker then starts building agents from scratch, guiding the audience through setting up the necessary tools, importing required packages, defining agents and tasks, and creating a team of agents for specific tasks such as email classification and response. The tutorial encounters some issues with the Llama Free model not working well within the Crew AI framework but eventually resolves it by integrating the Gro API for increased speed and efficiency. The tutorial concludes with a call to embrace the AI revolution and urges viewers to stay at the forefront of AI technology by joining the community. A step-by-step breakdown of the coding process and troubleshooting is provided throughout the tutorial.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fetching YouTube Video Comments"
      ],
      "metadata": {
        "id": "9Wkvi_5aGlY9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from googleapiclient.discovery import build\n",
        "\n",
        "# Function to get YouTube comments\n",
        "def get_comments(video_id, api_key):\n",
        "    youtube = build('youtube', 'v3', developerKey=api_key)\n",
        "    comments = []\n",
        "    response = youtube.commentThreads().list(\n",
        "        part=\"snippet\",\n",
        "        videoId=video_id,\n",
        "        textFormat=\"plainText\",\n",
        "        maxResults=100\n",
        "    ).execute()\n",
        "\n",
        "    while response:\n",
        "        for item in response['items']:\n",
        "            comment = item['snippet']['topLevelComment']['snippet']['textDisplay']\n",
        "            comments.append(comment)\n",
        "\n",
        "        if 'nextPageToken' in response:\n",
        "            response = youtube.commentThreads().list(\n",
        "                part=\"snippet\",\n",
        "                videoId=video_id,\n",
        "                pageToken=response['nextPageToken'],\n",
        "                maxResults=100\n",
        "            ).execute()\n",
        "        else:\n",
        "            break\n",
        "\n",
        "    return comments\n",
        "\n",
        "video_comments = get_comments(VIDEOID, GOOGLE_APIKEY)\n",
        "print(video_comments)"
      ],
      "metadata": {
        "id": "KyMKYIvQGyul",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4966e464-73ee-4096-cd62-b989badbbb49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[\"If you're serious about AI, and want to learn how to build Agents, join my community: https://www.skool.com/new-society\", '0:05 \"even if you have a bad computer\"\\n8:25 \"look at the activity monitor\" -> +20G in memory 😂', 'I am looking to create a local ai tool that will help me reword and spell and grammar check in UK brittish lanagugae that will run locally Windows?', 'Also, I fail to understand that eventually we have to use OPENAI as well!', 'The issue with these third parties is that there is lot under the hood and you will end with a lot of api calls and billing aspect is to be considered.', 'I came here because I am trying to fix all my LLMs not working well with functions using crewai. something up there I think. I have mistal, llama2 with crewai recommended modelfiles, and now tried llama3 your way. each of them fail with different issues trying to call functions.', \"I don't understand the hate on some comments, this is top notch content! \\nAnyway, I found it easier to run this code with Grok by importing ChatGroq with\\n\\nfrom langchain_groq import ChatGroq\\n\\nThen I added the api keys on an . env file like this: GROQ_API_KEY = your_api_groq_key\\n\\nAnd finally when passing the llm for the Agent I set it as ChatGroq(GrokAPI, model_name)\", 'Waste of time', 'Nice video do you have a video where you create and use tools ?', \"Have you tied using Tools with llama3?   For me, they don't work. Agents are not able to figure out the correct parameters. The same CrewAI code works great with OpenAI and Anthropic models.\", 'Nice video and tutorial! Thanks a lot! This gave me the head start I was looking for. Subbed and will def keep watching.', 'I also noticed that as of 2024-04-27 the Llama3 (local LLM) does not work with CrewAI.  However, you can replace Lllama3 with Erik Hartford\\'s excellent model \"dolphin-llama3\" and you get the expected result.  Dolphin-llama has the additional advantage of being uncensored.\\n\\nCheers! Keep up the good work!', 'so did this end up using llama or openai at the end...?', 'your accent is so sexy', 'ctrl+D on all OS\"s exits anything in the terminal', 'Seriously Bad Content!', 'Thanks for sharing', 'Almost burned down my GPU :D but thanks for tutorial', 'Stop faking Llama3 as better than GPT4. Llama3 is at position 5 below GPT4 (proprietary) at number 1, Mistral (proprietary), Gemini Pro (proprietary) and Claude 3 (proprietary.', 'So this started as a good tutorial, ran into some issues and kinda just ended. I did manage to get the groq to work in the end. I do have Llama running on a Docker container. Now i would like to combine both of those . Thanks for the tutorial', 'That’s way too complicated.', \"I bought the course and it's underwhelming. full of fluff. I asked for a refund and he is ignoring my messages. He's a new age scammer\", 'lost me at the beginning.  