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Last Updated
Jan 20, 2026
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About The Software
🚀 About the Project This Emotion Detection Webapp is a full-stack AI solution designed to recognize human facial expressions. Using Computer Vision and Deep Learning, the app classifies images into categories such as Happy, Sad, Angry, and Neutral.
🛠️ Tech Stack ● Frontend: HTML5, CSS3 (Custom Responsive Dashboard), Jinja2 ● Backend: Flask (Python) ● AI Engine: TensorFlow, Keras (MobileNetV2 Architecture) ● Processing: OpenCV (Haar Cascades for face detection)
🧠 Engineering Highlights Deploying a large AI model on a limited 512MB RAM environment (Render) presented a significant challenge. I successfully optimized the application by: 1. Lazy Loading: Initializing the neural network only on-demand to save idle memory. 2. TensorFlow-CPU: Utilizing a lightweight library variant to reduce the server footprint. 3. Image Optimization: Implementing grayscale conversion and precise ROI (Region of Interest) cropping for faster inference.
Key Features
- MobileNetV2 Deep Learning Model
- Real-time Emotion Classification
- Responsive Flask Dashboard
- Secure SQLite History Logging
System Requirements
- Modern Web Browser (Chrome, Firefox, Safari)
- Stable Internet Connection
How It Works
- 1Enter your name in the dashboard
- 2Upload a clear portrait photo
- 3Click Detect Emotion to see the AI analysis results
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Game Info
Developer
olayinka_igePlatform
Web BrowserVersion
1.0Published
Jan 20, 2026Size
0 GBAge Rating
All AgesLicense
Open SourceConnect
Tags
Verified
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