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YouTube ML Performance Predictor - System Startup Guide

🚀 Complete System Startup Commands

1. Activate Python Environment

cd "C:\Github\Youtube Project"
& "C:/Github/Youtube Project/venv/Scripts/Activate.ps1"

2. Start Dashboard API Server (Port 8000)

cd "C:\Github\Youtube Project"
python src/api_server.py

This serves the dashboard data and main analytics.

3. Start Prediction API Server (Port 8002)

cd "C:\Github\Youtube Project"
python src/prediction_api.py

This serves the ML-powered video performance predictions.

4. Start Frontend Development Server (Port 3001)

cd "C:\Github\Youtube Project\frontend"
npm run dev

This serves the React frontend with Vite.

📋 Complete Step-by-Step Setup

Terminal 1 - Dashboard API:

cd "C:\Github\Youtube Project"
& "C:/Github/Youtube Project/venv/Scripts/Activate.ps1"
python src/api_server.py

Expected output: 🚀 Starting YouTube Extractor API server...

Terminal 2 - Prediction API:

cd "C:\Github\Youtube Project"
& "C:/Github/Youtube Project/venv/Scripts/Activate.ps1"
python src/prediction_api.py

Expected output: 🔄 Loading ML models... followed by 🚀 Starting YouTube Performance Prediction API...

Terminal 3 - Frontend:

cd "C:\Github\Youtube Project\frontend"
npm run dev

Expected output: Vite dev server starting on http://localhost:3001

🌐 Access URLs

Once all services are running:

🔍 Quick Health Check Commands

# Check if all ports are active
netstat -an | findstr ":3001 :8000 :8002"

# Test prediction API
curl -X GET "http://localhost:8002/api/test"

# Test dashboard API
curl -X GET "http://localhost:8000/api/status"

🛠️ Troubleshooting Commands

If you need to kill existing processes:

# Find processes using the ports
netstat -ano | findstr ":8000"
netstat -ano | findstr ":8002"
netstat -ano | findstr ":3001"

# Kill specific process (replace PID with actual process ID)
taskkill /PID [PID_NUMBER] /F

📝 Service Dependencies

Order matters: Start services in this order:

  1. Dashboard API (8000) - Provides data
  2. Prediction API (8002) - Provides ML predictions
  3. Frontend (3001) - Consumes both APIs

The frontend is configured with proxy rules to route:

  • /api/predict → Prediction API (port 8002)
  • /api/* → Dashboard API (port 8000)

All services should show startup messages confirming they're ready before proceeding to the next one! 🎉

🎯 Features Available

Once all services are running, you'll have access to:

  • 📊 Data Dashboard - View extracted YouTube channel and video analytics
  • 🤖 ML Predictions - AI-powered video performance predictions
  • 🖼️ Thumbnail Analysis - Upload and analyze video thumbnails
  • 🏷️ Smart Tags - Get recommended tags based on content analysis
  • 📈 Performance Metrics - Views, RQS, and CTR predictions
  • 🎨 Dark/Light Mode - Toggle between visual themes
  • 📱 Responsive Design - Works on desktop and mobile devices

💡 Tips

  • Keep all three terminals open while using the application
  • The prediction API may take a few seconds to load all ML models on first startup
  • If you encounter port conflicts, use the troubleshooting commands to kill existing processes
  • The frontend will automatically proxy API requests to the correct backend services