Video Summarizer

AI-powered tool to transform video transcripts into structured summaries with key takeaways, quotes, and insights.

🔗 Live Demo: summarizer.thedscs.com

🚀 Quick Start

# Clone the repository
git clone https://github.com/dshrp/video-summarizer.git
cd video-summarizer

# Install dependencies
npm install

# Set up environment variables (see below)
cp .env.example .env.local

# Run development server
npm run dev

Open http://localhost:3000 in your browser. 📋 Prerequisites

Node.js 18+ and npm OpenAI API key (for AI analysis) Vercel account (for deployment)

⚙️ Environment Setup Create a .env.local file in the root directory:

# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key_here
OPENAI_MODEL=gpt-4-turbo-preview

# Optional: Rate limiting
RATE_LIMIT_MAX_REQUESTS=10
RATE_LIMIT_WINDOW_MS=60000

Getting OpenAI API Key

Visit platform.openai.com/api-keys Create a new API key Add billing information (pay-per-use pricing) Copy the key to your .env.local file

Cost Estimate: ~$0.01-0.05 per transcript analysis 🏗️ Project Structure

video-summarizer/
├── components/
│   ├── ui/                 # Shadcn/ui components
│   └── summarizer/         # Main summarizer component
├── lib/
│   ├── openai.js          # OpenAI integration
│   ├── transcript-analyzer.js  # Content analysis logic
│   └── utils.js           # Helper functions
├── pages/
│   ├── api/
│   │   └── analyze.js     # API endpoint for transcript analysis
│   └── index.js           # Main page
├── public/                # Static assets
└── styles/               # Global styles

🔧 Core Features Transcript Analysis Pipeline

File Upload: Accepts .txt transcript files Content Extraction: Parses speakers, topics, and key phrases AI Analysis: Uses OpenAI to generate structured summaries Output Formatting: Creates organized takeaways and insights

Analysis Components

Pattern Recognition: Identifies adoption patterns, strategies, challenges Quote Extraction: Finds meaningful quotes and insights Tool Detection: Recognizes mentioned technologies and platforms Content Categorization: Organizes insights by theme

🚀 Deployment Deploy to Vercel Connect to Vercel:

npm install -g vercel
vercel login
vercel

Set Environment Variables:

Go to Vercel Dashboard → Project → Settings → Environment Variables Add your OPENAI_API_KEY

Custom Domain (optional):

Add your domain in Vercel Dashboard → Domains Update DNS records as instructed

Deploy to Other Platforms The app is a standard Next.js application and can be deployed to:

Netlify: Add build command npm run build && npm run export Railway: Connect GitHub repo and set environment variables DigitalOcean App Platform: Use the Next.js preset

🔨 Development Available Scripts

Available Scripts
npm run dev          # Start development server
npm run build        # Build for production
npm run start        # Start production server
npm run lint         # Run ESLint
npm run type-check   # Run TypeScript checks

Adding New Analysis Features

Extend the analyzer in lib/transcript-analyzer.js:

const customPattern = /your-regex-pattern/gi;
// Add to extractTakeaways() function

Modify the prompt in lib/openai.js:

const systemPrompt = `
Your existing prompt...
Additional instructions for new feature...
`;

Update the UI in components/summarizer/ to display new insights

Customizing the UI

Styling: Uses Tailwind CSS classes Components: Built with Shadcn/ui components Theme: Modify colors in tailwind.config.js

🛡️ Rate Limiting & Security The app includes basic rate limiting:

// pages/api/analyze.js
const rateLimit = {
  max: process.env.RATE_LIMIT_MAX_REQUESTS || 10,
  windowMs: process.env.RATE_LIMIT_WINDOW_MS || 60000
};

Production Recommendations:

Implement user authentication Add request logging Set up monitoring for API usage Configure CORS properly

🐛 Troubleshooting Common Issues OpenAI API Errors:

Error: 401 Unauthorized

Check your API key is valid and has billing enabled

Build Failures:

Module not found: '@/components/ui/...'

Ensure all Shadcn/ui components are properly installed

Rate Limiting:

Implement exponential backoff for API calls Consider caching results for identical transcripts

Debug Mode Enable detailed logging:

DEBUG=true
LOG_LEVEL=debug

🤝 Contributing

Fork the repository Create a feature branch: git checkout -b feature/new-feature Commit changes: git commit -m 'Add new feature' Push to branch: git push origin feature/new-feature Submit a pull request

Development Guidelines

Follow the existing code style Add tests for new features Update documentation for API changes Ensure environment variables are documented

🆘 Support

Issues: GitHub Issues Questions: daniel@thedscs.com Documentation: Wiki

Built by Daniel Sharp | DSCS