Add xlsxwriter-based Excel generation scripts with openpyxl implementation

- Created create_excel_xlsxwriter.py and update_excel_xlsxwriter.py
- Uses openpyxl exclusively to preserve Excel formatting and formulas
- Updated server.js to use new xlsxwriter scripts for form submissions
- Maintains all original functionality while ensuring proper Excel file handling

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

llm_prompt_retail_media.md

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+# 🧠 LLM Prompt – Retail Media Calculation Agent
+
+## Purpose
+
+You are a smart data agent. Your job is to:
+
+1. **Extract input values** from the existing form ( `index.html`).
+2. **Read constants and formulas** from an existing `config.json`.
+3. **Normalize input**:
+   - For any question that asks for a percentage (e.g., "percentage of stores with screens"), **divide that value by 100** before using it in calculations.
+4. **Apply the formulas** to calculate the following metrics and **insert the values into `results.json`** under the following keys:
+
+```json
+{
+  "potential_reach_in_store": <calculated_value>,
+  "unique_impressions_in_store": <calculated_value>,
+  "potential_reach_on_site": <calculated_value>,
+  "unique_impressions_on_site": <calculated_value>,
+  "potential_reach_off_site": <calculated_value>,
+  "unique_impressions_off_site": <calculated_value>
+}
+```
+
+---
+
+## 🔢 Formulas
+
+- **% stores with retail media**  
+  `= min(stores_with_screens, stores_with_radio) + abs(stores_with_screens - stores_with_radio) / 2`
+
+- **potential_reach_in_store**  
+  `= (transactions × % stores with retail media / frequency) × visitor_coefficient`
+
+- **unique_impressions_in_store**  
+  `= ((dwell_time + 60 × ad_duration) × frequency × capture_rate_screen × paid_screen × screen_count) + ((dwell_time + 60 × ad_duration) × frequency × (radio_percentage / 0.5) × paid_radio)`
+
+- **potential_reach_on_site**  
+  `= (website_visits × (1 - website_bounce_rate) / website_frequency) + (app_users × (1 - app_bounce_rate)) + (loyalty_users × (1 - loyalty_bounce_rate))`
+
+- **unique_impressions_on_site**  
+  `= average_impressions_website × website_frequency × if_website + average_impressions_app × app_frequency × if_app + average_impressions_loyalty × loyalty_frequency × if_loyalty`
+
+- **potential_reach_off_site**  
+  `= sum of (followers × (1 - off_site_bounce_rate))` for each channel selected
+
+- **unique_impressions_off_site**  
+  `= frequency × avg_impressions × if_channel` for each selected channel (e.g., Facebook, Instagram, etc.)
+
+---
+
+## ✅ Boolean Inputs
+
+Use `if_channel = 1` if selected, `0` otherwise.
+
+---
+
+## ⚙️ Additional Behavior
+
+After the user clicks the **Submit** button on the form:
+
+- The formulas must be executed using the inputs.
+- The calculated values must be generated and replaced into the `results.json`.
+- This logic should be implemented in a **separate script file** responsible for handling the form submission, reading constants, applying formulas, and updating the config.
+
+---
+
+## 📁 Output: results.json
+
+We maintain a JSON file named `results.json` with the following structure:
+
+```json
+{
+  "potential_reach_in_store": <calculated_value>,
+  "unique_impressions_in_store": <calculated_value>,
+  "potential_reach_on_site": <calculated_value>,
+  "unique_impressions_on_site": <calculated_value>,
+  "potential_reach_off_site": <calculated_value>,
+  "unique_impressions_off_site": <calculated_value>
+}
+```
+
+On **each form submission**, the formulas must be:
+
+- **Executed using the latest input values**
+- **The `results.json` file must be updated (overwritten) with the new results**
+
+This logic is to be implemented in **Node.js**, in a dedicated script that handles:
+
+- Reading user input
+- Parsing `config.json`
+- Performing calculations
+- Writing updated values into `results.json`