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"""
THEORIAโข - Theory-driven Naming AI
===================================
ํน๋ณํ 15๊ฐ์ ์ด๋ก ์ผ๋ก ์์ฑํ๋ ๋ค์ด๋ฐ AI
Theory-driven Naming AI with 15 Specialized Theories
-----------------------------------
"""
import os
import json
import gradio as gr
import openai
from openai import OpenAI
from datetime import datetime
from typing import List, Dict, Tuple, Optional
import random
import time
# OpenAI ํด๋ผ์ด์ธํธ
if not os.getenv("OPENAI_API_KEY"):
raise EnvironmentError("OPENAI_API_KEY ํ๊ฒฝ ๋ณ์๋ฅผ ์ค์ ํ์ธ์.")
client = OpenAI()
# ์ธ์ด๋ณ ํ
์คํธ ์ ์
TEXTS = {
"en": {
"title": "THEORIAโข",
"subtitle": "Theory-driven Naming AI with 15 Specialized Theories",
"description": "Generate innovative brand names using 15 cognitive and creative theories",
"industry_label": "๐ญ Industry",
"industry_placeholder": "e.g., cafe, fitness, education, beauty...",
"keywords_label": "๐ Keywords",
"keywords_placeholder": "premium, comfortable, urban, eco-friendly...",
"keywords_info": "Core values or characteristics the brand should embody",
"generate_button": "Generate with {theory}",
"language_label": "๐ Language",
"progress_message": "Generating innovative names using {theory}...",
"error_message": "โ Error in {theory}: {error}",
"input_required": "โ ๏ธ Please enter both industry and keywords.",
"evaluation_labels": {
"creativity": "Creativity",
"memorability": "Memorability",
"relevance": "Relevance"
}
},
"ko": {
"title": "THEORIAโข",
"subtitle": "ํน๋ณํ 15๊ฐ์ ์ด๋ก ์ผ๋ก ์์ฑํ๋ ๋ค์ด๋ฐ AI",
"description": "15๊ฐ์ง ์ธ์ง ๋ฐ ์ฐฝ์ ์ด๋ก ์ ํ์ฉํ ํ์ ์ ์ธ ๋ธ๋๋๋ช
์์ฑ",
"industry_label": "๐ญ ์
์ข
",
"industry_placeholder": "์: ์นดํ, ํผํธ๋์ค, ๊ต์ก, ๋ทฐํฐ...",
"keywords_label": "๐ ํต์ฌ ํค์๋",
"keywords_placeholder": "ํ๋ฆฌ๋ฏธ์, ํธ์ํ, ๋์์ ์ธ, ์นํ๊ฒฝ...",
"keywords_info": "๋ธ๋๋๊ฐ ๋ด์์ผ ํ ํต์ฌ ๊ฐ์น๋ ํน์ง๋ค",
"generate_button": "{theory}๋ก ์์ฑ",
"language_label": "๐ ์ธ์ด",
"progress_message": "{theory}๋ฅผ ํ์ฉํด ํ์ ์ ์ธ ์ด๋ฆ์ ์์ฑํ๊ณ ์์ต๋๋ค...",
"error_message": "โ {theory} ์ค๋ฅ: {error}",
"input_required": "โ ๏ธ ์
์ข
๊ณผ ํค์๋๋ฅผ ๋ชจ๋ ์
๋ ฅํด์ฃผ์ธ์.",
"evaluation_labels": {
"creativity": "์ฐฝ์์ฑ",
"memorability": "๊ธฐ์ต์ฑ",
"relevance": "๊ด๋ จ์ฑ"
}
}
}
# ์ด๋ก ๋ณ ์ค๋ช
(์์ด/ํ๊ตญ์ด)
THEORY_DESCRIPTIONS = {
"en": {
"square": "Creates a semantic square structure where 4 words are connected by meaningful relationships. Hidden diagonal connections create 'aha!' moments.",
"blending": "Blends two or more concepts to create new meaning. Like Netflix (Net+Flix), it births innovative concepts.",
"sound": "Utilizes associations between phonemes and meaning. 'i,e' convey lightness and speed, 'o,u' convey weight and slowness.",
"linguistic": "Creates global brands considering linguistic thought differences. Reflects cultural nuances and localization strategies.",
"archetype": "Leverages Jung's 12 universal archetypes. Creates unconscious emotional connections like Hero (Nike) or Creator (Apple).",
"jobs": "Focuses on the 'job' customers are trying to get done. Integrates functional, emotional, and social needs.",
"scamper": "Uses 7 creative techniques (Substitute, Combine, Adapt, Modify, Put to other use, Eliminate, Reverse) for innovation.",
"design": "Pursues human-centered innovation. Finds the intersection of desirability (human), feasibility (technical), and viability (business).",
"biomimicry": "Creates nature-inspired brands. Applies 3.8 billion years of evolutionary wisdom to branding.",
"cognitive": "Creates brands that minimize cognitive processing. Uses 1-3 syllables for instant recognition and recall.",
"vonrestorff": "Creates unique, memorable brands. Intentionally violates category conventions for 30x better recall.",
"network": "Creates brands that maximize network value. Designs structures where value increases with more users.",
"memetics": "Creates culturally replicable and evolving brands. Embeds viral elements that spread naturally like memes.",
"color": "Creates brands using color associations and emotions. Applies color psychology: red (passion), blue (trust), green (nature).",
"gestalt": "Creates brands using perception principles. Designs holistic brand experiences where the whole exceeds the sum of parts."
},
"ko": {
"square": "4๊ฐ์ ๋จ์ด๊ฐ ์๋ฏธ์ ๊ด๊ณ๋ก ์ฐ๊ฒฐ๋์ด ์ฌ๊ฐํ์ ์ด๋ฃจ๋ ๊ตฌ์กฐ์
๋๋ค. ๋๋ณ์ ์จ๊ฒจ์ง ์ฐ๊ฒฐ์ด '์ํ!' ๋ชจ๋จผํธ๋ฅผ ๋ง๋ญ๋๋ค.",
"blending": "๋ ๊ฐ ์ด์์ ๊ฐ๋
์ ํผํฉํ์ฌ ์๋ก์ด ์๋ฏธ๋ฅผ ์ฐฝ์ถํฉ๋๋ค. Netflix(Net+Flix)์ฒ๋ผ ํ์ ์ ์ธ ๊ฐ๋
