commit 46b9c4cf28a54c760b345c632669cc32ff0c0fe7 Author: denisacirstea Date: Fri Sep 12 10:22:04 2025 +0300 Initial commit: Business Case Automation with Excel generation diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9cb1763 --- /dev/null +++ b/.gitignore @@ -0,0 +1,28 @@ +# Dependency directories +node_modules/ +npm-debug.log +yarn-debug.log +yarn-error.log + +# Optional npm cache directory +.npm + +# Environment variables +.env + +# OS generated files +.DS_Store +.DS_Store? +._* +.Spotlight-V100 +.Trashes +ehthumbs.db +Thumbs.db + +# Log files +logs +*.log + +# Generated files +output/ +*.xlsx \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..8598674 --- /dev/null +++ b/README.md @@ -0,0 +1,77 @@ +# Retail Media Business Case Calculator + +This application helps retail media professionals generate business cases by collecting key metrics and calculating potential reach and impressions across different channels. + +## Features + +- Clean, user-friendly form for collecting retail media data +- Automatic calculation of key metrics: + - Potential reach in-store (digital screens and radio) + - Unique impressions in-store + - Potential reach on-site + - Unique impressions on-site + - Potential reach off-site + - Unique impressions off-site +- Results saved to a JSON file for reporting +- Thank you page with confirmation message + +## Installation + +1. Clone the repository +2. Install dependencies: + +```bash +npm install +``` + +## Running the Application + +Start the server: + +```bash +npm start +``` + +For development with auto-restart: + +```bash +npm run dev +``` + +The application will be available at http://localhost:3000 + +## Project Structure + +- `index.html` - Main form interface for collecting user data +- `thank-you.html` - Confirmation page after form submission +- `server.js` - Express server handling form submissions and routing +- `index.js` - Business logic for calculating retail media metrics +- `config.json` - Configuration file with constants and coefficients +- `results.json` - Output file where calculation results are stored +- `public/` - Static assets directory + +## How It Works + +1. Users fill out the business case form with their retail media data +2. The form validates input and submits data to the server +3. Server processes the data using formulas in `index.js` +4. Results are saved to `results.json` and user is redirected to thank-you page +5. Retail media specialists follow up with the user with a customized business case + +## Technologies Used + +- Node.js and Express for the backend +- HTML/CSS/JavaScript for the frontend +- TailwindCSS for styling +- Vanilla JavaScript for form validation and interactions + +## Configuration + +The application uses a `config.json` file that contains constants and coefficients for the formulas. You can modify these values to adjust the calculation logic. + +## Development Notes + +- Form styling uses a clean white design with accent colors +- Form validation ensures complete and accurate data collection +- The server includes error handling for form submissions +- Calculations are based on industry-standard formulas for retail media \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..dcf3a83 --- /dev/null +++ b/config.json @@ -0,0 +1,61 @@ +{ + "user_data": { + "first_name": "Denisa", + "last_name": "Cirstea", + "company_name": "Footprints AI", + "email": "denisa@example.com", + "phone": "+40 712 345 678", + "store_name": "Media Romania", + "country": "Romania", + "starting_date": "2025-02-01", + "duration": 36, + "store_types": ["convenience", "supermarket", "hypermarket"], + "open_days_per_month": 26, + + "convenience_store_type": { + "stores_number": 120, + "monthly_transactions": 900000, + "has_digital_screens": true, + "screen_count": 300, + "screen_percentage": 70, + "has_in_store_radio": true, + "radio_percentage": 60, + "open_days_per_month": 26 + }, + + "supermarket_store_type": { + "stores_number": 80, + "monthly_transactions": 450000, + "has_digital_screens": true, + "screen_count": 200, + "screen_percentage": 50, + "has_in_store_radio": true, + "radio_percentage": 80, + "open_days_per_month": 26 + }, + + "hypermarket_store_type": { + "stores_number": 5, + "monthly_transactions": 60000, + "has_digital_screens": false, + "screen_count": 0, + "screen_percentage": 0, + "has_in_store_radio": true, + "radio_percentage": 100, + "open_days_per_month": 26 + }, + + "on_site_channels": ["Homepage Banners", "Search Results", "Category Pages"], + "website_visitors": 1200000, + "app_users": 350000, + "loyalty_users": 500000, + + "off_site_channels": ["Social Display", "Programmatic Video", "Search Ads"], + "facebook_followers": 250000, + "instagram_followers": 180000, + "google_views": 4200000, + "email_subscribers": 300000, + "sms_users": 220000, + "whatsapp_contacts": 150000 + } +} \ No newline at end of file diff --git a/create_excel.py b/create_excel.py new file mode 100755 index 0000000..feda667 --- /dev/null +++ b/create_excel.py @@ -0,0 +1,249 @@ +#!