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---
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 |