Text Normalizer

😫 The Pain Point

Survey results come in: “nguyen van a”, “NGUYEN VAN B”, ” Nguyen Van C ”. You can’t print certificates with messy capitalization.

🚀 Agentic Solution

The Beautifier: Applies grammar rules to names.

Key Features:

• Title Case: Converts nguyen van a -> Nguyen Van A.
• Trim: Removes double spaces.

⚔️ Phase 1: Commander (Quick Fix)

For a quick cleanup.

Prompt:

“Read names.txt. Convert every line to Title Case (first letter capitalized). Remove any leading/trailing whitespace. Save to clean_names.txt.”

Result: A clean list.

🏗️ Phase 2: Architect (Permanent Tool)

For Admin Assistants.

Engineering Prompt:

**Role:** Python Data Cleaner
**Task:** Create a "Customer Name Normalizer".
**Requirements:**
1.  **GUI:**
    *   Select Input Excel.
    *   Select Target Column.
    *   "Normalize" button.
2.  **Logic:**
    *   `str.title()` (Capitalize first letters).
    *   `str.strip()` (Remove outer spaces).
    *   regex `\s+` -> ` ` (Remove double spaces).
    *   Overwrite column and save.
3.  **Deliverables:** `text_normalize.py`, `run.bat` (Windows), `run.sh` (Mac).

🧠 Prompt Decoding

• Spaces: “Nguyen Van A” and “Nguyen Van A ” (space at end) are different to a computer. The generic term “Normalize” in prompt engineering usually implies trimming these invisible chars.

🛠️ Instructions

1. Copy Prompt -> Paste -> Run.
2. Select File -> Clean.