MoodTracker

MoodTracker Live Demo

Note: Scroll all the way down to access the projects

MoodTracker is a multi-modal mood analysis application that provides users with different ways to track and analyze their daily mood. This project combines three components: a real-time face emotion detection application, an emotion classifier app, and a voice-based mood analyzer. Each component offers unique features for mood tracking and analysis.

Features

Real-Time Face Emotion Detection Application

• Real-time facial expression analysis using the computer's webcam.
• Pre-trained model for emotion detection.
• Displays detected mood on the screen.

Emotion Classifier App (Text-Based Mood Analyzer)

• An NLP-powered web app that can predict emotions from text recognition with 70 percent accuracy.
• Utilizes Python libraries including Numpy, Pandas, Seaborn, Scikit-learn, Scipy, Joblib, eli5, lime, neattext, altair, and Streamlit.
• Employs a Linear regression model from the scikit-learn library to train a dataset containing speeches and their respective emotions.
• Joblib is used for storing and using the trained model in the website.

Voice-Based Mood Analyzer

• Captures user's spoken input to analyze daily mood.
• Utilizes sentiment analysis for mood tracking.
• Offers a graphical user interface for recording and analyzing mood.

Requirements

The MoodTracker project requires the following dependencies for each component:

Real-Time Face Emotion Detection Application:

• Python
• OpenCV
• Keras
• Haar Cascade Classifier
• Pre-trained Emotion Detection Model

Emotion Classifier App (Text-Based Mood Analyzer):

• Numpy
• Pandas
• Seaborn
• Scikit-learn
• Scipy
• Joblib
• eli5
• lime
• neattext
• altair
• Streamlit

Voice-Based Mood Analyzer:

• Python
• SpeechRecognition
• TextBlob
• Tkinter
• Matplotlib

Usage

To use the MoodTracker application, follow the specific installation and execution instructions for each component. Each component offers a different way to track and analyze your mood.

1. Real-Time Face Emotion Detection Application

• Install the required dependencies for the Real-Time Face Emotion Detection Application.
• Clone or download the project repository.

      git clone https://github.com/CODEWITHRIZA/MoodTracker.git

• Navigate to the Webcam Opencv Project folder.

   cd "Webcam Opencv Project"

• Install the required packages and dependencies by running the following command:

  pip install -r requirements.txt
  

• Run the application using the following command:

  streamlit run app.py

• The real-time face emotion detection application will open, and you can start using it by facing your webcam.

2. Emotion Classifier App (Text-Based Mood Analyzer)

• Install the required dependencies for the Emotion Classifier App.
• • If you've already cloned or downloaded the project repository, there's no need to do it again. The given command below

       git clone https://github.com/CODEWITHRIZA/MoodTracker.git
       cd MoodTracker

• Navigate to the NLP-Text-Emotion folder.

      cd NLP-Text-Emotion

• Install the required packages and dependencies by running the following command:

     pip install -r requirements.txt

• Run the application using the following command:

    streamlit run app.py

• Access the app in your web browser, as it will provide a web interface for you to enter text and analyze emotions.

3. Voice-Based Mood Analyzer

   • Install the required dependencies for the Voice-Based Mood Analyzer.
   • If you've already cloned or downloaded the project repository, there's no need to do it again. The given command below

       git clone https://github.com/CODEWITHRIZA/MoodTracker.git
       cd MoodTracker

   • Navigate to the root folder of the project.
   • • Install the required packages and dependencies by running the following command:

      pip install -r requirements.txt

   • Run the voice-based mood analyzer using the following command:

      python voice_mood_analyzer.py
     

   • The graphical user interface for voice-based mood analysis will open, allowing you to record and analyze your mood through spoken input.

Note

Each component offers a different way to track and analyze your mood. Make sure you have the required dependencies installed for the component you wish to use.

Combined Features

• Mood Tracking: Each component tracks daily mood using a specific modality (real-time face, text, voice).
• Sentiment Analysis: Sentiment analysis is performed on the captured data to determine mood.
• Data Visualization: The text-based and voice-based analyzers provide visual mood feedback using Matplotlib.
• Web and Graphical Interfaces: The emotion classifier app offers a web-based interface, while the voice-based component uses a graphical user interface.
• Real-time Updates: The real-time face emotion detection application provides real-time feedback based on facial expressions.

The MoodTracker project is designed to help users gain insights into their emotional well-being and better understand their daily mood patterns. It offers a variety of options for tracking and analyzing moods through different sensory modalities.

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