2025 By the Numbers

Spartanburg's Economic Metrics

$3.5B Investment, 1,024 New Jobs

Economic Development in 2025

Downtown Spartanburg 's Growth

Benefits All of Spartanburg County

Talent Gap Analysis 2.0

Building Our Talent Pipeline

Spartanburg: By the Numbers

st

Small Metro for Economic Growth

Leading Metro
nd

Job Market in the U.S.

Job Growth
th

Best Place to Live in SC

Livable Community

14keu.txt -

: A "Translate Document" function that scans an entire text file and highlights words found in the 14kEU.txt list, allowing for one-click replacement of common terms. User Benefit

: Helps non-native speakers or learners quickly identify the correct Urdu word for English concepts. Technical Implementation Idea

: Users can toggle between Urdu script (اردو) and Roman Urdu (e.g., "shukriya") to accommodate different typing preferences.

# Conceptual logic for the feature def get_urdu_suggestion(english_word, mapping_file="14kEU.txt"): # Load mapping into a dictionary for O(1) lookup with open(mapping_file, 'r', encoding='utf-8') as f: dictionary = dict(line.strip().split('\t') for line in f) return dictionary.get(english_word.lower(), "Translation not found") Use code with caution. Copied to clipboard

: As a user types an English word, the system cross-references the 14,000-word database to offer the most common Urdu equivalent in a small tooltip or autocomplete dropdown.

: Ensures that technical or common terms are translated consistently across a project.

: Reduces the need to switch between the workspace and an external dictionary.

: A "Translate Document" function that scans an entire text file and highlights words found in the 14kEU.txt list, allowing for one-click replacement of common terms. User Benefit

: Helps non-native speakers or learners quickly identify the correct Urdu word for English concepts. Technical Implementation Idea

: Users can toggle between Urdu script (اردو) and Roman Urdu (e.g., "shukriya") to accommodate different typing preferences.

# Conceptual logic for the feature def get_urdu_suggestion(english_word, mapping_file="14kEU.txt"): # Load mapping into a dictionary for O(1) lookup with open(mapping_file, 'r', encoding='utf-8') as f: dictionary = dict(line.strip().split('\t') for line in f) return dictionary.get(english_word.lower(), "Translation not found") Use code with caution. Copied to clipboard

: As a user types an English word, the system cross-references the 14,000-word database to offer the most common Urdu equivalent in a small tooltip or autocomplete dropdown.

: Ensures that technical or common terms are translated consistently across a project.

: Reduces the need to switch between the workspace and an external dictionary.