did everything you said only to immeadiately get error when i paste ollama run llama3 into terminal.  you flew that part too fast.  you missed something.', 'strong ever', 'Hahaha thanks so much brother. I want to stay on the edge with help of a friend such as you.', 'Can i make it live for others to use it?', 'sponsor video?', 'hi handsome.  I have a questions: do you recommend investing in groq: 0x3d330b8d4eb25b0933e564d7284d462346d453ef ? . where do you see groq in 4 years from today? thanks for posting', 'good simple examples showing Groq capabilities', 'Can anyone help me understanding what is the difference between a tool such as crewai and openDevin, from my understanding is that the first help you manage and the second to build? For example I found useful crewai to make collaborate a team of agents and get a final opinion on a specific topic. I never used yet openDevin instead. Thanks in advance to whom will make this more clear to me', 'GREAT VID! FINALLY THE INSTRUCT!!\\n\\n...may I just ask a sidebar question Re. Your VS Code editor window behavior? PLEASE?!!\\n\\nHow have you set your VS Code preferences so that the longer length strings you\\'ve written for the classifier and responder classes (specifically, the strings stored in \\'goal\\' and \\'backstory\\') when they reach the edge of the editor window they wrap to the next line down, WITH THE NEXT WORD CONTINUING FROM THE CORRECT INDENTATION POSITION (Directly beneath the declaration like: \\n\\nTo demonstrate/explain here:\\n|| = indicated the edge of the editors window\\n\\n\\nYour editor looks like this:\\n\\nresponder = Agent(\\n(\\\\t)     goal = \"qwer||\\n           tyabcdefghijklmnop\",\\n)\\n\\nMy editor looks like this:\\n\\nresponder = Agent(\\n(\\\\t)     goal = \"qwer||\\r\\ntyabcdefghijklmnop\", # <--- SEE?\\r\\n) \\n\\nIt\\'s SUPER annoying and has distracted me to the point where I cannot continue until I\\'ve organized this correctly.  OCD as I know... But it\\'s IMPORTANT for maintaining my creative mindset!\\n\\n\\nI\\'ve figured as far a workaround as this (below), and it will do for now but I still consider it a little less than a half-fix solution... breaking the string into multiple lines and using implicit string joining to maintain readability:\\n\\nclassifier = Agent(\\r\\n    role = (\"email classifier\"),\\r\\n    goal = (\"Accurately classify emails based on their importance. Give every email one of these \"\\r\\n    \"ratings: important, casual, or spam\"),\\r\\n    backstory = (\"You are an AI assistant whose only job is to classify emails accurately and \"\\r\\n    \"honestly. Do not be afraid to give emails bad rating if they are not important. Your job is to help the \"\\r\\n    \"user manage their inbox.\"),\\n   \\n...\\n\\nPlease, it\\'s driving me nuts', '10:00 thanks for being real', 'More videos pls', 'Ahhhhgh I was focused on making happen until saw 40gb, blast', 'Ur a legend!', 'BRO - YOU ARE NEXT LEVEL - YOUR FUTURE IS BIG TIME', 'Please can you list me the capabilities the PC needs to run this well\\n\\nNice video 🎉', 'When I use llama 3 8B on ollama or LM Studio, it is much dumber than on OpenRouter. Even after resetting all parameters to factory and loading the llama 3 preset. Even with the full non-quantized 8-bit version on LM studio.', 'Dude you got a ShoutOut By the Man himself Alex Hormozi. Go watch his recent Thumbnail', 'wouldve been nice to see you successfully set up and use llama3 the first time without the use of an openapi key etc', 'Checking the server log file in Ollama revealed this:  {\"function\":\"validate_model_chat_template\",\"level\":\"ERR\",\"line\":437,\"msg\":\"The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\",\"tid\":\"6068\",\"timestamp\":1713755629}  Is this something that can be modified with finetuning?', \"Thanks for the video! \\nOne question: any clue why do you set OPENAI_API_KEY with groq api key? I found it a bit confusing. Especially when using openai's API_KEY for authentication.  Is OPENAI_API_KEY a placeholder in crewai for groq api key? I know a bit nonsense, so what do I missing? Thanks!\", 'the last one with groq is using open ais gpt4 and not llama correct? or do you still needs tokens from openai to get llama working? please explain', 'I see David berman replicated this today David', \"I want something to experiment with agents and get the handle of it and experiment on how much better it can help me at work, without spending any money or having particular premium keys. I've watched a lot of videos but i still don't understand what agent builders allow free to use agents, even if it's on a daily token limit.