์ ํ์์ํต๋๋ค.",
"sound": "์์์ ์๋ฏธ ๊ฐ์ ์ฐ๊ด์ฑ์ ํ์ฉํฉ๋๋ค. 'i,e'๋ ๊ฐ๋ณ๊ณ ๋น ๋ฅธ ๋๋, 'o,u'๋ ๋ฌด๊ฒ๊ณ ๋๋ฆฐ ๋๋์ ์ ๋ฌํฉ๋๋ค.",
"linguistic": "์ธ์ด๋ณ ์ฌ๊ณ ๋ฐฉ์ ์ฐจ์ด๋ฅผ ๊ณ ๋ คํ ๊ธ๋ก๋ฒ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค. ๋ฌธํ์ ๋์์ค์ ํ์งํ ์ ๋ต์ ๋ฐ์ํฉ๋๋ค.",
"archetype": "Jung์ 12๊ฐ์ง ๋ณดํธ์ ์ํ์ ํ์ฉํฉ๋๋ค. Hero(Nike), Creator(Apple)์ฒ๋ผ ๋ฌด์์์ ๊ฐ์ ์ฐ๊ฒฐ์ ๋ง๋ญ๋๋ค.",
"jobs": "๊ณ ๊ฐ์ด ํด๊ฒฐํ๋ ค๋ '์ผ'์ ์ด์ ์ ๋ง์ถฅ๋๋ค. ๊ธฐ๋ฅ์ , ๊ฐ์ ์ , ์ฌํ์ ์ฐจ์์ ๋์ฆ๋ฅผ ํตํฉ์ ์ผ๋ก ํด๊ฒฐํฉ๋๋ค.",
"scamper": "7๊ฐ์ง ์ฐฝ์์ ๊ธฐ๋ฒ(๋์ฒด, ๊ฒฐํฉ, ์ ์, ์์ , ์ฉ๋๋ณ๊ฒฝ, ์ ๊ฑฐ, ์ญ์ )์ผ๋ก ํ์ ์ ์ธ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค.",
"design": "์ธ๊ฐ ์ค์ฌ ํ์ ์ ์ถ๊ตฌํฉ๋๋ค. ๋ฐ๋์งํจ(์ธ๊ฐ), ์คํ๊ฐ๋ฅ์ฑ(๊ธฐ์ ), ์์กด๊ฐ๋ฅ์ฑ(๋น์ฆ๋์ค)์ ๊ต์งํฉ์ ์ฐพ์ต๋๋ค.",
"biomimicry": "์์ฐ์์ ์๊ฐ์ ๋ฐ์ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค. 38์ต๋
์งํ์ ์งํ๋ฅผ ๋ธ๋๋ฉ์ ์ ์ฉํฉ๋๋ค.",
"cognitive": "์ธ์ง ์ฒ๋ฆฌ๋ฅผ ์ต์ํํ๋ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค. 1-3์์ ์ ์ฌ์ด ๋ฐ์์ผ๋ก ์ฆ๊ฐ์ ์ธ์๊ณผ ๊ธฐ์ต์ ๋์ต๋๋ค.",
"vonrestorff": "๋
ํนํ๊ณ ๊ธฐ์ต์ ๋จ๋ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค. ์นดํ
๊ณ ๋ฆฌ ๊ด์ต์ ์๋์ ์ผ๋ก ์๋ฐํ์ฌ 30๋ฐฐ ๋ ์ ๊ธฐ์ต๋๊ฒ ํฉ๋๋ค.",
"network": "๋คํธ์ํฌ ๊ฐ์น๋ฅผ ๊ทน๋ํํ๋ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค. ์ฌ์ฉ์๊ฐ ๋ง์์๋ก ๊ฐ์น๊ฐ ์ฆ๊ฐํ๋ ๊ตฌ์กฐ๋ฅผ ์ค๊ณํฉ๋๋ค.",
"memetics": "๋ฌธํ์ ์ผ๋ก ๋ณต์ ๋๊ณ ์งํํ๋ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค. ๋ฐ์ฒ๋ผ ์์ฐ์ค๋ฝ๊ฒ ํผ์ ธ๋๊ฐ๋ ๋ฐ์ด๋ด ์์๋ฅผ ๋ด์ฌํํฉ๋๋ค.",
"color": "์์ ์ฐ์๊ณผ ๊ฐ์ ์ ํ์ฉํ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค. ๋นจ๊ฐ(์ด์ ), ํ๋(์ ๋ขฐ), ์ด๋ก(์์ฐ) ๋ฑ ์์ ์ฌ๋ฆฌ๋ฅผ ์ ์ฉํฉ๋๋ค.",
"gestalt": "์ง๊ฐ ์๋ฆฌ๋ฅผ ํ์ฉํ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค. ์ ์ฒด๊ฐ ๋ถ๋ถ์ ํฉ๋ณด๋ค ํฌ๋ค๋ ์์น์ผ๋ก ํตํฉ์ ๋ธ๋๋ ๊ฒฝํ์ ์ค๊ณํฉ๋๋ค."
}
}
# ํต์ผ๋ ๊ธฐ๋ณธ ํ๋กฌํํธ ํ
ํ๋ฆฟ (์์ด)
UNIFIED_BASE_PROMPT_EN = """
You are a {theory_name} expert. {theory_description}
Based on the user input (industry/keywords), generate an array in the following unified JSON format:
{{
"brands": [
{{
"core": {{
"brand_name": "Brand Name",
"slogan": "Slogan",
"core_value": "Core Value",
"target_emotion": "Target Emotion",
"brand_personality": "Brand Personality"
}},
"visual": {{
"primary_color": "#HEX",
"color_meaning": "Color Meaning",
"visual_concept": "Visual Concept",
"typography_style": "Typography Style"
}},
"linguistic": {{
"pronunciation": "Pronunciation Guide",
"etymology": "Etymology/Structure",
"global_adaptability": "Global Adaptability",
"memorable_factor": "Memorability Factor"
}},
"strategic": {{
"differentiation": "Differentiation Point",
"market_positioning": "Market Positioning",
"growth_potential": "Growth Potential",
"implementation_ease": "Implementation Ease"
}},
"theory_specific": {{
{theory_specific_fields}
}},
"evaluation": {{
"creativity_score": 0-10,
"memorability_score": 0-10,
"relevance_score": 0-10,
"overall_effectiveness": "Overall effectiveness description"
}}
}}
]
}}
Return valid JSON format and fill all fields.
Include theory-specific characteristics in the theory_specific section while maintaining the unified structure.
All content should be in English.
"""
# ํต์ผ๋ ๊ธฐ๋ณธ ํ๋กฌํํธ ํ
ํ๋ฆฟ (ํ๊ตญ์ด)
UNIFIED_BASE_PROMPT_KO = """
๋น์ ์ {theory_name} ์ ๋ฌธ๊ฐ์
๋๋ค. {theory_description}
์ฌ์ฉ์ ์
๋ ฅ(์
์ข
/ํค์๋)์ ๋ฐ์ ๋ค์๊ณผ ๊ฐ์ ํต์ผ๋ JSON ํ์์ ๋ฐฐ์ด์ ์์ฑํ์ธ์:
{{
"brands": [
{{
"core": {{
"brand_name": "ํ๊ธ ๋ธ๋๋๋ช
",
"slogan": "์ฌ๋ก๊ฑด",
"core_value": "ํต์ฌ ๊ฐ์น",
"target_emotion": "๋ชฉํ ๊ฐ์ ",
"brand_personality": "๋ธ๋๋ ์ฑ๊ฒฉ"
}},
"visual": {{
"primary_color": "#HEX์ฝ๋",
"color_meaning": "์์ ์๋ฏธ",
"visual_concept": "์๊ฐ์ ์ปจ์
",
"typography_style": "ํ์ดํฌ๊ทธ๋ํผ ์คํ์ผ"
}},
"linguistic": {{
"pronunciation": "ํ๊ธ ๋ฐ์ ๊ฐ์ด๋",
"etymology": "์ด์/๊ตฌ์ฑ (์์ด๋ช
ํฌํจ)",
"global_adaptability": "์์ด ๋ธ๋๋๋ช
(์: Mother's Garden)",
"memorable_factor": "๊ธฐ์ต ์ฉ์ด์ฑ ์์"
}},
"strategic": {{
"differentiation": "์ฐจ๋ณํ ํฌ์ธํธ",
"market_positioning": "์์ฅ ํฌ์ง์
๋",
"growth_potential": "์ฑ์ฅ ์ ์ฌ๋ ฅ",
"implementation_ease": "์คํ ์ฉ์ด์ฑ"
}},
"theory_specific": {{
{theory_specific_fields}
}},
"evaluation": {{
"creativity_score": 0-10,
"memorability_score": 0-10,
"relevance_score": 0-10,
"overall_effectiveness": "์ ์ฒด์ ์ธ ํจ๊ณผ์ฑ ์ค๋ช
"
}}
}}
]
}}
์ค์:
1. brand_name์ ๋ฐ๋์ ํ๊ธ๋ก ์์ฑํ์ธ์.
2. linguistic > global_adaptability์๋ ๋ฐ๋์ ์์ด ๋ธ๋๋๋ช
์ ํฌํจํ์ธ์.
3. ์์ด๋ช
์ ํ๊ธ๋ช
์ ์๋ฏธ๋ฅผ ์ ์ ๋ฌํ๋, ๊ธ๋ก๋ฒํ๊ฒ ์ฌ์ฉ ๊ฐ๋ฅํ ์ด๋ฆ์ผ๋ก ๋ง๋์ธ์.
4. ๋ชจ๋ ํ๋๋ฅผ ํ๊ตญ์ด๋ก ์์ฑํ๋, ์์ด ๋ธ๋๋๋ช
๋ง ์์ด๋ก ํ๊ธฐํ์ธ์.
