🎬 Mood Meets Media 1.5

🌿 Let your mood guide your next binge… and your next emotional step. 📖 About Mood Meets Media 1.5

Mood Meets Media is an emotionally-aware content recommender that blends NLP-driven mood detection, emotion wheel mapping, and LLM-powered emotional reflection generation.
It helps users not just consume media — but connect emotionally with personalized suggestions, self-reflections, and emotional growth pathways.
Built with Streamlit, HuggingFace Transformers, OpenAI GPT, and modern cloud practices.

🛠 Tech Stack Component Tech App Framework Streamlit Mood Detection HuggingFace Models + Heuristic Context Adjustment Emotional Mapping Hybrid Emotion Wheel Mapping Generative AI OpenAI GPT-3.5 Turbo Content Recommendation TF-IDF Matching + Custom Emotional Themes Hosting Streamlit Cloud

📚 Architecture Overview

User Journaling Input ⬇ Detect Sentiment Polarity (HuggingFace + Heuristic Adjustment) ⬇ Map to Final Mood Group (Hybrid Emotion Wheel + Custom Mapping) ⬇ Generate Warm Emotional Reflection (OpenAI GPT-3.5) ⬇ Recommend Movies/TV Shows (based on emotional tone + dataset) ⬇ Suggest Emotional Transition Pathways (Optional)

✨ Key Features

🎯 Real-time Mood Detection (via Journaling or Mood Picker)

🎯 Emotionally Adaptive Recommendations (Movies + TV)

🎯 NEW! LLM-generated Emotional Reflections (GPT-3.5)

🎯 Smart Emotional Mapping and Transition Paths

🎯 User-Centered, Warm, and Intuitive Design

🎯 Built Fully with Latest OpenAI 1.0 API Standards

## New Feature Update ✨

Mood Meets Media now uses emotion-aware intelligence to personalize your content journey:

• Your journal entries are analyzed using NLP models.
• Detected moods are mapped to carefully curated genres.
• Recommendations dynamically shift based on your emotional needs.
• Gentle mood transitions are suggested to help you feel supported and uplifted.

Experience content that aligns with how you feel 🌿

🚀 Future Roadmap (Optional Section)

Mood Meets Media 2.0:

    Memory-Aware Reflection Tuning

    Dynamic LLM-based Content Suggestion

    Cross-format Recommendations (Books, Podcasts, YouTube)