/usr/bin/env python3 +import json +import os +import shutil +import datetime +import re +from pathlib import Path +from dateutil.relativedelta import relativedelta +import openpyxl + +def create_excel_from_template(): + """ + Create a copy of the Excel template, replacing {store_name} with the value from config.json + and save it to the output folder. + """ + # Define paths + script_dir = os.path.dirname(os.path.abspath(__file__)) + config_path = os.path.join(script_dir, 'config.json') + template_path = os.path.join(script_dir, 'template', 'Footprints AI for {store_name} - Retail Media Business Case Calculations.xlsx') + output_dir = os.path.join(script_dir, 'output') + + # Ensure output directory exists + os.makedirs(output_dir, exist_ok=True) + + # Read config.json to get store_name, starting_date, and duration + try: + with open(config_path, 'r') as f: + config = json.load(f) + user_data = config.get('user_data', {}) + store_name = user_data.get('store_name', '') + starting_date = user_data.get('starting_date', '') + duration = user_data.get('duration', 36) + + # If store_name is empty, use a default value + if not store_name: + store_name = "Your Store" + + # Calculate years array based on starting_date and duration + years = calculate_years(starting_date, duration) + print(f"Years in the period: {years}") + except Exception as e: + print(f"Error reading config file: {e}") + return False + + # Use first and last years from the array in the filename + year_range = "" + if years and len(years) > 0: + if len(years) == 1: + year_range = f"{years[0]}" + else: + year_range = f"{years[0]}-{years[-1]}" + else: + # Fallback to current year if years array is empty + current_year = datetime.datetime.now().year + year_range = f"{current_year}" + + # Create output filename with store_name and year range + output_filename = f"Footprints AI for {store_name} - Retail Media Business Case Calculations {year_range}.xlsx" + output_path = os.path.join(output_dir, output_filename) + + # Copy the template to the output directory with the new name + try: + shutil.copy2(template_path, output_path) + print(f"Excel file created successfully: {output_path}") + + # Now inject variables from config.json into the Variables sheet + inject_variables(output_path, config) + + return True + except Exception as e: + print(f"Error creating Excel file: {e}") + return False + +def calculate_years(starting_date, duration): + """ + Calculate an array of years that appear in the period from starting_date for duration months. + + Args: + starting_date (str): Date in format dd/mm/yyyy or dd.mm.yyyy + duration (int): Number of months, including the starting month + + Returns: + list: Array of years in the period [year1, year2, ...] + """ + # Default result if we can't parse the date + default_years = [datetime.datetime.now().year] + + # If starting_date is empty, return current year + if not starting_date: + return default_years + + try: + # Try to parse the date, supporting both dd/mm/yyyy and dd.mm.yyyy formats + if '/' in starting_date: + day, month, year = map(int, starting_date.split('/')) + elif '.' in starting_date: + day, month, year = map(int, starting_date.split('.')) + else: + # If format is not recognized, return default + return default_years + + # Create datetime object for starting date + start_date = datetime.datetime(year, month, day) + + # Calculate end date (starting date + duration months - 1 day) + end_date = start_date + relativedelta(months=duration-1) + + # Create a set of years (to avoid duplicates) + years_set = set() + + # Add starting year + years_set.add(start_date.year) + + # Add ending year + years_set.add(end_date.year) + + # If there are years in between, add those too + for y in range(start_date.year + 1, end_date.year): + years_set.add(y) + + # Convert set to sorted list + return sorted(list(years_set)) + + except Exception as e: + print(f"Error calculating years: {e}") + return default_years + +def inject_variables(excel_path, config): + """ + Inject variables from config.json into the Variables sheet of the Excel file. + + Args: + excel_path (str): Path to the Excel file + config (dict): Configuration data from config.json + """ + try: + # Load the workbook + workbook = openpyxl.load_workbook(excel_path) + + # Try to find the Variables sheet + sheet_names = workbook.sheetnames + variables_sheet = None + + # Print all sheet names for debugging + print("Available sheets:", sheet_names) + + # Look for the Variables sheet by name (case-insensitive) + for sheet_name in sheet_names: + if "variable" in sheet_name.lower(): + variables_sheet = workbook[sheet_name] + print(f"Found Variables sheet: '{sheet_name}'") + break + + # If Variables sheet not found by name, try the last sheet + if variables_sheet is None and sheet_names: + last_sheet_name = sheet_names[-1] + variables_sheet = workbook[last_sheet_name] + print(f"Using last sheet '{last_sheet_name}' as Variables sheet") + + # If still not found, try all sheets and look for specific cell patterns + if variables_sheet is None: + for sheet_name in sheet_names: + sheet = workbook[sheet_name] + # Check if this sheet has a cell B2 with a value + if sheet["B2"].value is not None: + variables_sheet = sheet + print(f"Using sheet '{sheet_name}' as it has data in cell B2") + break + + if variables_sheet is None: + print("Warning: Variables sheet not found. No variables were injected.") + return + + # Get user data from config + user_data = config.get("user_data", {}) + + # Map cell references to config values based on the image + cell_mappings = { + "B2": user_data.get("store_name", ""), + "B31": user_data.get("starting_date", ""), + "B32": user_data.get("duration", 36), + "B37": user_data.get("open_days_per_month", 0), + + # Convenience store type + "H37": user_data.get("convenience_store_type", {}).get("stores_number", 0), + "C37": user_data.get("convenience_store_type", {}).get("monthly_transactions", 0), + # Convert boolean to 1/0 for has_digital_screens + "I37": 1 if user_data.get("convenience_store_type", {}).get("has_digital_screens", False) else 0, + "J37": user_data.get("convenience_store_type", {}).get("screen_count", 0), + "K37": user_data.get("convenience_store_type", {}).get("screen_percentage", 0), + # Convert boolean to 1/0 for has_in_store_radio + "M37": 1 if user_data.get("convenience_store_type", {}).get("has_in_store_radio", False) else 0, + "N37": user_data.get("convenience_store_type", {}).get("radio_percentage", 0), + + # Supermarket store type + "H38": user_data.get("supermarket_store_type", {}).get("stores_number", 0), + "C38": user_data.get("supermarket_store_type", {}).get("monthly_transactions", 0), + # Convert boolean to 1/0 for has_digital_screens + "I38": 1 if user_data.get("supermarket_store_type", {}).get("has_digital_screens", False) else 0, + "J38": user_data.get("supermarket_store_type", {}).get("screen_count", 0), + "K38": user_data.get("supermarket_store_type", {}).get("screen_percentage", 0), + # Convert boolean to 1/0 for has_in_store_radio + "M38": 1 if user_data.get("supermarket_store_type", {}).get("has_in_store_radio", False) else 0, + "N38": user_data.get("supermarket_store_type", {}).get("radio_percentage", 0), + + # Hypermarket store type + "H39": user_data.get("hypermarket_store_type", {}).get("stores_number", 0), + "C39": user_data.get("hypermarket_store_type", {}).get("monthly_transactions", 0), + # Convert boolean to 1/0 for has_digital_screens + "I39": 1 if user_data.get("hypermarket_store_type", {}).get("has_digital_screens", False) else 0, + "J39": user_data.get("hypermarket_store_type", {}).get("screen_count", 0), + "K39": user_data.get("hypermarket_store_type", {}).get("screen_percentage", 0), + # Convert boolean to 1/0 for has_in_store_radio + "M39": 1 if user_data.get("hypermarket_store_type", {}).get("has_in_store_radio", False) else 0, + "N39": user_data.get("hypermarket_store_type", {}).get("radio_percentage", 0), + + # Website, App, Loyalty + "B43": user_data.get("website_visitors", 0), + "B44": user_data.get("app_users", 0), + "B45": user_data.get("loyalty_users", 0), + + # Social Media + "B49": user_data.get("facebook_followers", 0), + "B50": user_data.get("instagram_followers", 0), + "B51": user_data.get("google_views", 0) + } + + # Inject values into the Variables sheet + print(f"Injecting variables into sheet: {variables_sheet.title}") + for cell_ref, value in cell_mappings.items(): + try: + # Check if cell exists + if cell_ref in variables_sheet: + variables_sheet[cell_ref] = value + print(f"Set {cell_ref} = {value}") + else: + print(f"Warning: Cell {cell_ref} not found in sheet") + except Exception as e: + print(f"Warning: Could not set value for cell {cell_ref}: {e}") + + # Save the workbook + workbook.save(excel_path) + print(f"Variables successfully injected into {excel_path}") + + except Exception as e: + print(f"Error injecting variables: {e}") + +if __name__ == "__main__": + create_excel_from_template() diff --git a/create_excel_xlwings.py b/create_excel_xlwings.py new file mode 100755 index 0000000..b76bd2d --- /dev/null +++ b/create_excel_xlwings.py @@ -0,0 +1,381 @@ +#!/usr/bin/env python3 +import json +import os +import shutil +import datetime +import re +import traceback +from pathlib import Path +from dateutil.relativedelta import relativedelta +import sys +import unicodedata +from openpyxl import load_workbook +import zipfile +from xml.etree import ElementTree as ET + +def create_excel_from_template(): + """ + Create a copy of the Excel template, replacing {store_name} with the value from config.json + and save it to the output folder. + """ + # Define paths + script_dir = os.path.dirname(os.path.abspath(__file__)) + config_path = os.path.join(script_dir, 'config.json') + template_path = os.path.join(script_dir, 'template', 'Footprints AI for {store_name} - Retail Media Business Case Calculations.xlsx') + output_dir = os.path.join(script_dir, 'output') + + print(f"[DEBUG] script_dir={script_dir}") + print(f"[DEBUG] config_path={config_path}") + print(f"[DEBUG] template_path={template_path}") + print(f"[DEBUG] output_dir={output_dir}") + print(f"[DEBUG] cwd={os.getcwd()}") + + # Ensure output directory exists + os.makedirs(output_dir, exist_ok=True) + + if not os.path.exists(config_path): + print(f"[ERROR] config.json not found at: {config_path}") + return False + + # Read config.json to get store_name, starting_date, and duration + try: + with open(config_path, 'r') as f: + config = json.load(f) + user_data = config.get('user_data', {}) + store_name = user_data.get('store_name', '') + starting_date = user_data.get('starting_date', '') + duration = user_data.get('duration', 36) + + # If store_name is empty, use a default value + if not