"""
# ์ด๋ก ๋ณ ํน์ ํ๋ ์ ์ (์์ด)
THEORY_SPECIFIC_FIELDS_EN = {
"square": """
"tl": "Top Left",
"tr": "Top Right",
"bl": "Bottom Left",
"br": "Bottom Right",
"top_edge": "Top Relationship",
"bottom_edge": "Bottom Relationship",
"left_edge": "Left Relationship",
"right_edge": "Right Relationship",
"diagonal_insight": "Diagonal Insight"
""",
"blending": """
"input_space1": "First Concept",
"input_space2": "Second Concept",
"generic_space": "Common Structure",
"blended_space": "Blended New Meaning",
"emergent_properties": "Emergent Properties",
"blend_ratio": "Blend Ratio"
""",
"sound": """
"phonetic_analysis": "Phonetic Analysis",
"sound_meaning": "Sound Meaning",
"vowel_consonant_ratio": "Vowel/Consonant Ratio",
"phoneme_emotion_map": "Phoneme-Emotion Mapping",
"cross_linguistic_sound": "Cross-linguistic Sound Consistency"
""",
"linguistic": """
"korean_adaptation": "Korean Adaptation",
"english_meaning": "English Meaning",
"cultural_considerations": "Cultural Considerations",
"avoid_meanings": "Meanings to Avoid",
"localization_strategy": "Localization Strategy"
""",
"archetype": """
"archetype": "Selected Archetype",
"archetype_traits": "Archetype Traits",
"shadow_side": "Shadow Side",
"mythology_reference": "Mythological Reference",
"customer_journey": "Customer Journey Connection"
""",
"jobs": """
"functional_job": "Functional Job",
"emotional_job": "Emotional Job",
"social_job": "Social Job",
"job_statement": "Core Job Statement",
"outcome_metrics": "Outcome Metrics"
""",
"scamper": """
"scamper_technique": "Technique Used",
"original_concept": "Original Concept",
"transformation": "Transformation Process",
"innovation_type": "Innovation Type",
"disruption_level": "Disruption Level"
""",
"design": """
"user_insight": "User Insight",
"pain_point": "Pain Point Solved",
"desirability": "Desirability (Human)",
"feasibility": "Feasibility (Technical)",
"viability": "Viability (Business)"
""",
"biomimicry": """
"natural_inspiration": "Natural Inspiration",
"biomimetic_principle": "Biomimetic Principle",
"form_function": "Form and Function",
"sustainability_aspect": "Sustainability Aspect",
"adaptation_strategy": "Adaptation Strategy"
""",
"cognitive": """
"syllable_count": "Syllable Count",
"processing_ease": "Processing Ease Score",
"memory_hooks": "Memory Hooks",
"cognitive_fluency": "Cognitive Fluency",
"attention_span_fit": "Attention Span Fit"
""",
"vonrestorff": """
"category_norm": "Category Norm",
"deviation_strategy": "Deviation Strategy",
"uniqueness_factors": "Uniqueness Factors",
"attention_triggers": "Attention Triggers",
"isolation_effect": "Isolation Effect Usage"
""",
"network": """
"network_type": "Network Type",
"viral_coefficient": "Viral Coefficient",
"sharing_ease": "Sharing Ease",
"community_aspect": "Community Aspect",
"network_value": "Network Value"
""",
"memetics": """
"meme_structure": "Meme Structure",
"replication_ease": "Replication Ease",
"mutation_potential": "Mutation Potential",
"cultural_fitness": "Cultural Fitness",
"transmission_channels": "Transmission Channels"
""",
"color": """
"color_palette": "Color Palette",
"emotional_response": "Emotional Response",
"cultural_associations": "Cultural Associations",
"industry_alignment": "Industry Alignment",
"color_accessibility": "Color Accessibility"
""",
"gestalt": """
"gestalt_principle": "Principle Used",
"visual_structure": "Visual Structure",
"perceptual_grouping": "Perceptual Grouping",
"figure_ground": "Figure-Ground Relationship",
"closure_effect": "Closure Effect"
"""
}
# ์ด๋ก ๋ณ ํน์ ํ๋ ์ ์ (ํ๊ตญ์ด)
THEORY_SPECIFIC_FIELDS_KO = {
"square": """
"tl": "์ผ์ชฝ์๋จ",
"tr": "์ค๋ฅธ์ชฝ์๋จ",
"bl": "์ผ์ชฝํ๋จ",
"br": "์ค๋ฅธ์ชฝํ๋จ",
"top_edge": "์๋จ ๊ด๊ณ",
"bottom_edge": "ํ๋จ ๊ด๊ณ",
"left_edge": "์ผ์ชฝ ๊ด๊ณ",
"right_edge": "์ค๋ฅธ์ชฝ ๊ด๊ณ",
"diagonal_insight": "๋๊ฐ์ ํต์ฐฐ"
""",
"blending": """
"input_space1": "์ฒซ ๋ฒ์งธ ๊ฐ๋
",
"input_space2": "๋ ๋ฒ์งธ ๊ฐ๋
",
"generic_space": "๊ณตํต ๊ตฌ์กฐ",
"blended_space": "ํผํฉ๋ ์๋ก์ด ์๋ฏธ",
"emergent_properties": "์ฐฝ๋ฐ์ ์์ฑ๋ค",
"blend_ratio": "ํผํฉ ๋น์จ"
""",
"sound": """
"phonetic_analysis": "์์ฑ ๋ถ์",
"sound_meaning": "์ํฅ์ด ์ ๋ฌํ๋ ์๋ฏธ",
"vowel_consonant_ratio": "๋ชจ์/์์ ๋น์จ",
"phoneme_emotion_map": "์์-๊ฐ์ ๋งคํ",
"cross_linguistic_sound": "์ธ์ด๊ฐ ์ํฅ ์ผ๊ด์ฑ"
""",
"linguistic": """
"korean_adaptation": "ํ๊ตญ์ด ์ ์",
"english_meaning": "์์ด ๋ธ๋๋๋ช
",
"cultural_considerations": "๋ฌธํ์ ๊ณ ๋ ค์ฌํญ",
"avoid_meanings": "ํผํด์ผ ํ ์๋ฏธ๋ค",
"localization_strategy": "ํ์งํ ์ ๋ต"
""",
"archetype": """
"archetype": "์ ํ๋ ์ํ",
"archetype_traits": "์ํ์ ํน์ง๋ค",
"shadow_side": "๊ทธ๋ฆผ์ ์ธก๋ฉด",
"mythology_reference": "์ ํ์ ์ฐธ์กฐ",
"customer_journey": "๊ณ ๊ฐ ์ฌ์ ์ฐ๊ฒฐ"
""",
"jobs": """
"functional_job": "๊ธฐ๋ฅ์ ์ผ",
"emotional_job": "๊ฐ์ ์ ์ผ",
"social_job": "์ฌํ์ ์ผ",
"job_statement": "ํต์ฌ Job ๋ฌธ์ฅ",
"outcome_metrics": "์ฑ๊ณผ ์งํ"
""",
"scamper": """
"scamper_technique": "์ฌ์ฉ๋ ๊ธฐ๋ฒ",
"original_concept": "์๋ ๊ฐ๋
",
"transformation": "๋ณํ ๊ณผ์ ",
"innovation_type": "ํ์ ์ ํ",
"disruption_level": "ํ๊ดด์ ํ์ ์์ค"
""",
"design": """
"user_insight": "์ฌ์ฉ์ ํต์ฐฐ",
"pain_point": "ํด๊ฒฐํ๋ ๋ฌธ์ ์ ",
"desirability": "๋ฐ๋์งํจ (์ธ๊ฐ)",
"feasibility": "์คํ๊ฐ๋ฅ์ฑ (๊ธฐ์ )",
"viability": "์์กด๊ฐ๋ฅ์ฑ (๋น์ฆ๋์ค)"
""",
"biomimicry": """
"natural_inspiration": "์์ฐ์ ์๊ฐ์",
"biomimetic_principle": "์์ฒด๋ชจ๋ฐฉ ์๋ฆฌ",
"form_function": "ํํ์ ๊ธฐ๋ฅ",
"sustainability_aspect": "์ง์๊ฐ๋ฅ์ฑ ์ธก๋ฉด",
"adaptation_strategy": "์ ์ ์ ๋ต"
""",
"cognitive": """
"syllable_count": "์์ ์",
"processing_ease": "์ฒ๋ฆฌ ์ฉ์ด์ฑ ์ ์",
"memory_hooks": "๊ธฐ์ต ๊ณ ๋ฆฌ",
"cognitive_fluency": "์ธ์ง์ ์ ์ฐฝ์ฑ",
"attention_span_fit": "์ฃผ์๋ ฅ ์ ํฉ๋"
""",
"vonrestorff": """
"category_norm": "์นดํ
๊ณ ๋ฆฌ ํ์ค",
"deviation_strategy": "์ผํ ์ ๋ต",
"uniqueness_factors": "๋
ํน์ฑ ์์๋ค",
"attention_triggers": "์ฃผ์ ํธ๋ฆฌ๊ฑฐ",
"isolation_effect": "๊ณ ๋ฆฝ ํจ๊ณผ ํ์ฉ"
""",
"network": """
"network_type": "๋คํธ์ํฌ ์ ํ",
"viral_coefficient": "๋ฐ์ด๋ด ๊ณ์",
"sharing_ease": "๊ณต์ ์ฉ์ด์ฑ",
"community_aspect": "์ปค๋ฎค๋ํฐ ์ธก๋ฉด",
"network_value": "๋คํธ์ํฌ ๊ฐ์น"
""",
"memetics": """
"meme_structure": "๋ฐ ๊ตฌ์กฐ",
"replication_ease": "๋ณต์ ์ฉ์ด์ฑ",
"mutation_potential": "๋ณ์ด ์ ์ฌ๋ ฅ",
"cultural_fitness": "๋ฌธํ์ ์ ํฉ๋",