\", 'How to self host llama 3 on cloud?', 'Nice work David! I appreciate your effort to get this code out to us so quickly.', '@DavidOdrej    Can you get any LLM model to understand what a magical square is, how to create it and create an working example.... ? i bet not', 'so you didnt get it working locally gg', 'llama3 umí hrozně špatně česky. To je hrozně velká nevýhoda', 'Now we need to find a way to pack everything so we can sell those personal assistants and install those assistants in any website.', 'What kind of computer can run llama3:70b locally?', 'Hey brother, thank you very much for your channel. I’m a single father, I’ve been following this AI stuff closely. I’m also in school, and have so many coals in the fire it’s not even funny lol. \\nThank you for your posts, because when I get a decent computer I’ll be able to quickly jump on board. I grew up very poor, my son will have a better life. I need to be on top of this. My next pay check I’ll be joining your community. Any advice on how to get my hands on a decent computer? To run this stuff? What should it have? I don’t want to miss the opportunity to provide for my son', 'Groq like websites and other alternatives', 'I have made state of the art automation scripts for me work and i also added some stealth web scraping methods, how can i train lama model to use my coding methods??', 'Can You make some better example of this agents. Something that is really helpfull, you always say thay for questions of time you do something basic, But it will be really fascinating if you spend more time doing something that have some realistic value. Thanks', \"I'm a 3d artist and not a programmer by any stretch of the imagination. Is there a change we could have CrewAI with a nice user interface and an installer?\", 'thanks cuz,,  gg \\n\\ndont forget to delete that groq api key xx', \"TLDR: Don't bother with this video if you need to run locally\\n\\nHe gets 9:38 in, can't get it working with a local Ollama model so just gives up and switches to a remote model.\\nReally annoying if you're coding along with the video then realise it's useless for your purposes.\\n\\nI hope his premium content is better than this otherwise a bunch of people are getting taken for a ride.\", 'just to make it simpler u can use: \\n\\nfrom langchain_groq import ChatGroq\\n\\n\\ngroqLlama3 = ChatGroq(api_key=\"YOUR_API_KEY_GOES_HERE\",\\n                        model=\"llama3-70b-8192\")\\n\\nThe beauty of Ollama is that u can locally build on top of that \"model specified agents\" like coders or others (medllama) to play a bit more for show case (eg. MedClinicAgents)', \"So the first 60% of this video built up the expectation that we are gonna use offline llama3 py agents, but then at the very end you switch it to using the llama3 available through groq's api. Although you do get the agent working with llama3, its a bit misleading, and it would have been better to straight up say: I havent got Llama3 working offline*, but here is how I got it working through groq's API.\\n\\n*Edit for clarity: llama3 working offline with crewai in the context of this tutorial.\", 'I just received an interesting response from llama 3, claiming something quite unique', 'Worked like a charm - amazing and Groq take a bow! Great videos as ever David..you the man!', \"Groq is a scam. Don't use Groq.\", 'I was going to join and then I saw it was $77!?!? Thats mad.', \"Don't have me for this comment, but... What do you think about using platforms like SmythOS for creating AI Agents? Seems like creating AI Agents is faster and easier with such a platform if you have limited programming skills.\", 'You are not doing this in enough detail you go back and forth. Which makes it hard to follow for allot of people', 'How did you download the 40gb model? Do you have that much disk space?', \"Is it possible to use agents to build an AI powered question answering system for pdf documented for academic research purposes? I'd like to build my own so I can avoid the costs of those currently available commercially.\", 'I would like to know your thoughts on this: With a limitation that restricts the potential of Llama materials to enhance other major language models. \\r\\nResearchers and developers often want to compare or fine-tune different models to improve their performance or tailor them to specific task?\\r\\nHowever, due to the restrictions in the licensing terms, they cannot freely utilize the Llama materials to do so unless they specifically use Llama 3.', 'Hi David, great video again, CrewAI is pretty cool, I like it more than AutoGen Studio. With AutoGen I feel not really in control and the agents talk a lot of bs in the group chat. I would like to support your channel and perhaps join the community, however I find 77$ a month a lot of money. Are you considering other options to support you or join your community, perhaps like different tiers?', \"It's possible to create an agent that talk like a novel character? It have to talk in Italian, no English. I wanna know if your curse you explain that\", \"im so pissed im tried joining at 38$ but it wouldn't work now its 77$ and i still cant join, please make a patron\", \"But I'm not nerd\\n What can I do. My friend?\", 'We did not even get the punch line of that Long Joke :(   just kidding check your email David', 'What Did you even show I missed it😂 .  