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tirth8205/MoodMoji

MoodMoji is a Python-based desktop application that uses AI to detect emotions from webcam footage in real-time, mapping them to emojis for a fun, interactive experience. It features emotion recognition, custom emoji capture, and mood-based Spotify playlist suggestions, built with OpenCV, TensorFlow, and Tkinter.

aideep-learningemotion-detectionfacial-recognitionopencvpythonspotifytensorflow

MoodMoji: AI Emotion-to-Emoji Personality Mirror

MoodMoji is an innovative desktop application that leverages artificial intelligence to detect your emotions in real-time using your webcam. By analyzing your facial expressions, it maps your emotions to corresponding emojis, providing a fun and interactive way to visualize your mood. Additionally, MoodMoji enhances your experience by suggesting Spotify playlists tailored to your detected emotion, allowing you to immerse yourself in music that matches or uplifts your mood.

Table of Contents

Overview

MoodMoji uses a deep learning model to classify emotions such as Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral from live webcam footage. The app displays the detected emotion as an emoji and suggests a Spotify playlist that aligns with your mood. You can also capture custom emojis that blend your face with the detected emotion emoji, saving them as PNG files for keepsakes or sharing.

The project is built with Python, utilizing libraries like OpenCV for webcam access, TensorFlow for emotion recognition, and Tkinter for the graphical user interface. It’s designed to be user-friendly and engaging, making it a great tool for personal entertainment or exploring AI emotion detection.

Features

  • Real-time Emotion Detection: Detects emotions from your facial expressions using a pre-trained deep learning model.
  • Emoji Mapping: Displays an emoji corresponding to your detected emotion (e.g., 😢 for Sad, 😊 for Happy).
  • Mood Playlist Suggestion: Provides a clickable link to a Spotify playlist that matches your current emotion (e.g., "Sad Songs" for a sad mood).
  • Custom Emoji Capture: Allows you to save a custom emoji that overlays your face with the detected emotion emoji.
  • Demo Mode: If the pre-trained model is unavailable, the app can run in demo mode, cycling through emotions randomly.
  • User-Friendly Interface: Built with Tkinter, featuring a clean layout with a video feed, emoji display, and controls.

Screenshots

Here are some snapshots of MoodMoji in action:

  • Detecting a Happy Emotion with Playlist Suggestion
    MoodMoji detecting a happy emotion with playlist suggestion

  • Detecting a Sad Emotion with Playlist Suggestion
    MoodMoji detecting a sad emotion with playlist suggestion

Requirements

To run MoodMoji, you’ll need the following:

  • Operating System: macOS (tested on macOS 14+), Linux, or Windows (may require adjustments for Windows).
  • Hardware: A webcam for real-time emotion detection.
  • Python: Version 3.9 or higher (tested with 3.12.7).
  • Dependencies: The following Python packages (listed in requirements.txt):
    • opencv-python==4.5.5.64: For webcam access and image processing.
    • tensorflow==2.16.2: For the emotion recognition model.
    • numpy==1.26.4: For numerical operations.
    • matplotlib>=3.5.2: For plotting (used in training scripts).
    • pillow>=9.1.1: For image handling in Tkinter.
    • scikit-learn>=1.1.1: For data preprocessing (used in training scripts).
    • tqdm>=4.64.0: For progress bars during training.
    • pandas>=1.4.2: For data handling (used in training scripts).
    • h5py>=3.7.0: For loading the pre-trained model.
    • seaborn>=0.11.2: For visualization (used in training scripts).
    • pyobjc-core>=7.3 and pyobjc-framework-Cocoa>=7.3: For macOS compatibility with Tkinter.

Installation

Follow these steps to set up and run MoodMoji on your machine:

  1. Clone the Repository
    Clone the project from GitHub:

    git clone https://github.com/tirth8205/MoodMoji.git
cd MoodMoji

  2. Create a Virtual Environment
    Create a virtual environment to isolate dependencies:

    python3 -m venv .venv_system
source .venv_system/bin/activate

  3. Install Dependencies
    Install the required Python packages:

    pip install -r requirements.txt

  4. (macOS Only) Ensure Tkinter Compatibility
    If you encounter Tkinter issues on macOS, ensure your Python installation includes Tcl/Tk support. You may need to install Python via pyenv with Tcl/Tk:

    brew install tcl-tk
pyenv uninstall 3.12.7
env PYTHON_CONFIGURE_OPTS="--with-tcltk-libs='-L$(brew --prefix tcl-tk)/lib -ltcl8.6 -ltk8.6' --with-tcltk-includes='-I$(brew --prefix tcl-tk)/include'" pyenv install 3.12.7

    Then recreate the virtual environment as in step 2.

Usage

  1. Launch the Application
    Run the app with the following command (ensure the virtual environment is activated):

    export TK_SILENCE_DEPRECATION=1
python3 app.py --auto_start

    The --auto_start flag automatically starts the webcam. Omit it if you prefer to start the webcam manually.

  2. Interact with the App

    • The app will open a window displaying your webcam feed on the left.
    • On the right, the "Your MoodMoji" section shows the emoji corresponding to your detected emotion.
    • Below the video feed, the "Controls" section includes:
      • Start/Stop Webcam: Toggle the webcam on or off.
      • Capture MoodMoji: Save a custom emoji combining your face with the detected emotion.
      • Demo Mode: Toggle to cycle through emotions randomly if the model is unavailable.
      • Emotion and Confidence: Displays the detected emotion and confidence level.
      • Mood Playlist: Shows a Spotify playlist link matching your mood—click to open it in your browser.

  3. Save Custom Emojis
    Click the "Capture MoodMoji" button to save a custom emoji to the captured_emojis directory. The filename will include the emotion and timestamp (e.g., moodmoji_sad_20250430_123456.png).

Troubleshooting

  • Webcam Not Working
    Ensure your webcam is connected and accessible. On macOS, check System Preferences > Security & Privacy > Camera to grant permission to Python.

  • Tkinter Errors
    If you see ModuleNotFoundError: No module named '_tkinter', your Python installation lacks Tcl/Tk support. Follow the macOS-specific installation steps above to reinstall Python with Tcl/Tk.

  • Emotion Model Not Found
    If models/emotion_model.h5 is missing, the app will run in demo mode. You can train a new model using train.py or download a pre-trained model if available.

  • Spotify Playlist Links Not Opening
    Ensure you have a web browser installed and that the Spotify links are valid. You can update the links in app.py under the mood_playlists dictionary if needed.

Contributing

We welcome contributions to improve MoodMoji! To contribute:

  1. Fork the repository.
  2. Create a new branch for your feature or bugfix: 
    git checkout -b feature/your-feature-name
  3. Make your changes and commit them: 
    git commit -m "Add your feature description"
  4. Push to your fork: 
    git push origin feature/your-feature-name
  5. Open a pull request with a detailed description of your changes.

Please ensure your code follows PEP 8 style guidelines and includes appropriate comments/docstrings.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions, suggestions, or issues, feel free to reach out:

Acknowledgments

  • The emotion recognition model is based on a pre-trained deep learning model, fine-tuned for this project.
  • Thanks to the open-source community for providing libraries like OpenCV, TensorFlow, and Tkinter.
  • Spotify playlist links are sourced from public playlists available on Spotify as of April 2025.

Contributors

TI
Other
Created April 17, 2025
Updated April 30, 2025