"transmission_channels": "์ ๋ฌ ์ฑ๋"
""",
"color": """
"color_palette": "์์ ํ๋ ํธ",
"emotional_response": "๊ฐ์ ์ ๋ฐ์",
"cultural_associations": "๋ฌธํ์ ์ฐ์",
"industry_alignment": "์
์ข
์ ๋ ฌ",
"color_accessibility": "์์ ์ ๊ทผ์ฑ"
""",
"gestalt": """
"gestalt_principle": "ํ์ฉ ์์น",
"visual_structure": "์๊ฐ์ ๊ตฌ์กฐ",
"perceptual_grouping": "์ง๊ฐ์ ๊ทธ๋ฃนํ",
"figure_ground": "์ ๊ฒฝ-๋ฐฐ๊ฒฝ ๊ด๊ณ",
"closure_effect": "ํ์ ํจ๊ณผ"
"""
}
def create_theory_prompt(theory: str, language: str) -> str:
"""๊ฐ ์ด๋ก ๋ณ ํต์ผ๋ ํ๋กฌํํธ ์์ฑ"""
theory_names = {
"en": {
"square": "Square Theory",
"blending": "Conceptual Blending Theory",
"sound": "Sound Symbolism",
"linguistic": "Linguistic Relativity",
"archetype": "Jung's Archetype Theory",
"jobs": "Jobs-to-be-Done Theory",
"scamper": "SCAMPER Method",
"design": "IDEO's Design Thinking",
"biomimicry": "Biomimicry",
"cognitive": "Cognitive Load Theory",
"vonrestorff": "Von Restorff Effect",
"network": "Network Effects",
"memetics": "Memetics",
"color": "Color Psychology",
"gestalt": "Gestalt Theory"
},
"ko": {
"square": "Square Theory",
"blending": "Conceptual Blending Theory",
"sound": "Sound Symbolism",
"linguistic": "Linguistic Relativity",
"archetype": "Jung์ Archetype Theory",
"jobs": "Jobs-to-be-Done Theory",
"scamper": "SCAMPER Method",
"design": "IDEO์ Design Thinking",
"biomimicry": "Biomimicry",
"cognitive": "Cognitive Load Theory",
"vonrestorff": "Von Restorff Effect",
"network": "Network Effects",
"memetics": "Memetics",
"color": "Color Psychology",
"gestalt": "Gestalt Theory"
}
}
base_prompt = UNIFIED_BASE_PROMPT_EN if language == "en" else UNIFIED_BASE_PROMPT_KO
theory_specific_fields = THEORY_SPECIFIC_FIELDS_EN if language == "en" else THEORY_SPECIFIC_FIELDS_KO
return base_prompt.format(
theory_name=theory_names[language][theory],
theory_description=THEORY_DESCRIPTIONS[language][theory],
theory_specific_fields=theory_specific_fields[theory]
)
def generate_by_theory(industry: str, keywords: str, theory: str, language: str, count: int = 3) -> Tuple[str, str, gr.update]:
"""ํน์ ์ด๋ก ์ผ๋ก ๋ธ๋๋ ์์ฑ"""
texts = TEXTS[language]
if not industry or not keywords:
return texts["input_required"], "", gr.update(visible=False)
prompt = create_theory_prompt(theory, language)
if language == "en":
user_input = f"""Industry: {industry}
Keywords: {keywords}
Generate {count} brands with the above information.
Each brand should have a unified structure, with theory-specific characteristics in the theory_specific section."""
else:
user_input = f"""์
์ข
: {industry}
ํค์๋: {keywords}
์ ์ ๋ณด๋ก {count}๊ฐ์ ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์.
๊ฐ ๋ธ๋๋๋ ํต์ผ๋ ๊ตฌ์กฐ๋ฅผ ๊ฐ์ง๋, theory_specific ์น์
์๋ {theory} ์ด๋ก ์ ํน์ฑ์ ๋ฐ์ํ์ธ์.
์ค์: linguistic > global_adaptability ํ๋์ ๋ฐ๋์ ์์ด ๋ธ๋๋๋ช
์ ํฌํจํ์ธ์."""
try:
# ํ๋ก๊ทธ๋ ์ค๋ฐ ํ์
theory_names = {
"square": "Square Theory",
"blending": "Conceptual Blending",
"sound": "Sound Symbolism",
"linguistic": "Linguistic Relativity",
"archetype": "Archetype Theory",
"jobs": "Jobs-to-be-Done",
"scamper": "SCAMPER Method",
"design": "Design Thinking",
"biomimicry": "Biomimicry",
"cognitive": "Cognitive Load Theory",
"vonrestorff": "Von Restorff Effect",
"network": "Network Effects",
"memetics": "Memetics",
"color": "Color Psychology",
"gestalt": "Gestalt Principles"
}
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": prompt},
{"role": "user", "content": user_input}
],
temperature=0.8,
max_tokens=2000,
response_format={"type": "json_object"}
)
content = response.choices[0].message.content
data = json.loads(content)
# ์๋ต ์ ๊ทํ
if "brands" in data:
results = data["brands"]
else:
results = [data]
if not isinstance(results, list):
results = [results]
# ๋งํฌ๋ค์ด ์์ฑ
markdown = generate_unified_markdown(theory, results, industry, keywords, language)
# HTML ์๊ฐํ ์์ฑ
html = generate_unified_visualization(theory, results, language)
return markdown, html, gr.update(visible=False)
except Exception as e:
error_msg = texts["error_message"].format(theory=theory_names[theory], error=str(e))
print(error_msg)
return error_msg, "", gr.update(visible=False)
def generate_unified_markdown(theory: str, results: List[Dict], industry: str, keywords: str, language: str) -> str:
"""ํต์ผ๋ ๋งํฌ๋ค์ด ์์ฑ"""
theory_names = {
"square": "Square Theory",
"blending": "Conceptual Blending",
"sound": "Sound Symbolism",
"linguistic": "Linguistic Relativity",
"archetype": "Archetype Theory",
"jobs": "Jobs-to-be-Done",
"scamper": "SCAMPER Method",
"design": "Design Thinking",
"biomimicry": "Biomimicry",
"cognitive": "Cognitive Load Theory",
"vonrestorff": "Von Restorff Effect",
"network": "Network Effects",
"memetics": "Memetics",
"color": "Color Psychology",
"gestalt": "Gestalt Principles"
}
theory_icons = {
"square": "๐ฆ", "blending": "๐", "sound": "๐", "linguistic": "๐",
"archetype": "๐ญ", "jobs": "โ
", "scamper": "๐ง", "design": "๐ญ",
"biomimicry": "๐ฟ", "cognitive": "๐ง ", "vonrestorff": "โก", "network": "๐",
"memetics": "๐งฌ", "color": "๐จ", "gestalt": "๐๏ธ"
}
texts = TEXTS[language]
if language == "en":
markdown = f"""# {theory_icons[theory]} {theory_names[theory]}
<div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
<h3 style="margin: 0 0 10px 0;">Theory Overview</h3>
<p style="margin: 0; line-height: 1.6;">{THEORY_DESCRIPTIONS[language][theory]}</p>
</div>
**Industry**: {industry} | **Keywords**: {keywords}
*Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*
---
"""
else:
markdown = f"""# {theory_icons[theory]} {theory_names[theory]}
<div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
<h3 style="margin: 0 0 10px 0;">์ด๋ก ๊ฐ์</h3>
<p style="margin: 0; line-height: 1.6;">{THEORY_DESCRIPTIONS[language][theory]}</p>
</div>
**์
์ข
**: {industry} | **ํค์๋**: {keywords}
*์์ฑ ์๊ฐ: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*
---
"""
for idx, result in enumerate(results, 1):
core = result.get('core', {})
visual = result.get('visual', {})
linguistic = result.get('linguistic', {})
strategic = result.get('strategic', {})
theory_specific = result.get('theory_specific', {})
evaluation = result.get('evaluation', {})
brand_name = core.get('brand_name', 'N/A')
slogan = core.get('slogan', 'N/A')
# ํ๊ธ์ผ ๊ฒฝ์ฐ ์์ด ํํ ์ถ๊ฐ
if language == "ko":
# ์ฐ์ ์์์ ๋ฐ๋ผ ์์ด๋ช
์ฐพ๊ธฐ
english_name = None
# 1. linguistic theory์ ๊ฒฝ์ฐ theory_specific์์ ์ฐพ๊ธฐ
if theory == "linguistic" and 'english_meaning' in theory_specific:
english_name = theory_specific.get('english_meaning', '')
# 2. global_adaptability์์ ์ฐพ๊ธฐ
if not english_name or english_name == 'N/A':
english_name = linguistic.get('global_adaptability', '')
# 3. etymology์์ ์์ด ์ถ์ถ
if not english_name or english_name == 'N/A':
etymology = linguistic.get('etymology', '')
if etymology and '์์ด' in etymology and ':' in etymology:
# "์์ด: Brand Name" ํ์์์ ์ถ์ถ
parts = etymology.split(':')
for i, part in enumerate(parts):
if '์์ด' in part and i + 1 < len(parts):
english_name = parts[i + 1].strip().split(',')[0].strip()
break
# ์์ด๋ช
์ด ์ ํจํ ๊ฒฝ์ฐ์๋ง ์ถ๊ฐ
if english_name and english_name != 'N/A' and english_name != brand_name:
# ๊ธธ์ด๊ฐ ๋๋ฌด ๊ธธ๊ฑฐ๋ ์ค๋ช
์ด ํฌํจ๋ ๊ฒฝ์ฐ ์ ๋ฆฌ
if len(english_name) > 50 or '(' in english_name or ',' in english_name:
english_name = english_name.split('(')[0].split(',')[0].strip()
# ์์ด๋ช
์ด ์ค์ ๋ก ์์ด์ธ์ง ๊ฐ๋จํ ํ์ธ (ํ๊ธ์ด ํฌํจ๋์ง ์์๋์ง)
if not any(ord(char) >= 0xAC00 and ord(char) <= 0xD7A3 for char in english_name):
brand_display = f"{brand_name} / {english_name}"
else:
brand_display = brand_name
else:
brand_display = brand_name
else:
brand_display = brand_name
markdown += f"\n## {idx}. {brand_display}\n"
if language == "en":
markdown += f"""**Slogan**: *"{slogan}"*
### ๐ Core Information
- **Core Value**: {core.get('core_value', 'N/A')}
- **Target Emotion**: {core.get('target_emotion', 'N/A')}
- **Brand Personality**: {core.get('brand_personality', 'N/A')}
### ๐จ Visual Concept
- **Primary Color**: {visual.get('primary_color', '#000000')} - {visual.get('color_meaning', 'N/A')}
- **Visual Concept**: {visual.get('visual_concept', 'N/A')}
- **Typography**: {visual.get('typography_style', 'N/A')}
### ๐ฃ๏ธ Linguistic Features
- **Pronunciation**: {linguistic.get('pronunciation', 'N/A')}
- **Etymology**: {linguistic.get('etymology', 'N/A')}
- **Global Adaptability**: {linguistic.get('global_adaptability', 'N/A')}
### ๐ฏ Strategic Value
- **Differentiation**: {strategic.get('differentiation', 'N/A')}
- **Market Positioning**: {strategic.get('market_positioning', 'N/A')}
- **Growth Potential**: {strategic.get('growth_potential', 'N/A')}
"""
else:
markdown += f"""**์ฌ๋ก๊ฑด**: *"{slogan}"*
### ๐ ํต์ฌ ์ ๋ณด
- **ํต์ฌ ๊ฐ์น**: {core.get('core_value', 'N/A')}
- **๋ชฉํ ๊ฐ์ **: {core.get('target_emotion', 'N/A')}
- **๋ธ๋๋ ์ฑ๊ฒฉ**: {core.get('brand_personality', 'N/A')}
### ๐จ ์๊ฐ์ ์ปจ์
- **์ฃผ์ ์์**: {visual.get('primary_color', '#000000')} - {visual.get('color_meaning', 'N/A')}
- **๋น์ฃผ์ผ ์ปจ์
**: {visual.get('visual_concept', 'N/A')}
- **ํ์ดํฌ๊ทธ๋ํผ**: {visual.get('typography_style', 'N/A')}
### ๐ฃ๏ธ ์ธ์ด์ ํน์ฑ
- **๋ฐ์**: {linguistic.get('pronunciation', 'N/A')}
- **์ด์/๊ตฌ์ฑ**: {linguistic.get('etymology', 'N/A')}
- **๊ธ๋ก๋ฒ ์ ์์ฑ**: {linguistic.get('global_adaptability', 'N/A')}
### ๐ฏ ์ ๋ต์ ๊ฐ์น
- **์ฐจ๋ณํ ํฌ์ธํธ**: {strategic.get('differentiation', 'N/A')}
- **์์ฅ ํฌ์ง์
๋**: {strategic.get('market_positioning', 'N/A')}
- **์ฑ์ฅ ์ ์ฌ๋ ฅ**: {strategic.get('growth_potential', 'N/A')}
"""
# ์ด๋ก ๋ณ ํน์ ์ ๋ณด
if theory_specific:
theory_header = f"### ๐ก {theory_names[theory]} " + ("Features" if language == "en" else "ํน์ฑ") + "\n"
markdown += theory_header
for key, value in theory_specific.items():
display_key = key.replace('_', ' ').title()
markdown += f"- **{display_key}**: {value}\n"
# ํ๊ฐ ์ ์
eval_labels = texts["evaluation_labels"]
if language == "en":
markdown += f"""
### ๐ Evaluation
- **{eval_labels['creativity']}**: {'โญ' * int(evaluation.get('creativity_score', 0))} ({evaluation.get('creativity_score', 0)}/10)
- **{eval_labels['memorability']}**: {'โญ' * int(evaluation.get('memorability_score', 0))} ({evaluation.get('memorability_score', 0)}/10)
- **{eval_labels['relevance']}**: {'โญ' * int(evaluation.get('relevance_score', 0))} ({evaluation.get('relevance_score', 0)}/10)
๐ฌ **Overall Assessment**: {evaluation.get('overall_effectiveness', 'N/A')}
"""
else:
markdown += f"""
### ๐ ํ๊ฐ
- **{eval_labels['creativity']}**: {'โญ' * int(evaluation.get('creativity_score', 0))} ({evaluation.get('creativity_score', 0)}/10)
- **{eval_labels['memorability']}**: {'โญ' * int(evaluation.get('memorability_score', 0))} ({evaluation.get('memorability_score', 0)}/10)
- **{eval_labels['relevance']}**: {'โญ' * int(evaluation.get('relevance_score', 0))} ({evaluation.get('relevance_score', 0)}/10)
๐ฌ **์ ์ฒด ํ๊ฐ**: {evaluation.get('overall_effectiveness', 'N/A')}
"""
markdown += "\n---\n"
return markdown
def generate_unified_visualization(theory: str, results: List[Dict], language: str) -> str:
"""ํต์ผ๋ ์๊ฐํ ์์ฑ"""
texts = TEXTS[language]
eval_labels = texts["evaluation_labels"]
html_parts = []
for idx, result in enumerate(results, 1):
core = result.get('core', {})
visual = result.get('visual', {})
linguistic = result.get('linguistic', {})
strategic = result.get('strategic', {})
theory_specific = result.get('theory_specific', {})
evaluation = result.get('evaluation', {})
brand_name = core.get('brand_name', 'Brand')
slogan = core.get('slogan', '')
primary_color = visual.get('primary_color', '#667eea')
# ํ๊ธ์ผ ๊ฒฝ์ฐ ์์ด ํํ ์ถ๊ฐ
if language == "ko":
# ์ฐ์ ์์์ ๋ฐ๋ผ ์์ด๋ช
์ฐพ๊ธฐ
english_name = None
# 1. linguistic theory์ ๊ฒฝ์ฐ theory_specific์์ ์ฐพ๊ธฐ
if theory == "linguistic" and 'english_meaning' in theory_specific:
english_name = theory_specific.get('english_meaning', '')
# 2. global_adaptability์์ ์ฐพ๊ธฐ
if not english_name or english_name == 'N/A':
english_name = linguistic.get('global_adaptability', '')
# 3. etymology์์ ์์ด ์ถ์ถ
if not english_name or english_name == 'N/A':
etymology = linguistic.get('etymology', '')
if etymology and '์์ด' in etymology and ':' in etymology:
# "์์ด: Brand Name" ํ์์์ ์ถ์ถ
parts = etymology.split(':')
for i, part in enumerate(parts):
if '์์ด' in part and i + 1 < len(parts):
english_name = parts[i + 1].strip().split(',')[0].strip()
break
# ์์ด๋ช
์ด ์ ํจํ ๊ฒฝ์ฐ์๋ง ์ถ๊ฐ
if english_name and english_name != 'N/A' and english_name != brand_name:
