✔ Professional ✔ GitHub-friendly ✔ Includes screenshots placeholder ✔ Installation guide ✔ Features list ✔ Tech stack ✔ File structure ✔ Future roadmap ✔ License (MIT)

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✅ Your Complete README.md

https://github.com/sridhar-panigrahi/recipe_01/blob/main/README.md

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🧪 PantryClean — Recipe Ingredient Normalizer

PantryClean is a client-side recipe parsing tool that converts messy ingredient text into a structured format.
It automatically detects quantities, units, ingredient names, health alternatives, and ambiguity flags — while keeping everything stored locally in the browser.

⚡ No backend. No database. No tracking. Everything happens locally.

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🍽️ What PantryClean Does

Feature	Status
Parse free-text recipe ingredients	✅
Unit normalization (ml/g/cups/oz/etc.)	✅
Detect ambiguous instructions (like "pinch", "to taste")	✅
Autofill common interpretations (eggs, onions, cloves, bunches)	✅
Editable ingredient corrections	✅
Export to CSV, JSON, clipboard	✅
Built-in sample recipes	✅
Settings for unit system & precision	✅
LocalStorage persistence (no server)	✅
Responsive UI with dark/light theme	✅

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🏗️ Tech Stack

Layer	Technology
UI	HTML5 + CSS (custom styling, no framework)
Logic	Vanilla JavaScript
Storage	LocalStorage
UX	Tooltip parsing, modals, keyboard shortcuts
CSS Features	CSS variables, accessibility rules, responsive layout

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📁 Project Structure

PantryClean/ │ ├── index.html # Landing page ├── app.html # Main recipe normalizing app ├── styles.css # Full UI styling (~500 lines) ├── app.js # Core parsing engine + UI logic └── README.md # You’re reading this 😄

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🚀 Getting Started

1️⃣ Clone the repository:

git clone https://github.com/yourusername/PantryClean.git
cd PantryClean

2️⃣ Run locally:

No build step needed — just open:

index.html

in your browser.

3️⃣ (Optional) Run with a local server:

npx serve

or:

python3 -m http.server

Then visit:

http://localhost:8000


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🧠 How Parsing Works

PantryClean uses multiple detection layers:
	•	Unicode fraction handling
	•	Regex quantity extraction
	•	Unit synonym mapping (e.g., tbsp, tablespoon, T → tablespoon)
	•	Ingredient cleanup
	•	Ambiguity scoring
	•	Conversion rules based on user settings
	•	Local corrections override future parsing

Example:

"2 tbsp chopped fresh coriander"
↓
Qty: 30 ml
Unit: ml
Ingredient: coriander
Flags: none


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⚙️ Settings

Users can customize:
	•	Metric / Imperial formats
	•	Preferred volume unit (ml, cups, tablespoon, etc.)
	•	Preferred weight (g, oz, lb, etc.)
	•	Decimal precision
	•	Handling tiny measurements (pinch, <15ml, etc.)

All preferences are saved automatically.

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💾 Data Persistence
	•	Saved parsing corrections are stored under:

localStorage.pantryClean_corrections

	•	Settings are stored under:

localStorage.pantryClean_settings

No backend is used — everything lives on the user’s device.

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🧰 Keyboard Shortcuts

Shortcut	Action
Ctrl + Enter	Parse ingredients
Esc	Close active modal
S	Open Settings


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📤 Export Options

Format	Example Use
CSV	Meal planning, spreadsheets
JSON	Grocery apps, automation
Clipboard text	Quick sharing or notes


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🧩 Future Improvements (Roadmap)
	•	AI category tagging (vegetable, protein, spice, dairy, etc.)
	•	Shopping list grouping by aisle
	•	OCR support for printed recipes
	•	Multi-language ingredient dictionary
	•	Nutrition estimation API mode

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📸 Screenshots

(Add images here after deployment)

![Landing Page](images/landing.png)
![Parsed Ingredients](images/results.png)
![Settings Panel](images/settings.png)


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🧑‍💻 Contributing

Pull requests are welcome!
Open an issue for bugs, enhancements, or new feature proposals.

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📜 License

MIT License — free to use, modify, and distribute.

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🌟 Credits

Built by Shridhar Panigrahi
Designed for clean cooking workflows, automation, and digital recipe storage.

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Deployed link 

> https://ojt-recipeunitnormalizer.vercel.app/