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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.
- Real-time facial expression analysis using the computer's webcam.
- Pre-trained model for emotion detection.
- Displays detected mood on the screen.
- 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.
- 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.
The MoodTracker project requires the following dependencies for each component:
- Python
- OpenCV
- Keras
- Haar Cascade Classifier
- Pre-trained Emotion Detection Model
- Numpy
- Pandas
- Seaborn
- Scikit-learn
- Scipy
- Joblib
- eli5
- lime
- neattext
- altair
- Streamlit
- Python
- SpeechRecognition
- TextBlob
- Tkinter
- Matplotlib
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.
- 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.
- 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.
-
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.
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.
- 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.