Recipe De-Blogger：以本機 AI 提取食譜要點，減少瀏覽、專心烹飪
Recipe De-Blogger: Less Scrolling, More Cooking with Local AI
Recipe De-Blogger 是一款輕量網頁應用，利用本機運行的語言模型從食譜網頁提取標題、食材和烹調步驟，並在原食譜提供份量時調整食材分量。應用使用 Ollama 支援 AI 輔助提取，亦會從可用的 YouTube 文字稿提取資料；提取結果仍應在烹調前核對。
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
Recipe De-Blogger is a lightweight web app designed to help home cooks get straight to the useful part of an online recipe—without scrolling through lengthy personal stories, advertisements, and unnecessary page content.
Simply paste a recipe URL, and the app extracts the essential details into a clean, easy-to-follow format, including the recipe title, ingredients, and cooking instructions. It also supports ingredient scaling for different serving sizes when the original serving count is available.
The idea is simple: less scrolling, more cooking.
Recipe De-Blogger is especially useful for people who regularly cook from online recipes and want quick access to practical instructions while preparing their meals.
Demo
Code
The project uses a Python/FastAPI backend and a lightweight HTML, CSS, and JavaScript frontend. It extracts structured recipe data from web pages and uses a locally running language model for AI-assisted extraction when standard methods are insufficient.
How I Built It
I built Recipe De-Blogger using a small, practical technology stack:
- Python and FastAPI: Power the backend and its API endpoints.
- HTML, CSS, and JavaScript: Provide a simple, responsive frontend for interacting with the app.
- Recipe-page extraction: Prioritizes structured data, such as Schema.org recipe markup, before falling back to other extraction methods.
- Ollama: Runs an open-weight language model locally to support AI-assisted recipe extraction, including extraction from available YouTube transcripts.
- Recipe scaling logic: Adjusts ingredient quantities for a target serving size when the original serving count is known.
My focus was to keep the experience straightforward: enter a URL, extract the essential recipe details, and adjust the serving size when needed.
Since extraction quality can vary across websites and transcripts, the results should still be reviewed before cooking.
Why Does Open Innovation Matter?
Open innovation allowed me to experiment with AI-assisted extraction without relying on a paid, closed AI API for every recipe request. By running an open-weight model locally through Ollama, I gained greater control over the development process and an opportunity to understand how language models handle real-world, unstructured content.
This approach also keeps the architecture accessible and adaptable. By combining standard web technologies with a locally running model, the extraction workflow can be inspected, tested, and improved as the project evolves.
For a small open-source project, that flexibility is valuable. It allows me to experiment with prompts, refine parsing logic, and improve validation without making the entire application dependent on a single proprietary AI provider.
The Lazy Payoff
The real payoff is reducing the friction between finding a recipe and getting started in the kitchen.
Instead of repeatedly scrolling past lengthy introductions, advertisements, and other page content, users can access the essential recipe details in one organized view. They can then focus on the ingredients, follow the instructions, and adjust quantities when they need to cook for a different number of people.
I haven't made any measured time-saving claims because I haven't conducted a formal timing test. The intended benefit is simple: copy a recipe link, get the important details, and focus on cooking.
Other Team Members
Gaurika Sethi — @gaurikasethi88
來源：DEV Community · dev.to