https://github.com/guhyun9454/emotionanalyzerwebapp

This project aims to learn and implement microservice programming, continuous integration (CI), and continuous deployment (CD) using GitHub Actions. The goal is to create a web application that utilizes a pre-trained AI model (by me) to demonstrate real-time emotion analysis.
https://github.com/guhyun9454/emotionanalyzerwebapp

ai cicd docker dockercompose

Last synced: about 1 month ago
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This project aims to learn and implement microservice programming, continuous integration (CI), and continuous deployment (CD) using GitHub Actions. The goal is to create a web application that utilizes a pre-trained AI model (by me) to demonstrate real-time emotion analysis.

• Host: GitHub
• URL: https://github.com/guhyun9454/emotionanalyzerwebapp
• Owner: guhyun9454
• Created: 2024-05-02T03:58:22.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-18T17:53:48.000Z (7 months ago)
• Last Synced: 2024-05-19T14:53:07.083Z (7 months ago)
• Topics: ai, cicd, docker, dockercompose
• Language: Python
• Homepage:
• Size: 67.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# EmotionAnalyzerWebApp

## Preview

![Example Image](images/preview.png)

![Example Image](images/preview2.png)

## Getting Started

Follow these steps to run the project locally:

1. **Clone the repository:**

   Open your terminal and run the following command to clone the repository:

   ```Shell

   git clone https://github.com/guhyun9454/EmotionAnalyzerWebApp

   ```

2. **Navigate to the project directory:**

   After cloning, change into the project directory:

   

   ```Shell

   cd [cloned directory]

   ```

3. **Start the application:**

   Run the following command in the terminal to start the application using Docker Compose:

   ***Make sure that you installed and run Docker Desktop or Docker Daemon***

   ```Shell

   docker compose up

   ```

4. **Access the application:**

   Open your web browser and go to "http://localhost:8501" to experience the application.

5. **Explore**

   

   Test with your own images or test image provided.

   

   ***Accuracy may be poor for faces that do not look forward.***

5. **Stop the application**

   To stop and remove all stuffs created by `docker compose up`, run the following command in the terminal:

   ```Shell

   docker compose down  

   ```

## Architecture

![Example Image](images/architecture.jpeg)

The system uses a microservice architecture orchestrated by Docker Compose.

## AI Model Details

- The emotion classification model is based on a CNN architecture optimized for detecting subtle facial features and expressions.

- The model was trained from the scratch on a dataset specifically designed for Korean facial features to ensure higher accuracy in the target demographic. You can find the dataset [here](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=83).

This repository deploys the previously trained model as a web application. The training process involved testing multiple models and fine-tuning the architecture for optimal performance. The data was preprocessed and analyzed to ensure high-quality inputs for training. For detailed information on the model training process and to view the full report, please visit the [training report repository](https://github.com/guhyun9454/VisionAssistEmotionAI).