This project demonstrates how to enhance a personal blog through automated content summarization. It uses a Large Language Model (LLM) to generate concise and engaging summaries of blog articles. The system retrieves a blog URL, extracts text content, and produces a short, humorous summary that helps users quickly understand the main ideas.
This system uses the GPT-5-Nano model via the OpenAI API. The workflow receives a blog URL from the user, extracts content with BeautifulSoup4, and generates a lighthearted summary. Finally, the application is deployed using Streamlit Community Cloud.
- Environment Setup – Initialize the project structure.
- Data Collection – Fetch blog content using the provided URL.
- Data Preprocessing – Clean and prepare the extracted text.
- Summarization Pipeline –
- Extract data
- Process text
- Generate summary
- Cloud Deployment – Publish the app on Streamlit Community Cloud.
- Python 3.11.9
- Streamlit Cloud Community account (Free Tier)
- OpenAI API key (Paid)
Install the dependencies before running the project:
pip install -e .