https://github.com/parth-udawant/emotion-detection
https://github.com/parth-udawant/emotion-detection
artificial-neural-networks deeplearning deeplearning-ai machine-learning opencv opencv-python python python3 streamlit tensorflow tensorflow-examples
Last synced: about 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/parth-udawant/emotion-detection
- Owner: Parth-Udawant
- Created: 2025-07-23T15:51:02.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-07-23T15:54:58.000Z (11 months ago)
- Last Synced: 2025-07-23T17:44:46.587Z (11 months ago)
- Topics: artificial-neural-networks, deeplearning, deeplearning-ai, machine-learning, opencv, opencv-python, python, python3, streamlit, tensorflow, tensorflow-examples
- Language: Python
- Homepage:
- Size: 18.2 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
README
# 📸 Real-Time Emotion Detection App
Detect human emotions in real-time using deep learning and your webcam, built with **TensorFlow**, **OpenCV**, and **Streamlit**.
## 🚀 Features
- 🔍 Real-time emotion recognition from webcam feed
- 🧠 Trained on **FER-2013** dataset with high accuracy
- 💻 Simple and interactive **Streamlit UI**
- 💬 Detects emotions like: **Happy**, **Sad**, **Angry**, **Fear**, **Surprise**, **Neutral**.
- 💡 Easy to run locally or deploy on the web
## 🎭 Emotions Detected
| Label | Emoji | Description |
| -------- | :---: | ------------------ |
| Angry | 😠 | Displeasure, upset |
| Happy | 😄 | Smiling, cheerful |
| Sad | 😢 | Crying, downcast |
| Fear | 😨 | Shock, scared |
| Surprise | 😲 | Amazed, startled |
| Neutral | 😐 | No emotion |
## 📷 Screen Shots
## 🧠 Tech Stack
Python 3.10,
TensorFlow / Keras,
OpenCV,
Streamlit,
FER-2013 Dataset
## 🙋♂️ Author
Connect on [Instagram](https://instagram.com/theidealcoder)
Follow on [GitHub](https://github.com/parth-udawant)
Made with ❤️ by @theidealcoder
## 📦 Installation
```bash
# 1. Clone the repo
git clone https://github.com/parth-udawant/emotion-detection.git
cd emotion-detection
# 2. Create and activate virtual environment (optional but recommended)
python -m venv venv
# Activate venv:
# On Windows:
venv\Scripts\activate
# On Mac/Linux:
source venv/bin/activate
# 3. Install dependencies
pip install -r requirements.txt
# 4. Run the app
streamlit run app.py