Update README.md

README.md

@@ -9,14 +9,69 @@ app_file: app.py
 pinned: false
 ---
 
-# Menu to Excel Converter (Separate file per image)
+# Menu OCR → Excel (Batch + Validation) — Hugging Face Space
 
-Upload multiple restaurant menu images, extract text via OCR, parse items and prices heuristically, then download a ZIP containing individual Excel files (one per image).
+This package contains a ready-to-deploy Gradio app that processes menu images into Excel files.
 
-## How to use on Hugging Face
-1. Create a new Space (Gradio + Python).
-2. Upload `app.py`, `requirements.txt`, and this `README.md`.
-3. In Space Settings → System Packages, add:
-   - `tesseract-ocr`
-4. Restart the Space.
-5. Use the web UI to upload menu images and download the ZIP of 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:
+    <Store Name>_<Store Code> <Branch Name>.<ext>
+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.