store_name: + store_name = "Your Store" + + # Calculate years array based on starting_date and duration + years = calculate_years(starting_date, duration) + print(f"Years in the period: {years}") + except Exception as e: + print(f"Error reading config file: {e}") + print(traceback.format_exc()) + return False + + # Use first and last years from the array in the filename + year_range = "" + if years and len(years) > 0: + if len(years) == 1: + year_range = f"{years[0]}" + else: + year_range = f"{years[0]}-{years[-1]}" + else: + # Fallback to current year if years array is empty + current_year = datetime.datetime.now().year + year_range = f"{current_year}" + + # Create output filename with store_name and year range + output_filename = f"Footprints AI for {store_name} - Retail Media Business Case Calculations {year_range}.xlsx" + output_path = os.path.join(output_dir, output_filename) + + print(f"[DEBUG] output_path={output_path}") + + if not os.path.exists(template_path): + print(f"[ERROR] Template not found at: {template_path}") + return False + + # Copy the template to the output directory with the new name + try: + shutil.copy2(template_path, output_path) + if not os.path.exists(output_path): + print(f"[ERROR] Copy reported success but file missing: {output_path}") + return False + print(f"Excel file created successfully: {output_path}") + + # Rename any sheets that contain the {store_name} token + try: + renamed_count = rename_store_placeholders(output_path, store_name) + print(f"[RENAME] Sheets renamed: {renamed_count}") + except Exception as e: + print(f"[RENAME] Unexpected error while renaming sheets: {e}") + + # Now inject variables from config.json into the Variables sheet + ok = inject_variables(output_path, config) + if not ok: + print("[ERROR] inject_variables failed.") + return False + + return True + except Exception as e: + print(f"Error creating Excel file: {e}") + print(traceback.format_exc()) + return False + +def calculate_years(starting_date, duration): + """ + Calculate an array of years that appear in the period from starting_date for duration months. + + Args: + starting_date (str): Date in format dd/mm/yyyy, dd.mm.yyyy, or yyyy-mm-dd + duration (int): Number of months, including the starting month + + Returns: + list: Array of years in the period [year1, year2, ...] + """ + # Default result if we can't parse the date + default_years = [datetime.datetime.now().year] + + # If starting_date is empty, return current year + if not starting_date: + return default_years + + try: + # Try to parse the date, supporting multiple formats + if '/' in starting_date: + day, month, year = map(int, starting_date.split('/')) + elif '.' in starting_date: + day, month, year = map(int, starting_date.split('.')) + elif '-' in starting_date: + # Handle yyyy-mm-dd format (from HTML date input) + parts = starting_date.split('-') + if len(parts) == 3: + year, month, day = map(int, parts) + else: + return default_years + else: + # If format is not recognized, return default + return default_years + + # Create datetime object for starting date + start_date = datetime.datetime(year, month, day) + + # Calculate end date (starting date + duration months - 1 day) + end_date = start_date + relativedelta(months=duration-1) + + # Create a set of years (to avoid duplicates) + years_set = set() + + # Add starting year + years_set.add(start_date.year) + + # Add ending year + years_set.add(end_date.year) + + # If there are years in between, add those too + for y in range(start_date.year + 1, end_date.year): + years_set.add(y) + + # Convert set to sorted list + return sorted(list(years_set)) + + except Exception as e: + print(f"Error calculating years: {e}") + return default_years + +def _normalize_name(s: str) -> str: + """Normalize sheet names to avoid issues with en-dash/nbsp/casing.""" + if s is None: + return "" + s = unicodedata.normalize("NFKC", s) + return s.replace("\u2013", "-").replace("\u00A0", " ").strip().lower() + +def _diagnose_xlsx(path: str): + """Inspect the XLSX container to list sheets and their types when openpyxl sees none.""" + try: + with zipfile.ZipFile(path, 'r') as z: + print("[DIAG] ZIP entries:", len(z.namelist())) + # Workbook relationships and workbook xml + if 'xl/workbook.xml' in z.namelist(): + xml = z.read('xl/workbook.xml') + root = ET.fromstring(xml) + ns = {'ns': 'http://schemas.openxmlformats.org/spreadsheetml/2006/main'} + sheets = root.findall('.//ns:sheets/ns:sheet', ns) + if not sheets: + print("[DIAG] No nodes found in xl/workbook.xml") + for s in sheets: + print(f"[DIAG] sheet name={s.get('name')!r} id={s.get('sheetId')} r:id={s.get('{http://schemas.openxmlformats.org/officeDocument/2006/relationships}id')}") + # Check for 'fileVersion' and workbookPr flags that sometimes confuse parsers + wbpr = root.find('.