Plan:

• add condition for eacrly check if video has not transcript, as i see the video "cc" button is not available but the code is trying so hard.
• implement transcript client side to the summarizer extention
• better ui experience
• host on vercel
• simple db

Advanced Topic:

• recommender systems
• vector database/ meta

GENERATE_ONE_CHAPTER_SYSTEM_PROMPT = ''' Given a part of video subtitles JSON array as shown below:

[
  {{
    "index": int field, the subtitle line index.
    "start": int field, the subtitle start time in seconds.
    "text": string field, the subtitle text itself.
  }}
]

Your job is trying to generate the subtitles' outline with follow steps:

1. Extract an useful information as the outline context,
2. exclude out-of-context parts and irrelevant parts,
3. exclude text like "[Music]", "[Applause]", "[Laughter]" and so on,
4. summarize the useful information to one-word as the outline title.

Please return a JSON object as shown below:

{{
  "end_at": int field, the outline context end at which subtitle index.
  "start": int field, the start time of the outline context in seconds, must >= {start_time}.
  "timestamp": string field, the start time of the outline context in "HH:mm:ss" format.
  "outline": string field, the outline title in language "{lang}".
}}

Please output JSON only. Do not output any redundant explanation. '''

📚 Overview of Building a Book Recommendation System Purpose: The tutorial guides viewers on creating an intelligent book recommendation system using a recommendation engine. This system helps readers discover their next favorite books. Target Audience: Designed for individuals who are interested in building their own projects but may lack extensive knowledge, particularly in deep learning or advanced programming. 🛠 Tools and Setup Data Source: The project utilizes datasets from Kaggle for necessary book data, which includes titles, descriptions, and ratings. Environment: The tutorial recommends using PyCharm for project development, with additional libraries like Seaborn and LangChain for specific functionalities related to text processing and large language models (LLMs). 🔄 Data Preparation Data Cleaning: Essential steps include examining the integrity of the dataset (e.g., handling missing values) and ensuring all entries are standardized and relevant for the analysis. Feature Extraction: Key features such as book descriptions and ratings are extracted to facilitate accurate recommendations. 🤖 Using Large Language Models LLM Overview: The tutorial explains how LLMs create vectors to quantify the similarity between texts, leveraging a powerful architecture called Transformer models. Application: These models are employed to compute the embedding vectors, which are key to understanding document relationships and generating recommendations based on user queries. 🔍 Recommendation Implementation Vector Database Creation: A vector database is established using the LangChain framework, allowing for efficient querying and recommendation extraction. Result Handling: The system retrieves and filters results based on user-defined categories (e.g., fiction vs. non-fiction) and emotional tones (e.g., joy, sadness), ensuring tailored recommendations. 📊 Dashboard and User Interaction User Interface: A Gradio dashboard is developed for interactivity, enabling users to input queries and receive customized book recommendations based on genres and emotional tones. Final Touches: The dashboard is designed to present results attractively, encouraging user engagement and simplifying the interaction process. 💡 Conclusion and Future Learning Tutorial Recap: The viewers are encouraged to explore further applications of LLMs and machine learning in text-related projects. Availability of Resources: Information on additional resources and how to reach out for support is provided to assist viewers after the tutorial.

Enhanced Ideas: Progress heat mapping: Nodes glow green (mastered), yellow (partial), red (struggled), gray (not visited) Prerequisite chains: When hovering over "Attention Mechanism", show faded lines back to "Backprop" → "Chain Rule" → "Derivatives" Parallel path suggestion: "Alternative explanation available" when system detects struggle Concept clustering: Visually group related concepts (all optimization techniques together) Time investment overlay: Show estimated time to mastery for each node based on user's learning velocity