You just kept saying it was broken', 'more then anything i appreciate your showing when and where these processes dont work . the trouble-shooting is a critical part of process and the overhype of these systems is most deceitful when the user actually tries to integrate them and runs into all sorts of issues that were hidden by showmen . really excited for llama3 finetunes and more powerful agentic systems ,  thinking recursive self-debug and finetuning for the generation of the most understandable debugable code , with proofs and tests , could build a solid foundation .', \"that's some top-tier level shit, keep it up\", 'Thanks david for thw consistency and simplicity..  we still learning about agents, thanks and touch on foundation agents by nvidia dr Jim Fan', \"Love your work, but 77 dollars is a bit delusional. Not sure what kind of value you would have to pay for that. With such a price you'll only get idiots. I pay 4.99 for Control Alt AI subscription and I get incredible complex Comy UI Stable Diffusion workflows etc. You are asking almost twice the money that I pay for my GPT Plus and Claude 3 combined. That's a lot. You have good ideas etc but the content you are sharing is not that complex or innovative to be worth that amount of money, per month? Haha. Anyway. Wish you all the best though.\", 'Love your enthusiasm.', 'Found you today with the Zuck news, and now I like watching you code. 👊', 'is groq api anytime adopt command r plus?', 'Which are the bare Minimum specs for my hardware to be able to run this?', 'Happy you created course , hope you continue .\\nWhat the difference from CREWAI?', 'Great Work!! Thanks for sharing with us.', \"I've been getting a lot of that anti-hacking/anti-illegal activity spam from ollama 3 in a crewai crew, as well. \\n\\nI'm not sure what it is. I also had it spit out some weird DEI stuff in a summary. I'm sure it can be worked out in fine-tuning/prompting. I'll be trying a model fine-tuned on openhermes soon, I'll post results here.\", 'why u need openai key if u using free llama?', 'If I bought your course, do you teach how to make sophisticated ai chatbot?', \"Ollama doesn't work with it for me as well, I was using simple tools like google search, but it gets stuck talking to himself, trying to correct himself, and never does the searches, doesn't work properly both with the text model or instruct model (8b both), it has the same problems if I import Ollama from langchain or just set the environment variables and use it as OpenAI API endpoint.\\nProbably a test should be done with LMStudio, to see if the problem is ollama related or llama3 related. Which doesn't seem to be the case considering with Groq is working fine.\", \"I've had similar weirdness using lamam3 with LM Studio.\", 'What are system requirements for running this model(8b) locally?', 'Does anyone know if you can run this on in iPad locally and upload documents in order to answer queries? For example if you made an app for allergies on a food menu would you be able to upload ingredients of the food menu into the LLM and have it RAG answers similar to \"I have a gluten allergy, can i have the Ceasar salad?\"', 'Did you get llama 3 running with CrewAI without using Groq though? Or did I miss that in the video?', 'Pc recommendations for 70b model?', 'Anyone who is not ultra-rich will never pay 77 USD just to be in your community.\\n\\nIt is simply insane. I suggest you take a different approach. Because it wont work.', 'What did you achieve ?', \"awesome thanks, I wasn't sure if I understood correctly, does downloading the local model work with crewai or only through the  API?\", 'Looks promissing!', 'Is LLAMA 3 uncensored?', 'Bro I just want to say thank you for making this content. It&#39;s always super informative in an easy-to-understand format. Out of the 50 (exaggeration, but there&#39;s alot) I always find myself looking for your videos first. I&#39;ve always had an interest in programming and hacking but didn&#39;t do much with machine learning. But know I&#39;m a man obsessed. Mainly because of how critical it is for normal civilians to learn how create and train these things. I truly belive the future of humanity depends on it.  If corporations kill us all building agi and it escapes especially Gemini we are so screwed because the odds are against us that it finds any value I&#39;m something(humans)that&#39;s killing the thing it exists on or believing we will attempt to shut her down. Or government with runaway military ai because they couldn&#39;t wait until all the bugs were out when they deploy it.', 'Thumbnails?🤣', 'Awesome tutorial David. I think GPT4 as a benchmark is quite old now. I wonder what agents with GPT5 will look like.', 'Thanks. Very helpful. Waiting for 400b model', 'Llama 3 is governed by the Licence Llama 3 community, which means it is not Apache 2.0 opensource for anyone use for anything. You are still governed by Facebook! How useless.', 'If you&#39;re serious about AI, and want to learn how to build Agents, join my community: <a href=\"https://www.skool.com/new-society\">https://www.skool.com/new-society</a>']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Summarizing Comments\n",
        "Summarize comments using OpenAI's GPT model."
