Journal App with Emotion Analysis

The Journal App is a web application designed to help users track their daily emotions and journal entries. The system integrates a machine-learning emotion classifier to analyze journal content and provide insights into the user's emotional state.

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Subsystems

1. Machine Learning Client

• Functionality: Uses a Hugging Face pre-trained model (distilbert-base-uncased-emotion) to classify emotions from text.
• Docker Image: ML Client on DockerHub

2. Web Application

• Functionality: Provides the frontend and backend for journal management.
• Docker Image: Web App on DockerHub

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Team

• Nick Burwell
• Finn Eskeland
• Jessie Kim
• Dasha Miroshnichenko

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Setup Instructions

Prerequisites

• Docker and Docker Compose installed
• Python 3.11+
• MongoDB
• Git
• Pipenv

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Opening app

1. Clone the respository:

    git clone https://github.com/software-students-fall2024/5-final-fixers3-0.git
    cd 5-final-fixers3-0

2. Set up environment variables:

MONGO_DBNAME=SWE_Project_5
MONGO_URI=
SECRET_KEY=
ML_CLIENT_URL=http://ml-client:5002/analyze
FLASK_APP=app.py
FLASK_ENV=production
FLASK_PORT=5001
FLASK_DEBUG=1

3. Create a new virtual environment following the commands:

python3 -m venv .venv

Mac

source .venv/bin/activate

Windows

.venv\Scripts\activate

4. Install Dependencies if not already installed

pip install -r requirements.txt

5. Docker Compose

Make sure that the Docker Desktop is downloaded and you are logged into your account before running the following comand:

docker-compose up --build

Digital Ocean

(http://68.183.158.71:5001/)