//ns:workbookPr', ns) + if wbpr is not None: + print("[DIAG] workbookPr attrs:", wbpr.attrib) + else: + print("[DIAG] Missing xl/workbook.xml (file may be corrupted or not an xlsx).") + + # Look for worksheet vs chartsheet parts + worksheet_parts = [n for n in z.namelist() if n.startswith('xl/worksheets/sheet') and n.endswith('.xml')] + chartsheet_parts = [n for n in z.namelist() if n.startswith('xl/chartsheets/sheet') and n.endswith('.xml')] + dialogsheets = [n for n in z.namelist() if n.startswith('xl/dialogsheets/') and n.endswith('.xml')] + print(f"[DIAG] worksheets={len(worksheet_parts)}, chartsheets={len(chartsheet_parts)}, dialogsheets={len(dialogsheets)}") + if chartsheet_parts and not worksheet_parts: + print("[DIAG] This workbook appears to contain only chart sheets (no worksheets). openpyxl will show zero sheetnames.") + except Exception as e: + print(f"[DIAG] Failed to inspect xlsx: {e}") + print(traceback.format_exc()) + +def _sanitize_sheet_title(title: str) -> str: + """ + Make a worksheet title Excel-safe: + - Replace invalid characters : \ / ? * [ ] + - Trim to 31 chars + """ + invalid = r'[:\\/\?\*\[\]]' + safe = re.sub(invalid, ' ', title).strip() + if len(safe) > 31: + safe = safe[:31] + return safe + +def rename_store_placeholders(excel_path: str, store_name: str) -> int: + """ + Rename any worksheet whose title contains '{store_name}' by replacing the token + with the provided store_name, enforcing Excel naming rules and uniqueness. + Returns the number of sheets renamed. + """ + try: + wb = load_workbook(excel_path, data_only=False) + except Exception as e: + print(f"[RENAME] Could not open workbook for renaming: {e}") + return 0 + + renamed = 0 + existing = set(ws.title for ws in wb.worksheets) + + for ws in wb.worksheets: + old = ws.title + if "{store_name}" not in old: + continue + new_title_raw = old.replace("{store_name}", store_name or "Your Store") + new_title = _sanitize_sheet_title(new_title_raw) + + # Ensure uniqueness by appending (2), (3), ... + candidate = new_title + suffix = 2 + while candidate in existing and candidate != old: + base = new_title + # leave room for " (nn)" + max_base = 31 - (len(str(suffix)) + 3) + if len(base) > max_base: + base = base[:max_base] + candidate = f"{base} ({suffix})" + suffix += 1 + + if candidate != old: + try: + ws.title = candidate + existing.discard(old) + existing.add(candidate) + renamed += 1 + print(f"[RENAME] '{old}' → '{candidate}'") + except Exception as e: + print(f"[RENAME] Failed to rename '{old}' to '{candidate}': {e}") + + if renamed > 0: + try: + wb.save(excel_path) + print(f"[RENAME] Saved workbook after renaming {renamed} sheet(s).") + except Exception as e: + print(f"[RENAME] Failed to save workbook after renames: {e}") + else: + print("[RENAME] No sheets contained '{store_name}'.") + + return renamed + +def inject_variables(excel_path, config): + """ + Inject variables from config.json into the Variables sheet of the Excel file. + Linux-only path: uses openpyxl (no Excel required). This reads/writes .xlsx safely; .xlsm VBA projects are not preserved if you re-save them. + """ + user_data = config.get("user_data", {}) + + # Map cell references to config values based on the image + cell_mappings = { + "B2": user_data.get("store_name", ""), + "B31": user_data.get("starting_date", ""), + "B32": user_data.get("duration", 36), + "B37": user_data.get("open_days_per_month", 0), + "H37": user_data.get("convenience_store_type", {}).get("stores_number", 0), + "C37": user_data.get("convenience_store_type", {}).get("monthly_transactions", 0), + "I37": 1 if user_data.get("convenience_store_type", {}).get("has_digital_screens", False) else 0, + "J37": user_data.get("convenience_store_type", {}).get("screen_count", 0), + "K37": user_data.get("convenience_store_type", {}).get("screen_percentage", 0), + "M37": 1 if user_data.get("convenience_store_type", {}).get("has_in_store_radio", False) else 0, + "N37": user_data.get("convenience_store_type", {}).get("radio_percentage", 0), + "H38": user_data.get("supermarket_store_type", {}).get("stores_number", 0), + "C38": user_data.get("supermarket_store_type", {}).get("monthly_transactions", 0), + "I38": 1 if user_data.get("supermarket_store_type", {}).get("has_digital_screens", False) else 0, + "J38": user_data.get("supermarket_store_type", {}).get("screen_count", 0), + "K38": user_data.get("supermarket_store_type", {}).get("screen_percentage", 0), + "M38": 1 if user_data.get("supermarket_store_type", {}).get("has_in_store_radio", False) else 0, + "N38": user_data.get("supermarket_store_type", {}).get("radio_percentage", 0), + "H39": user_data.get("hypermarket_store_type", {}).get("stores_number", 0), + "C39": user_data.get("hypermarket_store_type", {}).get("monthly_transactions", 0), + "I39": 1 if user_data.get("hypermarket_store_type", {}).get("has_digital_screens", False) else 0, + "J39": user_data.get("hypermarket_store_type", {}).get("screen_count", 0), + "K39": user_data.get("hypermarket_store_type", {}).get("screen_percentage", 0), + "M39": 1 if user_data.get("hypermarket_store_type", {}).get("has_in_store_radio", False) else 0, + "N39": user_data.get("hypermarket_store_type", {}).get("radio_percentage", 0), + "B43": user_data.get("website_visitors", 0), + "B44": user_data.get("app_users", 0), + "B45": user_data.get("loyalty_users", 0), + "B49": user_data.get("facebook_followers", 0), + "B50": user_data.get("instagram_followers", 0), + "B51": user_data.get("google_views", 0), + "B53": user_data.get("sms_users", 0) + } + + # Warn if trying to process a macro-enabled workbook: openpyxl will not preserve VBA + if excel_path.lower().endswith(".xlsm"): + print("Warning: .xlsm