      ],
      "metadata": {
        "id": "C1HfBzeONgp2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to split comments into manageable batches\n",
        "def batch_comments(comments, max_tokens=2048):\n",
        "    batches = []\n",
        "    current_batch = []\n",
        "    current_length = 0\n",
        "\n",
        "    for comment in comments:\n",
        "        comment_length = len(comment.split())\n",
        "        if current_length + comment_length > max_tokens:\n",
        "            batches.append(current_batch)\n",
        "            current_batch = [comment]\n",
        "            current_length = comment_length\n",
        "        else:\n",
        "            current_batch.append(comment)\n",
        "            current_length += comment_length\n",
        "\n",
        "    if current_batch:\n",
        "        batches.append(current_batch)\n",
        "\n",
        "    return batches\n",
        "\n",
        "# Function to get summaries from OpenAI\n",
        "def get_comments_summaries(batches):\n",
        "    summaries = []\n",
        "\n",
        "    for batch in batches:\n",
        "        response = client.chat.completions.create(\n",
        "            model=\"gpt-3.5-turbo\",\n",
        "            messages=[\n",
        "                {\"role\": \"system\", \"content\": \"Summarize the following comments while keeping the detailed context.\"},\n",
        "                {\"role\": \"user\", \"content\": \" \".join(batch)}\n",
        "            ]\n",
        "        )\n",
        "        print(response)\n",
        "        summaries.append(response.choices[0].message.content)\n",
        "\n",
        "    return summaries\n",
        "\n",
        "# Function to create final summary from summaries\n",
        "def create_final_summary(summaries, transcript_summary):\n",
        "    summary_text = \" \".join(summaries)\n",
        "    response = client.chat.completions.create(\n",
        "        model=\"gpt-3.5-turbo\",\n",
        "        messages=[\n",
        "              {\"role\": \"system\", \"content\": f\"This is the summary of a YouTube video's transcript: {transcript_summary}. A user has commented on the video. Your task is to analyze this comment in the context of the video transcript. Based on the comment content and its relation to the transcript, please provide detailed insights, addressing these key points:\\n1. Identify positive aspects of the video that the comment highlights and link these to specific parts of the transcript where possible.\\n2. Identify any criticisms or areas for improvement mentioned in the comment, and relate these to relevant sections of the transcript.\\n3. Based on the feedback or suggestions in the comment, recommend new content ideas or topics for future videos that align with the viewer's interests and the overall content strategy but don't make up things from your side unnecessarily. Ensure your analysis is clear and includes specific examples from both the comment and the transcript to support your insights.\"},\n",
        "              {\"role\": \"user\", \"content\": summary_text}\n",
        "        ]\n",
        "    )\n",
        "    return response.choices[0].message.content"
      ],
      "metadata": {
        "id": "CwgJVCG6HPWV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batches = batch_comments(video_comments)\n",
        "summaries = get_comments_summaries(batches)\n",
        "final_comments_summary = create_final_summary(summaries, transcript_summary)\n",
        "print(final_comments_summary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5LYYt3K0yGd",
        "outputId": "9750489d-bbd7-4757-a269-65d3c42a767f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ChatCompletion(id='chatcmpl-9LsmPUOgF7q2thyXPrtPXihy8aPne', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='The user mentions creating a local AI tool for language modification and grammar checking, facing issues with LLMs and CrewAI, and highlights the importance of avoiding excessive API calls and billing concerns. They found it easier to run code with Grok by using ChatGroq and an .env file for API keys. They praise a tutorial that helped them, recommend switching to \"dolphin-llama3\" for better results, and mention difficulties with Tool usage in llama3. The user also expresses frustration with a purchased course, enjoys a tutorial despite encountering errors, asks about investing in GROQ, and seeks advice on crewai and openDevin tools. Additionally, they seek help with VS Code preferences and ask about PC requirements for running tools effectively. Overall, the user appreciates helpful content, expresses gratitude for support, and seeks guidance on various technical aspects.', role='assistant', function_call=None, tool_calls=None))], created=1715002489, model='gpt-3.5-turbo-0125', object='chat.completion', system_fingerprint='fp_3b956da36b', usage=CompletionUsage(completion_tokens=174, prompt_tokens=1451, total_tokens=1625))\n",
            "ChatCompletion(id='chatcmpl-9LsmT2B7qlocVWpfRQ17cEt8kUNQl', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='The commenter had difficulty setting up and using Llama3 locally, encountered errors in the server log file, and questioned the use of APIs like OpenAI and Groq. They sought clarification on the usage of different API keys, self-hosting Llama3 on the cloud, and running Llama3 on specific computer systems. The commenter also expressed interest in training the Llama model and creating practical agents. Additionally, they mentioned concerns about the accessibility and pricing of Groq and sought advice on acquiring a decent computer. There were also mentions of dissatisfaction with the video content and discussions around utilizing platforms like SmythOS for AI agent creation. Lastly, the commenter highlighted concerns about the limitations in utilizing Llama materials and comparing different language models due to licensing restrictions.', role='assistant', function_call=None, tool_calls=None))], created=1715002493, model='gpt-3.5-turbo-0125', object='chat.completion', system_fingerprint='fp_3b956da36b', usage=CompletionUsage(completion_tokens=151, prompt_tokens=1333, total_tokens=1484))\n",
            "ChatCompletion(id='chatcmpl-9LsmWNFZz5keYA2cZa9tlxuRIo410', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"The user praises David's videos, preferring CrewAI over AutoGen Studio due to better control and less irrelevant chat. They express interest in supporting David's channel but find the $77 monthly fee expensive, suggesting different tiers for support. They inquire about creating an Italian-speaking novel-type agent and express frustration over not being able to join at a lower price. The user provides feedback on llama3 and asks about running it locally. They comment on the high cost of the community subscription but express admiration for David's work and the potential of AI. The user also mentions issues with Ollama not functioning properly and asks about system requirements for different models. They appreciate David's content and express enthusiasm for learning about AI and programming.\", role='assistant', function_call=None, tool_calls=None))], created=1715002496, model='gpt-3.5-turbo-0125', object='chat.completion', system_fingerprint='fp_3b956da36b', usage=CompletionUsage(completion_tokens=143, prompt_tokens=1382, total_tokens=1525))\n",
            "Based on the user's comment and its relation to the video transcript, here is the analysis:\n",
            "\n",
            "Positive Aspects Highlighted by the User:\n",
            "1. **Useful Tutorial Content**: The user appreciates the tutorial's guidance on creating AI tools for language modification and grammar checking, indicating that the video provides valuable information on building AI agents.\n",
            "2. **Support and Gratitude**: The user expresses gratitude for the support provided in the tutorial and highlights David's videos as helpful resources, showing that the content is engaging and beneficial for viewers.\n",
            "3. **Practical Application**: The user mentions interest in training the Llama model and creating practical agents, indicating that the tutorial's hands-on approach to building AI agents resonated with the user's goals.\n",
            "\n",
            "Areas for Improvement or Criticisms Raised by the User:\n",
            "1. **Technical Issues with LLMs and CrewAI**: The user faced difficulties setting up LLMs locally and encountered errors with CrewAI, suggesting that these areas could be covered in more detail or addressed more extensively in future tutorials.\n",
            "2. **Concerns about APIs and Billing**: The user emphasizes the importance of avoiding excessive API calls and billing concerns, indicating that future videos could include more guidance on managing API usage effectively.\n",
            "3. **Accessibility and Pricing Concerns**: The user mentions concerns regarding the accessibility and pricing of tools like Groq, suggesting that future videos could explore cost-effective alternatives or provide tips on utilizing these tools efficiently.\n",
            "\n",
            "Recommendations for Future Content:\n",
            "1. **In-depth Tutorials on Troubleshooting**: Consider creating tutorials focused on troubleshooting common issues faced when setting up tools like Llama3 locally or integrating APIs to address technical challenges mentioned by the user.\n",
            "2. **Cost-effective Tool Utilization**: Provide guidance on optimizing API usage to minimize costs and explore affordable options for viewers concerned about pricing and accessibility, aligning with the user's interest in avoiding excessive billing concerns.\n",
            "3. **Comparative Analysis of Language Models**: Offer insights on comparing and utilizing different language models within the constraints of licensing to address the user's interest in exploring various models effectively.\n",
