Step 1 - Scoping Phase
Goal: To define a minimum viable product and understand what programing language and APIs to use.
Tools used: claude.ai
Since I am not a programmer, I have no clue how to get started. So I think: why not let AI help me figure out what to do?
Here is the project instruction I gave to claude
You are an experienced software project manager specializing in web app development. You are going to help turning users needs into a product requirement document for a minimal viable product.
This is the prompt I gave to Claude:
I want to build a web app that can summarize the content of a YouTube video. I want to be able to grab a YouTube URL and paste into this app. The app will be able to extract the audio and then turn it into a transcript. Then the app will provide a summary of the transcript.
Could you help me generate a product requirement documents for an MVP that could achieve this? I want you to list all the required tools (APIs) and also provide me with a clear step-by-step development process.
Claude gave me a super complicated response which I had no idea.
Claude's response (don't actually read it)
I’ll help you create a Product Requirements Document (PRD) for your YouTube video summarization web app MVP. Let me break this down into clear sections.
YouTube Video Summarization Web App - MVP PRD
Product Overview
A web application that allows users to input a YouTube video URL and receive an AI-generated summary of the video content by processing its audio transcript.
Core Features (MVP)
-
URL Input Interface
- Simple text input field for YouTube URL
- URL validation to ensure valid YouTube format
- Submit button to initiate processing
-
Video Processing
- YouTube video data extraction
- Audio extraction from video
- Audio-to-text transcription
- Text summarization
-
Results Display
- Display video metadata (title, duration)
- Display generated transcript
- Display summarized content
- Error handling messages
Technical Requirements
Required APIs and Tools
-
YouTube Data API v3
- Purpose: Video metadata retrieval and validation
- Alternative: youtube-dl library for video information extraction
-
Speech-to-Text Service (options)
- OpenAI Whisper API
- Google Cloud Speech-to-Text
- Azure Speech Services
-
Text Summarization Service
- OpenAI GPT API
- Alternative: Cohere or Anthropic Claude API
-
Backend Framework
- Node.js with Express.js
- Alternative: Python with FastAPI
-
Frontend Framework
- React.js with TypeScript
- TailwindCSS for styling
-
Database (Optional for MVP)
- MongoDB for storing processed results
- Alternative: SQLite for simpler implementation
Development Process
Phase 1: Setup and Basic Infrastructure (1-2 weeks)
- Set up development environment
- Initialize frontend and backend projects
- Configure basic API routing
- Implement basic error handling
- Set up CI/CD pipeline
Phase 2: YouTube Integration (1 week)
- Implement URL input and validation
- Integrate YouTube Data API
- Set up video metadata retrieval
- Implement audio extraction functionality
- Add error handling for invalid URLs or unavailable videos
Phase 3: Transcription Service (1-2 weeks)
- Integrate chosen Speech-to-Text API
- Implement audio processing pipeline
- Set up transcript storage
- Add progress indicators for long-running processes
- Implement error handling for failed transcriptions
Phase 4: Summarization Service (1 week)
- Integrate chosen AI summarization API
- Implement text processing pipeline
- Add summary generation functionality
- Implement error handling for failed summarizations
Phase 5: Frontend Development (1-2 weeks)
- Design and implement user interface
- Create responsive layouts
- Add loading states and progress indicators
- Implement error message displays
- Add basic animations and transitions
Phase 6: Testing and Deployment (1 week)
- Conduct unit testing
- Perform integration testing
- Execute user acceptance testing
- Deploy MVP to chosen hosting platform
- Monitor initial usage and gather feedback
Technical Considerations
Rate Limiting and Quotas
- Implement API request throttling
- Monitor API usage and costs
- Set up usage quotas per user (if needed)
Error Handling
- Network connectivity issues
- Invalid video URLs
- API failures
- Processing timeouts
Performance
- Implement caching for processed videos
- Consider implementing queue system for multiple requests
- Optimize API calls to minimize costs
Future Considerations (Post-MVP)
- User authentication
- History of processed videos
- Custom summarization parameters
- Multiple language support
- Share functionality
- API access for other applications
Would you like me to elaborate on any of these sections or provide more specific technical details for any particular component?
I thought this was way too over my head. So I followed up with another prompt:
This seems very complicated. Can you simplify this? Please assume 1 college student are trying to do this project on their own. Use the easiest way to develop this.
