README.md

metadata

title: AI Task Assignment System
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit

🧠 AI Task Assignment System

A self-learning task assignment engine that automatically optimizes team productivity by learning from real task completion results.

🎯 What This System Does

• Enter People & Tasks: Add team members and work items
• AI Decides Assignments: Optimal matching based on learned patterns
• System Learns: From real completion results (time, quality)
• Gets Smarter: Continuous improvement with each task

✨ Key Features

• Zero-bias assignments based on real performance data
• Universal application - works for any task type (coding, design, research, etc.)
• Real-time progress tracking with notes and updates
• Automatic skill discovery - learns who's good at what
• Burnout prevention through workload analysis
• Self-improving AI that gets better with more data

🚀 How to Use

1. Add Users: Start with your team members
2. Add Tasks: Enter work items with complexity (0-1) and deadline (hours)
3. Get Assignments: AI recommends optimal person for each task
4. Track Progress: Update task progress and add notes
5. Complete Tasks: Enter time taken and quality score (1-5)
6. Retrain AI: System learns and improves future assignments

🧠 The Learning Process

Initially assigns tasks randomly (no data), but learns from every completion:

• User skill patterns
• Task complexity preferences
• Time efficiency trends
• Quality consistency
• Workload capacity

📊 Real-World Applications

• Software Teams: Frontend, backend, testing assignments
• Study Groups: Subject-based task distribution
• Project Management: Optimal resource allocation
• Any Team Environment: Universal skill-based matching

🔄 Self-Learning Cycle

Add People & Tasks → AI Assigns → Work Completed → 
Enter Results → AI Learns → Better Assignments

Result: Maximum efficiency, minimum burnout, automatic skill discovery.

🛠️ Technical Details

• AI Model: Random Forest Regressor (scikit-learn)
• Features: User ID, task complexity, deadline pressure
• Target: Success score (quality × efficiency)
• Framework: Gradio for web interface
• Data: CSV files for users, tasks, results, JSON for progress tracking