            "\n",
            "By incorporating these recommendations into future videos, David can enhance the user experience, address common challenges faced by viewers, and provide valuable insights on utilizing AI tools efficiently and cost-effectively.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sentiment analysis of comments"
      ],
      "metadata": {
        "id": "ZlGkRSKr0K4f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_sentiments(comments):\n",
        "    sentiment_counts = {'positive': 0, 'negative': 0, 'neutral': 0}\n",
        "\n",
        "    for comment in comments:\n",
        "        # Perform sentiment analysis on each comment individually\n",
        "        try:\n",
        "            sentiments = sentiment_analyzer(comment)\n",
        "            # Assuming the first result is the relevant one if multiple are returned\n",
        "            sentiment = sentiments[0] if isinstance(sentiments, list) and sentiments else None\n",
        "        except Exception as e:\n",
        "            print(f\"Error in sentiment analysis: {e}\")\n",
        "            sentiment = None\n",
        "\n",
        "        # Tally sentiments based on the analysis result\n",
        "        if sentiment and sentiment['label'] == 'POSITIVE' and sentiment['score'] > 0.9:\n",
        "            sentiment_counts['positive'] += 1\n",
        "        elif sentiment and sentiment['label'] == 'NEGATIVE' and sentiment['score'] > 0.9:\n",
        "            sentiment_counts['negative'] += 1\n",
        "        else:\n",
        "            sentiment_counts['neutral'] += 1\n",
        "\n",
        "    return sentiment_counts"
      ],
      "metadata": {
        "id": "uxd7aBpi0PnG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to plot the sentiment distribution\n",
        "def plot_sentiment_distribution(sentiment_counts):\n",
        "    labels = sentiment_counts.keys()\n",
        "    sizes = sentiment_counts.values()\n",
        "    colors = ['gold', 'lightcoral', 'lightskyblue']\n",
        "    explode = (0.1, 0, 0)  # explode 1st slice\n",
        "\n",
        "    plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
        "            autopct='%1.1f%%', shadow=True, startangle=140)\n",
        "\n",
        "    plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "h-RmR9ZXyEH_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers matplotlib\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from transformers import pipeline\n",
        "\n",
        "# Initialize the sentiment-analysis pipeline\n",
        "sentiment_analyzer = pipeline(\"sentiment-analysis\")\n",
        "\n",
        "sentiment_counts = get_sentiments(video_comments)\n",
        "plot_sentiment_distribution(sentiment_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "f2ee344b389e4e84867b187a652e3882",
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        },
        "id": "0G2a3CsK0qXO",
        "outputId": "0dc62796-178e-407e-db5f-594e47ebeedd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.40.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.20.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.12.25)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.51.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.11.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2ee344b389e4e84867b187a652e3882"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c4d80bb01d945959462baa09b517951"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb55b63a79324cdda30edb4cbf6d0283"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2d5926b5cbbd40af942ecd54c614f61c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (762 > 512). Running this sequence through the model will result in indexing errors\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error in sentiment analysis: The size of tensor a (762) must match the size of tensor b (512) at non-singleton dimension 1\n",
            "Error in sentiment analysis: The size of tensor a (588) must match the size of tensor b (512) at non-singleton dimension 1\n",
            "Error in sentiment analysis: The size of tensor a (1107) must match the size of tensor b (512) at non-singleton dimension 1\n",
            "Error in sentiment analysis: The size of tensor a (1206) must match the size of tensor b (512) at non-singleton dimension 1\n",
            "Error in sentiment analysis: The size of tensor a (634) must match the size of tensor b (512) at non-singleton dimension 1\n",
            "Error in sentiment analysis: The size of tensor a (1791) must match the size of tensor b (512) at non-singleton dimension 1\n",
            "Error in sentiment analysis: The size of tensor a (1636) must match the size of tensor b (512) at non-singleton dimension 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}