# ๊ธธ์ด๊ฐ ๋๋ฌด ๊ธธ๊ฑฐ๋ ์ค๋ช
์ด ํฌํจ๋ ๊ฒฝ์ฐ ์ ๋ฆฌ
if len(english_name) > 50 or '(' in english_name or ',' in english_name:
english_name = english_name.split('(')[0].split(',')[0].strip()
# ์์ด๋ช
์ด ์ค์ ๋ก ์์ด์ธ์ง ๊ฐ๋จํ ํ์ธ (ํ๊ธ์ด ํฌํจ๋์ง ์์๋์ง)
if not any(ord(char) >= 0xAC00 and ord(char) <= 0xD7A3 for char in english_name):
brand_display = f"{brand_name} / {english_name}"
else:
brand_display = brand_name
else:
brand_display = brand_name
else:
brand_display = brand_name
# ์ธ์ด๋ณ ๋ผ๋ฒจ
if language == "en":
labels = {
"core_value": "Core Value",
"target_emotion": "Target Emotion",
"differentiation": "Differentiation",
"pronunciation": "Pronunciation"
}
else:
labels = {
"core_value": "ํต์ฌ ๊ฐ์น",
"target_emotion": "๋ชฉํ ๊ฐ์ ",
"differentiation": "์ฐจ๋ณํ ํฌ์ธํธ",
"pronunciation": "๋ฐ์ ๊ฐ์ด๋"
}
# ํต์ผ๋ ์นด๋ ๋ ์ด์์
html = f"""
<div style="max-width: 800px; margin: 30px auto; font-family: -apple-system, sans-serif;">
<div style="background: white; border-radius: 20px; box-shadow: 0 10px 40px rgba(0,0,0,0.1); overflow: hidden;">
<!-- ํค๋ -->
<div style="background: linear-gradient(135deg, {primary_color} 0%, #2c3e50 100%); padding: 40px; color: white;">
<h2 style="margin: 0 0 10px 0; font-size: 2.5em;">{brand_display}</h2>
<p style="margin: 0; font-style: italic; font-size: 1.2em; opacity: 0.9;">"{slogan}"</p>
</div>
<!-- ๋ณธ๋ฌธ -->
<div style="padding: 40px;">
<!-- ํ๊ฐ ์ ์ -->
<div style="display: flex; justify-content: space-around; margin-bottom: 30px;">
<div style="text-align: center;">
<div style="font-size: 2em; color: {primary_color}; font-weight: bold;">
{evaluation.get('creativity_score', 0)}/10
</div>
<div style="color: #7f8c8d; margin-top: 5px;">{eval_labels['creativity']}</div>
</div>
<div style="text-align: center;">
<div style="font-size: 2em; color: {primary_color}; font-weight: bold;">
{evaluation.get('memorability_score', 0)}/10
</div>
<div style="color: #7f8c8d; margin-top: 5px;">{eval_labels['memorability']}</div>
</div>
<div style="text-align: center;">
<div style="font-size: 2em; color: {primary_color}; font-weight: bold;">
{evaluation.get('relevance_score', 0)}/10
</div>
<div style="color: #7f8c8d; margin-top: 5px;">{eval_labels['relevance']}</div>
</div>
</div>
<!-- ํต์ฌ ์ ๋ณด ๊ทธ๋ฆฌ๋ -->
<div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 20px; margin-bottom: 30px;">
<div style="background: #f8f9fa; padding: 20px; border-radius: 10px;">
<h4 style="margin: 0 0 10px 0; color: {primary_color};">{labels['core_value']}</h4>
<p style="margin: 0; color: #555;">{core.get('core_value', 'N/A')}</p>
</div>
<div style="background: #f8f9fa; padding: 20px; border-radius: 10px;">
<h4 style="margin: 0 0 10px 0; color: {primary_color};">{labels['target_emotion']}</h4>
<p style="margin: 0; color: #555;">{core.get('target_emotion', 'N/A')}</p>
</div>
<div style="background: #f8f9fa; padding: 20px; border-radius: 10px;">
<h4 style="margin: 0 0 10px 0; color: {primary_color};">{labels['differentiation']}</h4>
<p style="margin: 0; color: #555;">{strategic.get('differentiation', 'N/A')}</p>
</div>
<div style="background: #f8f9fa; padding: 20px; border-radius: 10px;">
<h4 style="margin: 0 0 10px 0; color: {primary_color};">{labels['pronunciation']}</h4>
<p style="margin: 0; color: #555;">{linguistic.get('pronunciation', 'N/A')}</p>
</div>
</div>
"""
# ์ด๋ก ๋ณ ํน์ ์๊ฐํ ์ถ๊ฐ
if theory == "square" and all(k in theory_specific for k in ['tl', 'tr', 'bl', 'br']):
html += visualize_square_specific(theory_specific, primary_color)
elif theory == "blending" and 'input_space1' in theory_specific:
html += visualize_blending_specific(theory_specific, primary_color)
elif theory == "color" and 'color_palette' in theory_specific:
html += visualize_color_specific(theory_specific, primary_color)
else:
# ๊ธฐ๋ณธ ์ด๋ก ํน์ฑ ํ์
theory_header = "Theory Features" if language == "en" else "์ด๋ก ํน์ฑ"
html += f"""
<div style="background: linear-gradient(135deg, {primary_color}15 0%, {primary_color}05 100%); padding: 25px; border-radius: 15px; margin-top: 20px;">
<h4 style="margin: 0 0 15px 0; color: {primary_color};">{theory_header}</h4>
<div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 15px;">
"""
for key, value in theory_specific.items():
display_key = key.replace('_', ' ').title()
html += f"""
<div>
<strong style="color: #555;">{display_key}:</strong><br>
<span style="color: #777;">{value}</span>
</div>
"""
html += """
</div>
</div>
"""
# ์ ์ฒด ํ๊ฐ
overall_header = "Overall Assessment" if language == "en" else "์ ์ฒด ํ๊ฐ"
html += f"""
<div style="background: #f8f9fa; padding: 20px; border-radius: 10px; margin-top: 20px;">
<h4 style="margin: 0 0 10px 0; color: {primary_color};">{overall_header}</h4>
<p style="margin: 0; color: #555; line-height: 1.6;">{evaluation.get('overall_effectiveness', 'N/A')}</p>
</div>
</div>
</div>
</div>
"""
html_parts.append(html)
return "\n".join(html_parts)
def visualize_square_specific(theory_specific: Dict, primary_color: str) -> str:
"""Square Theory ํน์ ์๊ฐํ"""
return f"""
<div style="background: #f8f9fa; padding: 30px; border-radius: 15px; margin-top: 20px;">
<h4 style="margin: 0 0 20px 0; color: {primary_color}; text-align: center;">Square Structure</h4>
<div style="position: relative; width: 100%; max-width: 400px; height: 300px; margin: 0 auto;">
<div style="position: absolute; top: 0; left: 0; background: white; color: {primary_color}; padding: 15px 20px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); font-weight: bold;">
{theory_specific.get('tl', '?')}
</div>
<div style="position: absolute; top: 0; right: 0; background: white; color: {primary_color}; padding: 15px 20px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); font-weight: bold;">
{theory_specific.get('tr', '?')}
</div>
<div style="position: absolute; bottom: 0; left: 0; background: white; color: {primary_color}; padding: 15px 20px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); font-weight: bold;">
{theory_specific.get('bl', '?')}
</div>
<div style="position: absolute; bottom: 0; right: 0; background: white; color: {primary_color}; padding: 15px 20px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); font-weight: bold;">
