Emotion Detection & Journaling Web App

📌 Overview

The Emotion Detection & Journaling Web App is an AI-powered platform that allows users to log their daily emotions and track their mood trends over time. It utilizes a Bi-Directional LSTM + CNN model to analyze user inputs and predict emotions, providing insightful visualizations to help users understand their emotional patterns.

🌐 Live Web App

Try out the Emotion Journaling Web App here: Emotion Journaling App

🚀 Features

• Emotion Detection: Uses a deep learning model to classify emotions from text.
• Sentence-wise Emotion Analysis: Breaks down journal entries sentence by sentence and assigns emotions.
• Hard & Soft Emotion Categorization: Provides both granular (sentence-level) and overall (soft) emotion scores.
• Emotion Journaling: Users can log their emotions daily.
• Trend Visualization: Interactive graphs to display emotion trends over time.
• Modern UI: Clean and intuitive interface using Streamlit.

🛠️ Tech Stack

• Framework: Streamlit
• Deep Learning: Bi-Directional LSTM + CNN (TensorFlow/Keras)
• Database: Supabase (used for storing user journal entries and emotion trends)
• Visualization: Matplotlib, Seaborn

⚡ Installation & Setup

Prerequisites

Ensure you have the following installed:

• Python 3.10
• Virtual environment (optional but recommended)

Steps to Run the Project

1. Clone the repository

   git clone https://github.com/kashika13/emotion_detection.git
   cd emotion_detection

2. Create a virtual environment (optional but recommended)

   python -m venv venv
   venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Run the Streamlit app

   streamlit run app.py

📊 Emotion Trend Visualization

• Each journal entry is analyzed sentence-wise to predict emotions.
• The soft emotion (overall emotion) is stored in the Supabase database for long-term trend analysis.
• Users can track their emotional progress over time using interactive graphs.

📝 Usage

1. Enter your journal entry, and the AI model will predict emotions for each sentence.
2. View the overall soft emotion, which is saved for tracking trends.
3. Analyze emotion trends over time with visualizations.

📂 Project Structure

├── app.py                  # Main Streamlit application
├── model/                  # Contains pre-trained ML and DL models
├── notebook/               # Contains jupyter notebook for differnt models and dataset
├── requirements.txt        # List of dependencies
├── README.md               # Project documentation

💡 Contributing

Contributions are welcome! Feel free to fork the repo and submit a pull request.

📜 License

This project is licensed under the MIT License.