detected. openpyxl cannot preserve VBA projects; consider switching to a .xlsx template or running this step on Windows/Excel.") + + # ---- openpyxl fallback (works on Linux, no Excel required) ---- + try: + wb = load_workbook(excel_path, data_only=False) + if not wb.sheetnames: + print("[WARN] openpyxl reports no worksheets. Running container diagnostics…") + _diagnose_xlsx(excel_path) + print("Available sheets (openpyxl):", [repr(s) for s in wb.sheetnames]) + + # Find Variables sheet (case-insensitive, normalized) + target_idx = None + for idx, name in enumerate(wb.sheetnames): + if "variable" in _normalize_name(name): + target_idx = idx + break + if target_idx is None: + target_idx = len(wb.sheetnames) - 1 if wb.sheetnames else None + if target_idx is not None: + print(f"Variables sheet not found by name; using last sheet: {wb.sheetnames[target_idx]}") + else: + print("Suggestion: Ensure the template has at least one normal worksheet (not only chartsheets). Open and 'Save As' a regular .xlsx in Excel.") + + if target_idx is None: + print("Warning: Workbook has no sheets. No variables were injected.") + return False + + ws = wb[wb.sheetnames[target_idx]] + + # Write values + for cell_ref, value in cell_mappings.items(): + try: + ws[cell_ref].value = value + print(f"[openpyxl] Set {cell_ref} = {value}") + except Exception as e: + print(f"Warning: Could not set value for cell {cell_ref}: {e}") + + # Ensure we're saving to .xlsx path to avoid accidental macro loss if template was .xlsm + save_path = excel_path + if save_path.lower().endswith(".xlsm"): + save_path = save_path[:-5] + ".xlsx" + print(f"Saving as {save_path} to avoid stripping VBA from .xlsm.") + + wb.save(save_path) + print(f"Variables successfully injected into {save_path} using openpyxl") + return True + except Exception as e: + print(f"Error in openpyxl fallback: {e}") + print(traceback.format_exc()) + return False + +if __name__ == "__main__": + try: + ok = create_excel_from_template() + sys.exit(0 if ok else 1) + except Exception as e: + print(f"[FATAL] Unhandled exception: {e}") + print(traceback.format_exc()) + sys.exit(2) diff --git a/index.html b/index.html new file mode 100644 index 0000000..e607810 --- /dev/null +++ b/index.html @@ -0,0 +1,1602 @@ + + + + + + Retail Media Business Case + + + + +
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+

Retail Media Business Case

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Complete the form below, and our retail media specialists will reach out soon.

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+ Contact + Store Details + In-Store + On-Site + Off-Site + Business Case +
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Contact Information

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+ + + + + diff --git a/index.js b/index.js new file mode 100644 index 0000000..c25daf3 --- /dev/null +++ b/index.js @@ -0,0 +1,187 @@ +const fs = require('fs'); +const path = require('path'); + +// Function to update config.json with form data +async function updateConfig(formData) { + return new Promise((resolve, reject) => { + const configPath = path.join(__dirname, 'config.json'); + + // Read the existing config file + fs.readFile(configPath, 'utf8', (err, data) => { + if (err) { + reject(new Error(`Failed to read config file: ${err.message}`)); + return; + } + + try { + // Parse the existing config + const configData = JSON.parse(data); + + // Update user_data in the config with form data + configData.user_data = { + // Contact information + first_name: formData.firstName || "", + last_name: formData.lastName || "", + company_name: formData.company || "", + email: formData.email || "", + phone: formData.phone || "", + store_name: formData.storeName || "", + country: formData.country || "", + starting_date: formData.startingDate || "", + duration: parseInt(formData.duration) || 36, + + // Store information + store_types: getSelectedStoreTypes(formData), + open_days_per_month: parseInt(formData.openDays) || 0, + + // Store type specific data + convenience_store_type: { + stores_number: isStoreTypeSelected(formData, 'Convenience') ? parseInt(formData.convenience_stores) || 0 : 0, + monthly_transactions: isStoreTypeSelected(formData, 'Convenience') ? parseInt(formData.convenience_transactions) || 0 : 0, + has_digital_screens: isStoreTypeSelected(formData, 'Convenience') ? formData.convenience_screens === "Yes" : false, + screen_count: isStoreTypeSelected(formData, 'Convenience') ? parseInt(formData.convenience_screen_count) || 0 : 0, + screen_percentage: isStoreTypeSelected(formData, 'Convenience') ? parseInt(formData.convenience_screen_percentage) || 0 : 0, + has_in_store_radio: isStoreTypeSelected(formData, 'Convenience') ? formData.convenience_radio === "Yes" : false, + radio_percentage: isStoreTypeSelected(formData, 'Convenience') ? parseInt(formData.convenience_radio_percentage) || 0 : 0, + open_days_per_month: parseInt(formData.openDays) || 0 + }, + + supermarket_store_type: { + stores_number: isStoreTypeSelected(formData, 'Supermarket') ? parseInt(formData.supermarket_stores) || 0 : 0, + monthly_transactions: isStoreTypeSelected(formData, 'Supermarket') ? parseInt(formData.supermarket_transactions) || 0 : 0, + has_digital_screens: