🧘 MindY - Human-Centered Exploration of Mindfulness Content Discovery 🎥✨

MindY is a concept and exploration project that examines how people search for and engage with mindfulness and relaxation content. It uses natural language processing and information retrieval techniques as a lens to explore personalization, language, and user intent, rather than to deliver a production-ready wellness tool.

This project integrates YouTube metadata retrieval and natural language processing (NLP) with TF-IDF vectorization and cosine similarity to rank and recommend relevant relaxation and mindfulness content.

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Context & Intent

MindY was developed as part of a learning journey focused on human-centered questions around content discovery. Its purpose is to explore how people search for and engage with mindfulness and relaxation content, rather than to deliver a production-ready tool.

The project focuses on experimentation, reflection, and design considerations, with attention to language, intent, and ethical use of technology in wellbeing contexts.

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📑 Table of Contents

1. Project Overview
2. Key Features
3. How It Works
4. Project Structure
5. Requirements
6. Usage
7. Why This System?
8. Next Steps

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📖 Project Overview

MindY consists of two core functionalities:

1. 🧠 AI-Powered Chatbot for Personalized Techniques

   • Uses GPT-4 to generate actionable wellness techniques based on user queries.
   • Allows users to select wellness categories for more refined recommendations.

2. 🎥 Intelligent YouTube Video Search

   • Retrieves relevant wellness videos from YouTube API.
   • Ranks videos by semantic similarity using TF-IDF & cosine similarity.
   • Ensures users get high-quality, meaningful video recommendations.

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✨ Key Features

🎯 Personalized Recommendations

• Uses GPT-4 to suggest specific techniques tailored to user queries.
• Example: A query like "How can I reduce stress?" may generate:
  • Guided Breathing Exercise
  • Mindfulness Meditation for Anxiety Relief

🔍 Smart Video Search

• Queries YouTube API and ranks videos by similarity to the user's query.
• Prevents generic recommendations by matching video descriptions to techniques.

📂 Category-Based Suggestions

• Users can select predefined wellness categories, such as:
  • 🧘 Mindfulness & Meditation
  • 💨 Breathing Exercises
  • 🤸 Somatic Practices

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🤔 How It Works

Query Processing Pipeline

1. 💬 User Input

   • The app collects a query (e.g., "How do I sleep better?").
   • If a category is selected, the response is focused on that domain.

2. 🧠 AI Response Generation

   • GPT-4 generates a personalized list of techniques.
   • Extracts keywords and themes from the response.

3. 🔗 YouTube Search & Ranking

   • The system queries YouTube API for videos using refined keywords.
   • Uses TF-IDF & cosine similarity to re-rank videos based on relevance.

4. 📜 Results Display

   • The app presents AI-generated techniques alongside top-ranked videos.

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📂 Project Structure

Key Files & Directories

• app.py → Main Streamlit app (chatbot & video recommendations)
• requirements/ → Contains dependencies for different functionalities:
  • mindy_app.txt → Dependencies for Streamlit app
  • genai.txt → Dependencies for query simulation & clustering
  • nlp.txt → Dependencies for data analysis
• simulate_queries/ → Query simulations & GPT data extraction
  • Contains the following key files:
    • app_logic.py: Implements core logic for GPT-4 recommendations, YouTube video fetching, ranking, and query processing.
    • simulate_queries.py: Automates the simulation of user queries using app_logic.py to extract GPT-recommended techniques and descriptions. Outputs results to simulation_results.csv.
• data_analysis/ → NLP analysis & clustering scripts:
  • Contains scripts for text preprocessing, clustering, and visualization of results.
  • Includes simulated_results.csv, which holds the output of simulated user queries for analysis.
• MindyPresentation.pdf → Project presentation
• README.md → Project documentation

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⚙️ Requirements

1. API Keys:

   • YouTube API Key (store your Key in a .env file as YOUTUBE_API_KEY).
   • OpenAI API Key (store your Key in the .env file as OPENAI_API_KEY).

2. Dependencies:

   • Python libraries: streamlit, openai, scikit-learn, google-api-python-client, python-dotenv.

3. Install Dependencies:

   • To keep dependencies isolated and avoid conflicts, create separate virtual environments for each functional area.

   🟢 MindY App (Streamlit)

   1. Create a virtual environment for the Streamlit app:

   python3 -m venv mindy_app_env
   source mindy_app_env/bin/activate  # On Windows, use mindy_app_env\Scripts\activate

   2. Install the dependencies:

   pip install -r requirements/mindy_requirements.txt

   🟡 Simulating Queries (GPT Data Extraction & Clustering)

   1. Create a virtual environment for query simulation and clustering:

   python3 -m venv genai_env
   source genai_env/bin/activate  # On Windows, use genai_env\Scripts\activate

   2. Install the dependencies:

   pip install -r requirements/genai.txt

   🔵 Data Analysis (Clustering & NLP Processing)

   1. Create a virtual environment for data analysis:

   python3 -m venv nlp_env
   source nlp_env/bin/activate  # On Windows, use nlp_env\Scripts\activate

   2. Install the dependencies:

   pip install -r requirements/nlp.txt

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🚀 Usage

1️⃣ Run the Application

• Activate the virtual environment for the MindY App:

source mindy_app.env/bin/activate  # On Windows: mindy_app.env\Scripts\activate

• Launch the chatbot and video recommender:

streamlit run app.py

2️⃣ Input Queries

• Enter a wellness-related query (e.g., "How can I improve my focus?").
• Optionally, select a category for more refined suggestions.

3️⃣ View AI-Powered Recommendations

• The app displays customized techniques & top-ranked YouTube videos.

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❓ Why This System?

✅ Traditional YouTube searches lack personalization.

This system improves recommendations by:

• 🎯 Understanding query intent (semantic search, not just keywords).
• 🛠️ Filtering & ranking YouTube videos for better quality.
• 💡 Providing AI-powered guidance, not just links.

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🛠️ Next Steps

🚀 Integrate Clusters into GPT’s Workflow

• Leverage clustered techniques to provide more cohesive recommendations.
• Map user queries to cluster themes for more personalized outputs.

🔧 Enhance Query Preprocessing

• Improve text preprocessing by refining techniques like removing stop words and applying lemmatization.

🎯 Refine Prompt Design

• Shift from a narrow focus on Mindfulness and Relaxation to a broader range of well-being areas.
• Expand prompts to include diverse domains like emotional management and cognitive behavioral therapy (CBT).

📚 Broaden Categories

• Introduce new categories (e.g., journaling prompts) to generate more varied and meaningful recommendations.

💡 Experiment with Free Models

• Simulate queries using a generalized system, testing a free model like DeepSeek.

🔄 Evaluate Clustering Impact

• Repeat clustering processes and analyze how the new implementations influence results.