The AI-Powered Task Optimizer is an intelligent system designed to enhance workplace productivity and employee well-being by analyzing emotions through text, speech, and facial expressions. Using machine learning and data science techniques, the system provides real-time emotion detection, personalized task recommendations, and stress management insights, fostering a healthier and more empathetic work environment.

1. Real-Time Emotion Detection

Analyzes text, live video (facial expressions), and speech signals to detect emotions accurately.

2. Task Recommendations

Suggests tasks aligned with the detected emotional state to improve productivity and satisfaction.

3. Historical Mood Tracking

Maintains mood trends over time for individual employees to identify patterns and insights.

4. Stress Management and Alerts

Flags prolonged stress or disengagement and notifies HR or managers for timely interventions.

5. Team Mood Analytics

Aggregates mood data across teams to monitor morale and productivity trends.

6. Data Privacy and Security

Ensures sensitive data is anonymized and stored securely, complying with privacy regulations.

1. Data Collection

Text inputs, live video for facial emotion recognition, and speech recordings for tone analysis.

2. Emotion Detection

Uses NLP, computer vision, and audio signal processing for comprehensive emotion detection.

3. Task Matching

Maps detected emotions to predefined task recommendations.

4. Insights and Alerts

Generates reports and sends alerts for prolonged stress or team morale trends.

Prerequisites

Python 3.10

Libraries:

• transformers
• opencv-python
• librosa
• numpy
• matplotlib
• sounddevice

1. Clone the repository: git clone https://github.com/iamakashjha/AI-Powered-Task-Optimizer

2. Install dependencies: pip install -r requirements.txt

3. Run the application: python main.py

• Launch the application.
• The system will use your webcam for facial emotion detection.
• Enter text inputs for analysis or speak into the microphone for speech emotion detection.
• The system will display detected emotions and recommend tasks.

Based on detected emotions:

• Happy: Collaborative or creative tasks.
• Sad: Light or repetitive tasks.
• Stressed: Mindfulness activities or task prioritization.
• Neutral: Scheduled tasks or skill development.

• Notifications will be sent to HR or managers if prolonged stress or disengagement is detected.

• Facial Expression: Happy
• Text Emotion: Positive (Confidence: 95%)
• Speech Emotion: Neutral

• Lead a brainstorming session with the team.

• Hugging Face for the NLP models.
• OpenCV for computer vision capabilities.
• Librosa for speech emotion analysis.

For questions or support, please reach out to me: