🎯 Multi-Sport AI Prediction Engine

Remote Controller Architecture - Replit serves as lightweight controller, all heavy processing on AWS.

🏗️ Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────────┐
│   Replit        │    │      AWS         │    │    Data Sources     │
│  (Controller)   │    │   (Processing)   │    │                     │
│                 │    │                  │    │                     │
│ • Telegram Bot  │◄──►│ • SageMaker      │◄──►│ • Kaggle Datasets   │
│ • AWS Clients   │    │ • Lambda         │    │ • Sports APIs       │
│ • Validation    │    │ • S3 Storage     │    │ • Real-time feeds   │
│ • Monitoring    │    │ • EC2 Compute    │    │                     │
└─────────────────┘    └──────────────────┘    └─────────────────────┘

🚀 Setup Instructions

1. AWS Prerequisites

# User provides AWS credentials via .env
cp .env.template .env
# Fill in your AWS keys, bucket names, Lambda functions

2. Local Controller Setup

# Lightweight dependencies only
pip install -r requirements.txt

# Start remote controller
python main.py

3. AWS Infrastructure (Deploy separately)

• SageMaker: Train ensemble models (TensorFlow, PyTorch, XGBoost)
• Lambda: Handle predictions and data uploads
• S3: Store Kaggle datasets and trained models
• EC2: Optional for larger model inference

🤖 Telegram Bot Commands

/predict Manchester United vs Liverpool
/compare Djokovic vs Nadal
/train football
/status

📊 Supported Sports

• ⚽ Football (Premier League, La Liga, etc.)
• 🏈 American Sports (NFL, NBA, MLB, NHL)
• 🥊 Combat Sports (UFC, Boxing, MMA)
• 🏏 Cricket (T20, ODI, Test)
• 🎾 Tennis (ATP/WTA)
• 🏉 Rugby (Union/League)

🛡️ Validation Features

✅ Prevents cross-sport comparisons ("Tyson vs Chelsea")
✅ Validates team/player names
✅ Checks sport compatibility
✅ Rejects nonsensical matchups

🔄 Data Pipeline

1. Kaggle → S3: Lambda downloads datasets, uploads to S3
2. S3 → SageMaker: Training jobs read from S3
3. Models → S3: Trained models stored in S3
4. Replit → Lambda: Prediction requests via API calls

⚠️ Important Notes

• NO LOCAL PROCESSING: All ML operations on AWS
• NO SYNTHETIC DATA: Real Kaggle datasets only
• REMOTE CONTROLLER: Replit only manages AWS operations
• USER PROVIDES: AWS keys when ready for deployment

📁 Project Structure

├── src/
│   ├── aws_controller.py      # AWS service management
│   ├── telegram_bot.py        # Bot interface
│   └── sport_validator.py     # Input validation
├── main.py                    # Entry point
├── requirements.txt           # Lightweight deps only
├── .env.template             # AWS config template
└── README.md                 # This file

🎯 Next Steps

1. User provides AWS credentials
2. Deploy AWS infrastructure (SageMaker, Lambda, S3)
3. Configure Telegram bot token
4. Start remote controller: python main.py

Ready for immediate AWS deployment! 🚀