README.md

metadata

title: Menu to Excel Converter
emoji: 📊
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false

Menu OCR → Excel (Batch + Validation) — Hugging Face Space

This package contains a ready-to-deploy Gradio app that processes menu images into Excel files.

Files included:

• app.py — Gradio application (batch processing + validation)
• requirements.txt — Python dependencies for the Space

Filename format (recommended for automatic metadata extraction)

Images should be named like: _ . Example: Fortis Hospital_60247010108 Rohini.jpg

The app extracts:

• A1 = Store Name (e.g., "Fortis Hospital")
• B1 = Store Code (e.g., "60247010108")
• C1 = Branch Name (e.g., "Rohini")

How to use (UI steps)

1. Create a Hugging Face Space: SDK = Gradio, Runtime = Python 3.10.
2. Upload app.py and requirements.txt to the Space files area.
3. Open the Space UI after build completes.
4. In the UI:
   • Upload multiple menu images (left) and a single Excel template (.xlsx).
   • Click "Parse all images".
   • Select a parsed image from the dropdown to review.
   • Edit the extracted table if needed and click "Save current edits".
   • When finished, click "Download ZIP of all (use after saving/edits)" to get all generated Excel files.

Output format

Each generated .xlsx is a copy of your uploaded template with:

• Row 1: metadata (A1 Store Name, B1 Store Code, C1 Branch Name)
• Row 2: your existing headers (unchanged)
• Row 3 onward: parsed menu items mapped into columns A..S: A: Parent Category B: Category C: Name D: Item Code E: Master Item Name F: EAN Code G: Price H: Active I: Priority J: Image K: Food type L: NoOfMains M: OnlineName N: AlternateClassification O: ItemTaxInclusive P: TaxPct Q: BrandName R: ClassificationCode S: HSN Code

Notes & troubleshooting

• Tesseract OCR must be installed on the host. If you get a Tesseract error, install system Tesseract or ask me to provide a transformer-based fallback.
• For better OCR accuracy, use high-resolution, well-lit images.
• To adjust price parsing, edit PRICE_REGEX inside app.py.
• To improve category detection, edit CATEGORY_HINTS inside app.py.

If you want me to bundle these files into a zip here, reply "please zip" and I will produce the downloadable package.