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Developed with React + Electron + FastAPI.\n\n## 📸 Screenshots\n\n### Video Upload Recognition\n![Video Recognition](screenshots/video_recognition.png)\n\n## 👨‍💻 Tech Stack\n\n- Frontend: ReactJS, Electron, FaceAPI.js\n- Backend: Python, FastAPI\n- ML: TensorFlow, PyTorch (VGG16 models)\n\n## 💡 Features\n\n- Real-time webcam-based emotion recognition\n- Upload image/video for analysis\n- Compare 8 emotion models\n- Fully local app (no cloud required)\n\n## 🧠 Datasets Used\n\n- FER2013\n- KDEF\n- NHFI\n- Mixed Dataset\n\n## 🧪 Model Training\n\nEach dataset was used to train two models (TensorFlow + PyTorch). A two-phase transfer learning strategy was used with VGG16 as the base model.\n\n## 📊 Model Results\n\n| Dataset  | Framework  | Accuracy | F1 Score |\n|----------|------------|----------|----------|\n| KDEF     | TensorFlow | 92%      | 0.92     |\n| KDEF     | PyTorch    | 91%      | 0.91     |\n| NHFI     | TensorFlow | 78%      | 0.76     |\n| NHFI     | PyTorch    | 72%      | 0.71     |\n| FER2013  | TensorFlow | 67%      | 0.65     |\n| FER2013  | PyTorch    | 66%      | 0.63     |\n| Mixed    | TensorFlow | 70%      | 0.70     |\n| Mixed    | PyTorch    | 68%      | 0.68     |\n\n## 🚀 How to Run It\n\n### Backend\n```bash\ncd Backend\npip install -r requirements.txt\nuvicorn app.main:app --reload\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fe1washere%2Femotion-recognition-app","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fe1washere%2Femotion-recognition-app","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fe1washere%2Femotion-recognition-app/lists"}