https://github.com/rafaykhattak/emotionai
EmotionAI uses a CNN model to analyze facial expressions and accurately recognize emotions (happiness, sadness, anger, etc.)
https://github.com/rafaykhattak/emotionai
cnn computer-vision emotion-recognition keras-neural-networks
Last synced: 10 months ago
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EmotionAI uses a CNN model to analyze facial expressions and accurately recognize emotions (happiness, sadness, anger, etc.)
- Host: GitHub
- URL: https://github.com/rafaykhattak/emotionai
- Owner: RafayKhattak
- Created: 2023-05-23T09:50:05.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-05-23T10:58:32.000Z (about 3 years ago)
- Last Synced: 2025-04-02T16:50:40.697Z (over 1 year ago)
- Topics: cnn, computer-vision, emotion-recognition, keras-neural-networks
- Language: Python
- Homepage: https://rafaykhattak-emotionai-app-spj22z.streamlit.app
- Size: 13.9 MB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# EmotionAI
EmotionAI is an Emotion Detection tool powered by artificial intelligence technology. It leverages a powerful CNN model to analyze facial expressions and accurately recognize emotions such as happiness, sadness, anger, and more.
## Features
- Real-time emotion detection from webcam feed.
- Supports multiple emotions including Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise.
- Simple and intuitive user interface.
## Demo
Go to the website link to test the project yourself.
## Installation
1. Clone the repository:
```
git clone https://github.com/your-username/EmotionAI.git
cd EmotionAI
```
2. Install the required dependencies:
```
pip install -r requirements.txt
```
3. Run the EmotionAI tool:
```
streamlit run app.py
```
## Technologies Used
- Python
- OpenCV
- Keras
- TensorFlow
- Streamlit
- Streamlit WebRTC
## Model Training
If you're interested in training your own emotion detection model, follow these steps:
- Download the images dataset from here: https://www.kaggle.com/jonathanoheix/face-expression-recognition-dataset
- Implement and train a Convolutional Neural Network (CNN) model using a framework like Keras or TensorFlow.
- Evaluate the model's performance and fine-tune as necessary.
- Save the trained model weights.
- Update the model.h5 file in the project with your own trained model.