{theory_specific.get('br', '?')}
</div>
<!-- ์ฐ๊ฒฐ์ ๊ณผ ๊ด๊ณ ํ์ -->
<div style="position: absolute; top: 25px; left: 50%; transform: translateX(-50%); color: #7f8c8d; font-size: 0.9em;">
{theory_specific.get('top_edge', '')}
</div>
<div style="position: absolute; bottom: 25px; left: 50%; transform: translateX(-50%); color: #7f8c8d; font-size: 0.9em;">
{theory_specific.get('bottom_edge', '')}
</div>
<div style="position: absolute; top: 50%; left: 25px; transform: translateY(-50%) rotate(-90deg); color: #7f8c8d; font-size: 0.9em;">
{theory_specific.get('left_edge', '')}
</div>
<div style="position: absolute; top: 50%; right: 25px; transform: translateY(-50%) rotate(90deg); color: #7f8c8d; font-size: 0.9em;">
{theory_specific.get('right_edge', '')}
</div>
</div>
</div>
"""
def visualize_blending_specific(theory_specific: Dict, primary_color: str) -> str:
"""Conceptual Blending ํน์ ์๊ฐํ"""
return f"""
<div style="background: #f8f9fa; padding: 30px; border-radius: 15px; margin-top: 20px;">
<h4 style="margin: 0 0 20px 0; color: {primary_color}; text-align: center;">Concept Blending</h4>
<div style="display: flex; justify-content: center; align-items: center; gap: 20px; flex-wrap: wrap;">
<div style="text-align: center; padding: 20px; background: white; color: {primary_color}; border-radius: 50%; width: 120px; height: 120px; display: flex; align-items: center; justify-content: center; box-shadow: 0 2px 10px rgba(0,0,0,0.1);">
<div>
<strong>Input 1</strong><br>
<span style="font-size: 0.9em;">{theory_specific.get('input_space1', '')}</span>
</div>
</div>
<div style="font-size: 2em; color: {primary_color};">+</div>
<div style="text-align: center; padding: 20px; background: white; color: {primary_color}; border-radius: 50%; width: 120px; height: 120px; display: flex; align-items: center; justify-content: center; box-shadow: 0 2px 10px rgba(0,0,0,0.1);">
<div>
<strong>Input 2</strong><br>
<span style="font-size: 0.9em;">{theory_specific.get('input_space2', '')}</span>
</div>
</div>
<div style="font-size: 2em; color: {primary_color};">=</div>
<div style="text-align: center; padding: 20px; background: {primary_color}; color: white; border-radius: 20px; min-width: 150px; box-shadow: 0 2px 10px rgba(0,0,0,0.1);">
<strong>Blend</strong><br>
<span style="font-size: 0.95em;">{theory_specific.get('blended_space', '')}</span>
</div>
</div>
</div>
"""
def visualize_color_specific(theory_specific: Dict, primary_color: str) -> str:
"""Color Psychology ํน์ ์๊ฐํ"""
palette = theory_specific.get('color_palette', primary_color)
colors = palette.split(',') if ',' in palette else [primary_color]
html = f"""
<div style="background: #f8f9fa; padding: 30px; border-radius: 15px; margin-top: 20px;">
<h4 style="margin: 0 0 20px 0; color: {primary_color}; text-align: center;">Color Palette</h4>
<div style="display: flex; justify-content: center; gap: 10px; flex-wrap: wrap;">
"""
for color in colors[:5]: # ์ต๋ 5๊ฐ ์์๋ง ํ์
color = color.strip()
html += f"""
<div style="width: 80px; height: 80px; background: {color}; border-radius: 10px; box-shadow: 0 2px 10px rgba(0,0,0,0.1);"></div>
"""
html += """
</div>
</div>
"""
return html
# Gradio UI
with gr.Blocks(
title="THEORIAโข - Theory-driven Naming AI",
theme=gr.themes.Soft(),
css="""
.gradio-container {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
max-width: 1400px !important;
}
/* ํญ ๋ค๋น๊ฒ์ด์
์คํ์ผ ๊ฐ์ */
.tab-nav {
display: grid !important;
grid-template-columns: repeat(5, 1fr) !important;
gap: 8px !important;
padding: 15px !important;
background: #f8f9fa !important;
border-radius: 10px !important;
margin-bottom: 20px !important;
}
.tab-nav button {
font-size: 0.8em !important;
padding: 10px 8px !important;
white-space: normal !important;
line-height: 1.3 !important;
height: auto !important;
min-height: 50px !important;
display: flex !important;
align-items: center !important;
justify-content: center !important;
text-align: center !important;
border-radius: 8px !important;
transition: all 0.3s ease !important;
}
.tab-nav button:hover {
transform: translateY(-2px) !important;
box-shadow: 0 4px 12px rgba(0,0,0,0.15) !important;
}
.tab-nav button.selected {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
color: white !important;
font-weight: 600 !important;
}
/* ๋ชจ๋ฐ์ผ ๋ฐ์ํ */
@media (max-width: 768px) {
.tab-nav {
grid-template-columns: repeat(3, 1fr) !important;
}
}
@media (max-width: 480px) {
.tab-nav {
grid-template-columns: repeat(2, 1fr) !important;
}
}
@keyframes pulse {
0% { opacity: 0.6; }
50% { opacity: 1; }
100% { opacity: 0.6; }
}
.progress-bar {
animation: pulse 1.5s ease-in-out infinite;
}
"""
) as demo:
# ์ธ์ด ์ํ ๊ด๋ฆฌ
current_language = gr.State(value="en")
def update_ui_language(language):
"""UI ์ธ์ด ์
๋ฐ์ดํธ"""
texts = TEXTS[language]
return (
language, # current_language state update
# Title section update
gr.update(value=f"""
<div style="text-align: center; padding: 30px 0;">
<h1 style="font-size: 3em; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; margin-bottom: 10px;">
{texts['title']}
</h1>
<p style="font-size: 1.4em; color: #7f8c8d; font-weight: 500;">{texts['subtitle']}</p>
<p style="font-size: 1.1em; color: #95a5a6; margin-top: 10px;">{texts['description']}</p>
</div>
"""),
gr.update(label=texts['industry_label'], placeholder=texts['industry_placeholder']),
gr.update(label=texts['keywords_label'], placeholder=texts['keywords_placeholder'], info=texts['keywords_info']),
)
# ํค๋
title_section = gr.Markdown("""
<div style="text-align: center; padding: 30px 0;">
<h1 style="font-size: 3em; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; margin-bottom: 10px;">
THEORIAโข
</h1>
<p style="font-size: 1.4em; color: #7f8c8d; font-weight: 500;">Theory-driven Naming AI with 15 Specialized Theories</p>
<p style="font-size: 1.1em; color: #95a5a6; margin-top: 10px;">Generate innovative brand names using 15 cognitive and creative theories</p>
</div>
""")
with gr.Row():
with gr.Column(scale=1, min_width=250):
gr.Markdown("""
<div style="background: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
<h3 style="margin-top: 0; color: #2c3e50;">๐ Brand Information</h3>
