A journaling and emotion-tracking web application that helps users monitor their emotional well-being over time.
- Daily journaling: Log journal entries to track thoughts and emotions.
- Emotion detection: Uses a fine-tuned NLP model to classify emotions (joy, sadness, anger, fear, disgust, neutral).
- Mood trends: Visualizes emotional patterns over time with interactive charts.
- Personalized suggestions: Provides well-being tips based on recent dominant emotions.
- Data export: Download the journal history as CSV for offline use.
- Frontend/UI: Streamlit (Python)
- Backend Logic: Hugging Face Transformers (local pipeline)
- Data Storage: CSV file (
journal.csv) - Visualization: Matplotlib, Streamlit charts
- ML Model: DistilRoBERTa (fine-tuned for emotion classification)
- Students exploring mental health and AI applications
- Individuals interested in tracking and improving emotional well-being
Q: How do I log my emotions?
A: Enter a journal entry in the app; the model will automatically classify the emotion.
Q: Can I track trends over time?
A: Yes. Weekly and monthly emotion trends are visualized with charts.
Q: Are the suggestions personalized?
A: Yes. Suggestions are generated based on recent dominant emotions.