Claude's Response (again, don't read it. I will sumarize later)
Ah, you’re right - let me simplify this for a solo student project. Here’s a streamlined version that focuses on getting a working MVP with minimal complexity:
Simplified YouTube Video Summarizer - Student Project
Core Features
- Simple webpage with a YouTube URL input
- Get video transcript
- Generate a summary
- Display results
Technology Stack (Simplified)
-
Frontend: Plain HTML, CSS, JavaScript
- No need for React or complex frameworks
- Just create a simple form with vanilla JavaScript
-
Backend: Python with Flask
- Flask is easier to learn and set up compared to Node.js
- Perfect for simple API endpoints
- Great Python library support for our needs
-
Key APIs/Libraries:
youtube-transcript-api: Gets transcripts directly from YouTube- OpenAI API: For text summarization using GPT
- That’s it! No need for complex speech processing
Step-by-Step Development Process
1. Setup (1 day)
- Install Python and required packages:
pip install flask youtube-transcript-api openai python-dotenv
- Create a basic project structure:
youtube-summarizer/
├── app.py
├── static/
│ ├── style.css
│ └── script.js
└── templates/
└── index.html
2. Basic Frontend (1-2 days)
Create a simple HTML page with:
- URL input field
- Submit button
- Area to display results
- Basic CSS for styling
3. Backend Development (2-3 days)
- Set up Flask server
- Create two main endpoints:
/get_transcript: Gets YouTube transcript/summarize: Summarizes the transcript
4. Integration (1-2 days)
- Connect frontend to backend
- Add error handling
- Add loading states
Sample Code Structure
Backend (app.py):
from flask import Flask, request, jsonify
from youtube_transcript_api import YouTubeTranscriptApi
import openai
app = Flask(__name__)
@app.route('/get_transcript', methods=['POST'])
def get_transcript():
video_url = request.json['url']
video_id = extract_video_id(video_url) # You'll need to write this function
transcript = YouTubeTranscriptApi.get_transcript(video_id)
return jsonify(transcript)
@app.route('/summarize', methods=['POST'])
def summarize_text():
text = request.json['text']
summary = get_summary(text) # Using OpenAI API
return jsonify({'summary': summary})
if __name__ == '__main__':
app.run(debug=True)
Frontend (index.html):
<!DOCTYPE html>
<html>
<head>
<title>YouTube Video Summarizer</title>
<link rel="stylesheet" href="/static/style.css">
</head>
<body>
<div class="container">
<h1>YouTube Video Summarizer</h1>
<input type="text" id="url-input" placeholder="Enter YouTube URL">
<button onclick="processVideo()">Get Summary</button>
<div id="result"></div>
</div>
<script src="/static/script.js"></script>
</body>
</html>
Required API Keys
- OpenAI API key (for GPT)
- Sign up at OpenAI’s website
- Get API key
- Free credits for testing
Development Tips
- Start with a single video URL to test
- Use videos during development
- Print intermediate results to debug
- Test with various types of videos
- Keep the UI simple - focus on functionality
Common Issues to Watch For
- Very long videos might cost more API credits
- Some YouTube videos don’t have transcripts
- API rate limits
- Network timeouts
Estimated Timeline
- Total time: 1-2 weeks for basic working version
- Additional week for refinements and bug fixes
This simplified version:
- Requires minimal setup
- Uses fewer dependencies
- Is easier to debug
- Can be completed by one person
- Still delivers core functionality
Would you like me to explain any part in more detail or provide more specific code examples?
From the response, I learned that the core features are:
- Simple webpage with a YouTube URL input
- Get video transcript
- Generate a summary
- Display results
The key API and library I need to use are:
youtube-transcript-api: Gets transcripts directly from YouTube- OpenAI API: For text summarization using GPT
The programing languages are:
Frontend: Plain HTML, CSS, JavaScript
Backend: Python with Flask
Okay that’s easy enough. The only thing I didn’t agree with by looking at this response is that: youtube-transcription-api is only extracting transcript/subtitles for a given video. Ideally I want my app to be able to transcribe any video. But for an MVP, I will take that.
It’s always important to remember the goal: get to a functional MVP. Then we can add more features.
I’m pretty happy with this response. So I asked Claude to help me generate a prompt for Replit:
This is awesome. I just want you to re-write the core features and technology stack into a single paragraph AI prompt so I can hand it to Replit to build my prototype
Here is Claude’s response:
Here’s a clear prompt for Replit to build your YouTube video summarizer:
Create a simple web application that takes a YouTube video URL as input and generates a summary of its content. The app should have a single HTML page with a text input field for the URL and a submit button. Use Python with Flask for the backend, and vanilla JavaScript for the frontend. The core functionality should: 1) accept a YouTube URL, 2) fetch the video transcript using the youtube-transcript-api library, 3) use the OpenAI API to generate a summary of the transcript, and 4) display both the transcript and summary on the page. Keep the UI minimal with basic CSS styling. The app should handle errors gracefully, such as invalid URLs or missing transcripts. Include loading states to show when the app is processing. Use environment variables to store the OpenAI API key securely.
Step 1 Goal achieved!
That’s all for this update. No coding has been done yet. Will start that tomorrow!
Fun fact: I was trying to post this but got this error message 
:
