🧠 EmotiSense AI: Real-time Emotion Intelligence System

EmotiSense AI is a comprehensive, production-ready emotion detection and analytics platform. Built with Python, OpenCV, and Deep Learning (FER/DeepFace), it provides real-time facial expression analysis from webcams, video files, and RTSP streams.

(Example Illustration)

🚀 Features

• Multi-Source Support: Real-time webcam, uploaded video files, and CCTV/IP camera (RTSP) streams.
• Dual Model Engine:
  • FER: Lightweight, high-speed detection (best for real-time).
  • DeepFace: High-accuracy analysis with multiple backends (SSD, MTCNN, RetinaFace).
• Advanced Tracking: Persistent face tracking across frames using IoU-based centroid assignment.
• Intelligent Alerts: Real-time warnings for high anger levels and crowd mood shifts.
• Analytics Dashboard: Dynamic charts for emotion distribution, confidence trends, and face counts.
• Automated Logging: Every detection event is saved to thread-safe CSV logs.
• Report Generation: Export session summaries (TXT) and combined logs (CSV).
• Modern UI: Sleek, dark-themed Streamlit interface with responsive design.

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🛠️ Installation & Setup

1. Clone the Repository

git clone https://github.com/your-username/emotisense-ai.git
cd emotisense-ai

2. Create Virtual Environment (Recommended)

python -m venv venv
venv\Scripts\activate  # Windows
source venv/bin/activate  # Linux/Mac

3. Install Dependencies

pip install -r requirements.txt

4. Run the Application

streamlit run app.py

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📂 Project Structure

sentiment-analysis/
├── assets/             # UI assets and CSS
│   └── style.css       # Custom dark theme
├── data/               # Persistent storage
│   ├── logs/           # Session CSV logs
│   └── snapshots/      # Face snapshots (optional)
├── modules/            # Core logic
│   ├── alerts.py       # Alert system
│   ├── analytics.py    # Charting & stats
│   ├── detector.py     # Emotion detection (FER/DeepFace)
│   ├── logger.py       # CSV logging logic
│   ├── tracker.py      # Face tracking
│   └── video_processor.py # OpenCV pipeline
├── utils/              # Helper utilities
│   ├── drawing.py      # Annotation logic
│   └── helpers.py      # File & frame utils
├── app.py              # Main Streamlit frontend
├── config.py           # Central configuration
└── requirements.txt    # Project dependencies

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📊 Analytics & Metrics

The system tracks seven core emotions:

• 😊 Happy | 😢 Sad | 😡 Angry | 😐 Neutral
• 😨 Fear | 😲 Surprise | 🤢 Disgust

Advanced Capabilities

• Emotion Trend Graph: Visualizes how confidence levels change over time.
• Crowd Mood Analysis: Aggregates individual emotions to determine the overall vibe.
• Alert System: Triggers "Critical" alerts when the number of angry faces exceeds user-defined thresholds.

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🔧 Deployment Steps

Streamlit Cloud

1. Push your code to a GitHub repository.
2. Log in to Streamlit Cloud.
3. Connect your repo and select app.py as the main entry point.
4. Add any necessary environment variables in the Streamlit Dashboard.

Docker (Optional)

Create a Dockerfile:

FROM python:3.10-slim
RUN apt-get update && apt-get install -y libgl1-mesa-glx libglib2.0-0
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

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🔮 Future Improvements

• Face Recognition: Integrate with a database to identify specific individuals.
• Gaze Tracking: Analyze where users are looking for heatmaps.
• Cloud DB Integration: Sync logs to MongoDB or PostgreSQL for long-term storage.
• Model Quantization: Optimize DeepFace models for Edge devices (Raspberry Pi/Jetson).
• Audio-Visual Sentiment: Combine facial expressions with voice tone analysis.

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📄 License

MIT License - See LICENSE for details.