Below is a summary of a long conversation between me and ChatGPT to develop a code that takes Youtube links, and outputs summaries of the video.
The actual conversations prompts are too long to put here on leothreads, so I decided to post the summary only.
Project Overview: YouTube Transcript Summarization
You are developing a Python application to fetch YouTube video transcripts and summarize them using Hugging Face models via Gradio. The application allows users to input a YouTube URL, retrieves the transcript, and generates a concise summary along with bullet points.
Initial Stage: Basic Functionality
Starting Point
You began with a basic structure that included fetching transcripts using the
youtube_transcript_api. The application could retrieve the transcript but lacked robust error handling and flexibility in choosing models for summarization.transcript = YouTubeTranscriptApi.get_transcript(video_id)Key Features
Improvement Stage 1: Error Handling and User Input
Enhancements
You introduced error handling for summarization, allowing the application to retry upon failure. Additionally, you enabled users to quit or retry if an error persisted.
while True: try: summary = summarize_transcript(transcript) break # Exit the loop if successful except Exception as e: user_input = input("Press any key to retry or type 'q' to quit: ").strip().lower() if user_input in ['q', 'quit']: sys.exit()Improvement Stage 3: Integration with Hugging Face Token
Token Management
You enhanced the application by integrating a Hugging Face token management system, allowing users to authenticate and access the models more securely.
from dotenv import load_dotenv load_dotenv() hf_token = os.getenv("HUGGINGFACE_TOKEN") client = Client("client_name", hf_token=hf_token)Final Stage: File Management
Saving Summaries
You implemented functionality to save the generated summaries to text files, naming them based on the first part of the summary or the video ID.
filename = f"{sanitized_filename}.txt" with open(filename, 'w', encoding='utf-8') as f: f.write(summary)Conclusion
Your project has evolved from a simple transcript fetcher to a robust summarization tool capable of handling multiple models and providing a user-friendly interface. The integration of error handling, token management, and file output has made the application more functional and versatile for users seeking concise video summaries.