isStoreTypeSelected(formData, 'Supermarket') ? formData.supermarket_screens === "Yes" : false, + screen_count: isStoreTypeSelected(formData, 'Supermarket') ? parseInt(formData.supermarket_screen_count) || 0 : 0, + screen_percentage: isStoreTypeSelected(formData, 'Supermarket') ? parseInt(formData.supermarket_screen_percentage) || 0 : 0, + has_in_store_radio: isStoreTypeSelected(formData, 'Supermarket') ? formData.supermarket_radio === "Yes" : false, + radio_percentage: isStoreTypeSelected(formData, 'Supermarket') ? parseInt(formData.supermarket_radio_percentage) || 0 : 0, + open_days_per_month: parseInt(formData.openDays) || 0 + }, + + hypermarket_store_type: { + stores_number: isStoreTypeSelected(formData, 'Hypermarket') ? parseInt(formData.hypermarket_stores) || 0 : 0, + monthly_transactions: isStoreTypeSelected(formData, 'Hypermarket') ? parseInt(formData.hypermarket_transactions) || 0 : 0, + has_digital_screens: isStoreTypeSelected(formData, 'Hypermarket') ? formData.hypermarket_screens === "Yes" : false, + screen_count: isStoreTypeSelected(formData, 'Hypermarket') ? parseInt(formData.hypermarket_screen_count) || 0 : 0, + screen_percentage: isStoreTypeSelected(formData, 'Hypermarket') ? parseInt(formData.hypermarket_screen_percentage) || 0 : 0, + has_in_store_radio: isStoreTypeSelected(formData, 'Hypermarket') ? formData.hypermarket_radio === "Yes" : false, + radio_percentage: isStoreTypeSelected(formData, 'Hypermarket') ? parseInt(formData.hypermarket_radio_percentage) || 0 : 0, + open_days_per_month: parseInt(formData.openDays) || 0 + }, + + // On-site channels + on_site_channels: getSelectedChannels(formData, 'onSiteChannels'), + website_visitors: isChannelSelected(formData, 'onSiteChannels', 'Website') ? parseInt(formData.websiteVisitors) || 0 : 0, + app_users: isChannelSelected(formData, 'onSiteChannels', 'Mobile App') ? parseInt(formData.appUsers) || 0 : 0, + loyalty_users: isChannelSelected(formData, 'onSiteChannels', 'Loyalty Program') ? parseInt(formData.loyaltyUsers) || 0 : 0, + + // Off-site channels + off_site_channels: getSelectedChannels(formData, 'offSiteChannels'), + facebook_followers: isChannelSelected(formData, 'offSiteChannels', 'Facebook Business') ? parseInt(formData.facebookFollowers) || 0 : 0, + instagram_followers: isChannelSelected(formData, 'offSiteChannels', 'Instagram Business') ? parseInt(formData.instagramFollowers) || 0 : 0, + google_views: isChannelSelected(formData, 'offSiteChannels', 'Google Business Profile') ? parseInt(formData.googleViews) || 0 : 0, + email_subscribers: isChannelSelected(formData, 'offSiteChannels', 'Email') ? parseInt(formData.emailSubscribers) || 0 : 0, + sms_users: isChannelSelected(formData, 'offSiteChannels', 'SMS') ? parseInt(formData.smsUsers) || 0 : 0, + whatsapp_contacts: isChannelSelected(formData, 'offSiteChannels', 'WhatsApp') ? parseInt(formData.whatsappContacts) || 0 : 0, + + // Preserve existing calculation results if they exist + potential_reach_in_store: 0, + unique_impressions_in_store: 0, + potential_reach_on_site: 0, + unique_impressions_on_site: 0, + potential_reach_off_site: 0, + unique_impressions_off_site: 0 + }; + + // Write the updated config back to the file + const updatedConfig = JSON.stringify(configData, null, 2); + + fs.writeFile(configPath, updatedConfig, 'utf8', (writeErr) => { + if (writeErr) { + reject(new Error(`Failed to write to config file: ${writeErr.message}`)); + return; + } + + resolve(); + }); + } catch (parseError) { + reject(new Error(`Failed to parse config file: ${parseError.message}`)); + } + }); + }); +} + +// Helper function to check if a channel is selected +function isChannelSelected(formData, channelType, channelName) { + const selectedChannels = getSelectedChannels(formData, channelType); + return selectedChannels.includes(channelName); +} + +// Helper function to get selected channels from form data +function getSelectedChannels(formData, channelType) { + console.log(`Getting selected channels for ${channelType} from formData:`, formData[channelType]); + + let channels = []; + + if (formData[channelType]) { + if (Array.isArray(formData[channelType])) { + channels = formData[channelType]; + } else { + channels = [formData[channelType]]; + } + } + + console.log(`Selected ${channelType}:`, channels); + return channels; +} + +// Helper function to check if a store type is selected +function isStoreTypeSelected(formData, storeType) { + const selectedTypes = getSelectedStoreTypes(formData); + return selectedTypes.includes(storeType); +} + +// Helper function to get selected store types from form data +function getSelectedStoreTypes(formData) { + console.log('Getting selected store types from formData:', formData); + + // Check if storeTypes is an array or single value + let storeTypes = []; + + if (formData.storeTypes) { + if (Array.isArray(formData.storeTypes)) { + storeTypes = formData.storeTypes; + } else { + storeTypes = [formData.storeTypes]; + } + } + + console.log('Selected store types:', storeTypes); + return storeTypes; +} + +// Function to fetch config.json +async function fetchConfig() { + return new Promise((resolve, reject) => { + fs.readFile(path.join(__dirname, 'config.json'), 'utf8', (err, data) => { + if (err) { + reject(new Error(`Failed to read config file: ${err.message}`)); + return; + } + + try { + const config = JSON.parse(data); + resolve(config); + } catch (parseError) { + reject(new Error(`Failed to parse config file: ${parseError.message}`)); + } + }); + }); +} + +// For Node.js environment, export the functions +if (typeof module !