Develop a real-time emotion detection system that operates on streaming video data and identifies the predominant emotion in each frame.

• README.md - Updated to include project details and instructions.
• null_5 (1).ipynb - Jupyter notebook for emotion detection analysis.
• Task 5 Report.pdf - Detailed report of Task 5.
• null 5 app.py - Python script for the real-time emotion detection app.
• null 5 accuracy.png - Accuracy metrics of the emotion detection model.
• null 5 confusion matrix.png - Confusion matrix showing model performance.
• null 5 loss.png - Loss graph of the model training process.
• .gitignore - Git ignore file.
• LICENSE - Project license.

Here are some sample images from the system in action:

• Real-Time Processing: The system processes streaming video data to detect and classify emotions in real-time.
• Emotion Identification: Identifies predominant emotions in each frame of the video stream.
• User-Friendly Interface: Integrated into a Python application for easy use and deployment.

• Technologies Used: TensorFlow, Keras, OpenCV, NumPy, Pandas, Streamlit.
• Models: Utilized deep learning models for emotion recognition.
• Performance Metrics: Includes accuracy, confusion matrix, and loss graphs to evaluate the system's performance.

Feel free to explore the provided files and demo images to understand the system's capabilities and implementation!