Burak OdabaşHey devs! 👋 I want to share how I built an AI-powered YouTube video summarizer. Let's dive into the technical implementation!
The Process Flow
- Extract YouTube Video Data
- Get Video Transcript
- Process with ChatGPT
- Present to User
Technical Implementation
1. Fetching Video Metadata
const getVideoMetadata = async (videoUrl) => {
try {
const videoId = extractVideoId(videoUrl);
const response = await youtube.videos.list({
part: 'snippet,contentDetails',
id: videoId
});
return {
title: response.data.items[0].snippet.title,
description: response.data.items[0].snippet.description,
duration: response.data.items[0].contentDetails.duration
};
} catch (error) {
console.error('Failed to fetch metadata:', error);
throw error;
}
};
2. Getting Video Transcript
const getTranscript = async (videoId) => {
try {
const transcript = await YoutubeTranscript.fetchTranscript(videoId);
return transcript.map(item => item.text).join(' ');
} catch (error) {
console.error('Failed to fetch transcript:', error);
throw error;
}
};
3. Processing with ChatGPT
const generateSummary = async (transcript, summaryType) => {
const prompts = {
quick: "Provide a brief 2-3 sentence summary of the main points:",
detailed: "Provide a detailed summary with key points and examples:",
bullets: "List the main points as bullet points:",
};
try {
const completion = await openai.createChatCompletion({
model: "gpt-4",
messages: [{
role: "user",
content: `${prompts[summaryType]} ${transcript}`
}]
});
return completion.data.choices[0].message.content;
} catch (error) {
console.error('Failed to generate summary:', error);
throw error;
}
};
4. Presenting Results
interface SummaryResult {
metadata: VideoMetadata;
transcript: string;
summary: string;
}
const processVideo = async (
videoUrl: string,
summaryType: 'quick' | 'detailed' | 'bullets'
): Promise<SummaryResult> => {
const metadata = await getVideoMetadata(videoUrl);
const transcript = await getTranscript(videoUrl);
const summary = await generateSummary(transcript, summaryType);
return {
metadata,
transcript,
summary
};
};
Key Challenges Solved
- Rate Limiting: Implemented proper delays between API calls
- Error Handling: Added robust error handling for both YouTube and OpenAI APIs
- Prompt Engineering: Optimized prompts for different summary types
- Token Management: Chunking large transcripts for API limits
Results
The app is now live on the App Store, helping users save time by summarizing YouTube videos efficiently. You can check it out here: [https://apps.apple.com/us/app/video-summarize-ai-video-chat/id6692608763]
What's Next?
- Adding support for more languages
- Implementing timestamp-based summaries
- Adding more summary types
Would love to hear your thoughts and suggestions in the comments!
Happy coding! 🚀