== 'undefined' && module.exports) { + module.exports = { + updateConfig, + fetchConfig + }; +} \ No newline at end of file diff --git a/llm_prompt_retail_media.md b/llm_prompt_retail_media.md new file mode 100644 index 0000000..ce69d80 --- /dev/null +++ b/llm_prompt_retail_media.md @@ -0,0 +1,92 @@ +# 🧠 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": , + "unique_impressions_in_store": , + "potential_reach_on_site": , + "unique_impressions_on_site": , + "potential_reach_off_site": , + "unique_impressions_off_site": +} +``` + +--- + +## 🔢 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": , + "unique_impressions_in_store": , + "potential_reach_on_site": , + "unique_impressions_on_site": , + "potential_reach_off_site": , + "unique_impressions_off_site": +} +``` + +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` diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 0000000..6788d23 --- /dev/null +++ b/package-lock.json @@ -0,0 +1,2290 @@ +{ + "name": "retail-media-calculator", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "retail-media-calculator", + "version": "1.0.0", + "dependencies": { + "body-parser": "^1.20.2", + "exceljs": "^4.4.0", + "express": "^4.18.2", + "fs-extra": "^11.3.1", + "node-xlsx": "^0.24.0", + "python-shell": "^5.0.0", + "xlsx": "^0.18.5" + }, + "devDependencies": { + "nodemon": "^3.0.1" + } + }, + "node_modules/@fast-csv/format": { + "version": "4.3.5", + "resolved": "https://registry.npmjs.org/@fast-csv/format/-/format-4.3.5.tgz", + "integrity": "sha512-8iRn6QF3I8Ak78lNAa+Gdl5MJJBM5vRHivFtMRUWINdevNo00K7OXxS2PshawLKTejVwieIlPmK5YlLu6w4u8A==", + "license": "MIT", + "dependencies": { + 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"^3.0.0", + "readable-stream": "^3.6.0" + }, + "engines": { + "node": ">= 10" + } + } + } +} diff --git a/package.json b/package.json new file mode 100644 index 0000000..ebcee13 --- /dev/null +++ b/package.json @@ -0,0 +1,22 @@ +{ + "name": "retail-media-calculator", + "version": "1.0.0", + "description": "Retail Media Business Case Calculation Agent", + "main": "server.js", + "scripts": { + "start": "node server.js", + "dev": "nodemon server.js" + }, + "dependencies": { + "body-parser": "^1.20.2", + "exceljs": "^4.4.0", + "express": "^4.18.2", + "fs-extra": "^11.3.1", + "node-xlsx": "^0.24.0", + "python-shell": "^5.0.0", + "xlsx": "^0.18.5" + }, + "devDependencies": { + "nodemon": "^3.0.1" + } +} diff --git a/server.js b/server.js new file mode 100644 index 0000000..f29f0d7 --- /dev/null +++ b/server.js @@ -0,0 +1,69 @@ +const express = require('express'); +const bodyParser = require('body-parser'); +const fs = require('fs'); +const path = require('path'); +const { exec } = require('child_process'); +const { updateConfig } = require('./index'); + +// Create Express app +const app = express(); +const PORT = process.env.PORT || 3001; + +// Middleware +app.use(express.static(__dirname)); // Serve static files +app.use(bodyParser.json()); +app.use(bodyParser.urlencoded({ extended: true })); + +// Route to serve the HTML form +app.get('/', (req, res) => { + res.sendFile(path.join(__dirname, 'index.html')); +}); + +// Route to serve the thank you page +app.get('/thank-you.html', (req, res) => { + res.sendFile(path.join(__dirname, 'thank-you.html')); +}); + +// API endpoint to handle form submissions +app.post('/calculate', async (req, res) => { + try { + console.log('Received form submission'); + const formData = req.body; + console.log('Form data received:', JSON.stringify(formData, null, 2)); + + // Update config file with form data + await updateConfig(formData); + console.log('Config file updated successfully'); + + // Run Python script to create Excel file with variables injection + exec('python3 create_excel_xlwings.py', (error, stdout, stderr) => { + if (error) { + console.error(`Error executing Python script: ${error}`); + console.error(`stderr: ${stderr}`); + } else { + console.log(`Python script output: ${stdout}`); + } + }); + + // Send success response + res.json({ + success: true, + message: 'Form data saved successfully' + }); + console.log('Success response sent'); + } catch (error) { + console.error('Error processing form data:', error); + console.error('Error stack:', error.stack); + res.status(500).json({ + success: false, + message: 'Error processing form data', + error: error.message + }); + console.error('Error response sent'); + } +}); + +// Start the server +app.listen(PORT, () => { + console.log(`Server running on port ${PORT}`); +}); \ No newline at end of file diff --git a/thank-you.html b/thank-you.html new file mode 100644 index 0000000..b7854b9 --- /dev/null +++ b/thank-you.html @@ -0,0 +1,33 @@ + + + + + + Thank You - Retail Media Business Case + + + +
+
+

Thank You!

+
+ +
+
+ + + +
+ +

+ Your submission has been received successfully. Our retail media specialists will reach out to you soon. +

+ + + Return Home + +
+
+ + \ No newline at end of file