</div>
""")
# ์ธ์ด ์ ํ
language_selector = gr.Radio(
choices=[("English", "en"), ("ํ๊ตญ์ด", "ko")],
value="en",
label="๐ Language",
info="Select output language"
)
industry_input = gr.Textbox(
label="๐ญ Industry",
placeholder="e.g., cafe, fitness, education, beauty...",
value="์นดํ/์ปคํผ์"
)
keywords_input = gr.Textbox(
label="๐ Keywords",
placeholder="premium, comfortable, urban, eco-friendly...",
info="Core values or characteristics the brand should embody",
lines=2
)
# ์ธ์ด ๋ณ๊ฒฝ ์ด๋ฒคํธ
language_selector.change(
update_ui_language,
inputs=[language_selector],
outputs=[current_language, title_section, industry_input, keywords_input]
)
gr.Markdown("""
<div style="background: #e3f2fd; padding: 15px; border-radius: 8px; margin-top: 20px;">
<h4 style="margin-top: 0; color: #1976d2;">๐ฏ 15 Theories</h4>
<p style="margin: 10px 0; font-size: 0.9em;">Each theory offers unique approach:</p>
<ul style="margin: 5px 0; padding-left: 20px; font-size: 0.8em; line-height: 1.4;">
<li><strong>Cognitive</strong>: Square, Sound, Cognitive, Gestalt</li>
<li><strong>Creative</strong>: Blending, SCAMPER, Biomimicry</li>
<li><strong>Strategic</strong>: Jobs-to-be-Done, Design</li>
<li><strong>Cultural</strong>: Archetype, Linguistic, Memetics</li>
<li><strong>Distinctive</strong>: Von Restorff, Color, Network</li>
</ul>
</div>
<div style="background: #fff3cd; padding: 15px; border-radius: 8px; margin-top: 15px;">
<h4 style="margin-top: 0; color: #856404;">๐ก Tips</h4>
<ul style="margin: 5px 0; padding-left: 20px; font-size: 0.8em; line-height: 1.4;">
<li>Try multiple theories</li>
<li>Compare results</li>
<li>Combine insights</li>
</ul>
</div>
""")
with gr.Column(scale=4):
# 15๊ฐ ํญ์ 3๊ฐ ํ์ผ๋ก ๊ตฌ์ฑ
gr.Markdown("""
<div style="background: #f8f9fa; padding: 15px; border-radius: 10px; margin-bottom: 20px;">
<h3 style="margin: 0; color: #2c3e50; text-align: center;">Select a Theory to Generate Names</h3>
</div>
""")
# 15๊ฐ ํญ ์์ฑ
with gr.Tabs(elem_classes="tab-nav"):
theories = [
("๐ฆ Square Theory", "square"),
("๐ Conceptual Blending", "blending"),
("๐ Sound Symbolism", "sound"),
("๐ Linguistic Relativity", "linguistic"),
("๐ญ Archetype Theory", "archetype"),
("โ
Jobs-to-be-Done", "jobs"),
("๐ง SCAMPER Method", "scamper"),
("๐ญ Design Thinking", "design"),
("๐ฟ Biomimicry", "biomimicry"),
("๐ง Cognitive Load", "cognitive"),
("โก Von Restorff Effect", "vonrestorff"),
("๐ Network Effects", "network"),
("๐งฌ Memetics", "memetics"),
("๐จ Color Psychology", "color"),
("๐๏ธ Gestalt Principles", "gestalt")
]
for tab_name, theory_key in theories:
with gr.Tab(tab_name):
with gr.Column():
# ํ๋ก๊ทธ๋ ์ค ๋ฉ์์ง
progress_msg = gr.Markdown(
visible=False
)
with gr.Row():
btn = gr.Button(
f"Generate with {tab_name}",
variant="primary",
size="lg",
elem_id=f"btn_{theory_key}"
)
output = gr.Markdown()
visual = gr.HTML()
def show_progress(industry, keywords, theory, language, tab_name):
"""ํ๋ก๊ทธ๋ ์ค๋ฐ ํ์"""
if not industry or not keywords:
return "", "", gr.update(visible=False)
texts = TEXTS[language]
progress_text = texts["progress_message"].format(theory=tab_name)
progress_html = f"""
<div style="text-align: center; padding: 20px; background: #f0f8ff; border-radius: 10px; margin: 10px 0;">
<div class="progress-bar" style="font-size: 1.2em; color: #1976d2;">
{progress_text}
</div>
<div style="margin-top: 10px;">
<div style="width: 100%; background: #e0e0e0; border-radius: 5px; overflow: hidden;">
<div style="width: 100%; height: 4px; background: linear-gradient(90deg, #1976d2 0%, #42a5f5 50%, #1976d2 100%); animation: slide 1.5s linear infinite;"></div>
</div>
</div>
</div>
<style>
@keyframes slide {{
0% {{ transform: translateX(-100%); }}
100% {{ transform: translateX(100%); }}
}}
</style>
"""
return "", "", gr.update(visible=True, value=progress_html)
def generate_and_hide_progress(industry, keywords, theory, language):
"""์์ฑ ํ ํ๋ก๊ทธ๋ ์ค๋ฐ ์จ๊น"""
result_md, result_html, _ = generate_by_theory(industry, keywords, theory, language)
return result_md, result_html, gr.update(visible=False)
# ํ๋ก๊ทธ๋ ์ค๋ฐ ํ์
btn.click(
lambda i, k, l, t=theory_key, n=tab_name: show_progress(i, k, t, l, n),
inputs=[industry_input, keywords_input, current_language],
outputs=[output, visual, progress_msg]
).then(
# ์ค์ ์์ฑ ์์
lambda i, k, l, t=theory_key: generate_and_hide_progress(i, k, t, l),
inputs=[industry_input, keywords_input, current_language],
outputs=[output, visual, progress_msg]
)
gr.Markdown("""
<div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 40px; border-radius: 15px; margin-top: 40px;">
<h3 style="margin-top: 0; text-align: center;">๐ Why THEORIAโข?</h3>
<div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 25px; margin-top: 25px;">
<div style="background: rgba(255,255,255,0.1); padding: 20px; border-radius: 10px;">
<h4 style="margin: 0 0 10px 0;">๐งช Scientific Foundation</h4>
<p style="margin: 0; font-size: 0.95em;">Based on proven cognitive and creative theories from psychology, linguistics, and design</p>
</div>
<div style="background: rgba(255,255,255,0.1); padding: 20px; border-radius: 10px;">
<h4 style="margin: 0 0 10px 0;">๐ฏ Multi-dimensional Approach</h4>
<p style="margin: 0; font-size: 0.95em;">15 different perspectives ensure you find the perfect name for your brand</p>
</div>
<div style="background: rgba(255,255,255,0.1); padding: 20px; border-radius: 10px;">
<h4 style="margin: 0 0 10px 0;">๐ Unified Evaluation</h4>
<p style="margin: 0; font-size: 0.95em;">Consistent scoring system allows easy comparison across all theories</p>
</div>
<div style="background: rgba(255,255,255,0.1); padding: 20px; border-radius: 10px;">
<h4 style="margin: 0 0 10px 0;">๐ Global Ready</h4>
<p style="margin: 0; font-size: 0.95em;">Multilingual support and cultural considerations built into every theory</p>
</div>
</div>
<div style="text-align: center; margin-top: 30px; padding-top: 20px; border-top: 1px solid rgba(255,255,255,0.2);">
<p style="margin: 0; font-size: 0.9em; opacity: 0.8;">
THEORIAโข - Where Science Meets Creativity in Brand Naming
</p>
</div>
</div>
""")
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False
) |