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--- |
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title: Menu to Excel Converter |
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emoji: 📊 |
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colorFrom: blue |
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colorTo: green |
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sdk: gradio |
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sdk_version: 5.49.1 |
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app_file: app.py |
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pinned: false |
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--- |
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# Menu OCR → Excel (Batch + Validation) — Hugging Face Space |
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This package contains a ready-to-deploy Gradio app that processes menu images into Excel files. |
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Files included: |
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- `app.py` — Gradio application (batch processing + validation) |
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- `requirements.txt` — Python dependencies for the Space |
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Filename format (recommended for automatic metadata extraction) |
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-------------------------------------------------------------- |
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Images should be named like: |
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<Store Name>_<Store Code> <Branch Name>.<ext> |
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Example: |
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Fortis Hospital_60247010108 Rohini.jpg |
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The app extracts: |
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- A1 = Store Name (e.g., "Fortis Hospital") |
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- B1 = Store Code (e.g., "60247010108") |
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- C1 = Branch Name (e.g., "Rohini") |
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How to use (UI steps) |
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--------------------- |
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1. Create a Hugging Face Space: SDK = Gradio, Runtime = Python 3.10. |
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2. Upload `app.py` and `requirements.txt` to the Space files area. |
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3. Open the Space UI after build completes. |
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4. In the UI: |
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- Upload multiple menu images (left) and a single Excel template (.xlsx). |
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- Click "Parse all images". |
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- Select a parsed image from the dropdown to review. |
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- Edit the extracted table if needed and click "Save current edits". |
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- When finished, click "Download ZIP of all (use after saving/edits)" to get all generated Excel files. |
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Output format |
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------------- |
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Each generated .xlsx is a copy of your uploaded template with: |
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- Row 1: metadata (A1 Store Name, B1 Store Code, C1 Branch Name) |
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- Row 2: your existing headers (unchanged) |
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- Row 3 onward: parsed menu items mapped into columns A..S: |
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A: Parent Category |
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B: Category |
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C: Name |
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D: Item Code |
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E: Master Item Name |
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F: EAN Code |
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G: Price |
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H: Active |
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I: Priority |
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J: Image |
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K: Food type |
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L: NoOfMains |
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M: OnlineName |
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N: AlternateClassification |
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O: ItemTaxInclusive |
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P: TaxPct |
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Q: BrandName |
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R: ClassificationCode |
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S: HSN Code |
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Notes & troubleshooting |
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----------------------- |
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- 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. |
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- For better OCR accuracy, use high-resolution, well-lit images. |
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- To adjust price parsing, edit `PRICE_REGEX` inside `app.py`. |
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- To improve category detection, edit `CATEGORY_HINTS` inside `app.py`. |
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If you want me to bundle these files into a zip here, reply "please zip" and I will produce the downloadable package. |
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