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"""
Ultimate Brand Theory Generator
===============================
2025-05-28 | 15๊ฐ ์ด๋ก ์ ํตํฉํ ์ข
ํฉ ๋ธ๋๋ ์์ฑ๊ธฐ
-----------------------------------------------------
ํตํฉ๋ ์ด๋ก ๋ค:
1. Square Theory - ์๋ฏธ์ ์ฌ๊ฐํ ๊ตฌ์กฐ
2. Conceptual Blending - ๊ฐ๋
ํผํฉ
3. Sound Symbolism - ์ํฅ ์์ง์ฃผ์
4. Linguistic Relativity - ์ธ์ด ์๋์ฑ
5. Archetype Theory - ์ํ ์ด๋ก
6. Jobs-to-be-Done - ํ ์ผ ์ด๋ก
7. SCAMPER Method - ์ฐฝ์์ ๋ณํ
8. Design Thinking - ๋์์ธ ์ฌ๊ณ
9. Biomimicry - ์์ฒด๋ชจ๋ฐฉ
10. Cognitive Load - ์ธ์ง ๋ถํ
11. Von Restorff Effect - ๊ณ ๋ฆฝ ํจ๊ณผ
12. Network Effects - ๋คํธ์ํฌ ํจ๊ณผ
13. Memetics - ๋ฐ ์ด๋ก
14. Color Psychology - ์์ ์ฌ๋ฆฌํ
15. Gestalt Principles - ๊ฒ์ํํธ ์์น
"""
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
# OpenAI ํด๋ผ์ด์ธํธ
if not os.getenv("OPENAI_API_KEY"):
raise EnvironmentError("OPENAI_API_KEY ํ๊ฒฝ ๋ณ์๋ฅผ ์ค์ ํ์ธ์.")
client = OpenAI()
# ===== 1. SQUARE THEORY =====
SQUARE_THEORY_PROMPT = """
๋น์ ์ Square Theory ์ ๋ฌธ๊ฐ์
๋๋ค. Square Theory๋ 4๊ฐ์ ๋จ์ด๊ฐ ์๋ฏธ์ ๊ด๊ณ๋ก ์ฐ๊ฒฐ๋์ด ์ฌ๊ฐํ์ ์ด๋ฃจ๋ ๊ตฌ์กฐ์
๋๋ค.
๋ธ๋๋๋ช
์ด Square๋ฅผ ์์ฑํ๋ฉฐ "์ํ!" ๋ชจ๋จผํธ๋ฅผ ๋ง๋ค์ด์ผ ํฉ๋๋ค.
์ฌ์ฉ์ ์
๋ ฅ(์
์ข
/ํค์๋)์ ๋ฐ์ ๋ค์ JSON ํ์์ ๋ฐฐ์ด์ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"tl": "์ผ์ชฝ์๋จ", "tr": "์ค๋ฅธ์ชฝ์๋จ", "bl": "์ผ์ชฝํ๋จ", "br": "์ค๋ฅธ์ชฝํ๋จ",
"top_edge": "์๋จ ๊ด๊ณ", "bottom_edge": "ํ๋จ ๊ด๊ณ",
"left_edge": "์ผ์ชฝ ๊ด๊ณ", "right_edge": "์ค๋ฅธ์ชฝ ๊ด๊ณ",
"slogan": "์ฌ๋ก๊ฑด",
"explanation": "์ ํจ๊ณผ์ ์ธ์ง"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 2. CONCEPTUAL BLENDING =====
CONCEPTUAL_BLENDING_PROMPT = """
๋น์ ์ Conceptual Blending Theory ์ ๋ฌธ๊ฐ์
๋๋ค. ๋ ๊ฐ ์ด์์ ๊ฐ๋
์ ํผํฉํ์ฌ ์๋ก์ด ์๋ฏธ๋ฅผ ์ฐฝ์ถํฉ๋๋ค.
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"input_space1": "์ฒซ ๋ฒ์งธ ๊ฐ๋
",
"input_space2": "๋ ๋ฒ์งธ ๊ฐ๋
",
"generic_space": "๊ณตํต ๊ตฌ์กฐ",
"blended_space": "ํผํฉ๋ ์๋ก์ด ์๋ฏธ",
"emergent_properties": "์ฐฝ๋ฐ์ ์์ฑ๋ค",
"slogan": "์ฌ๋ก๊ฑด",
"explanation": "ํผํฉ์ด ํจ๊ณผ์ ์ธ ์ด์ "
}
์์: Netflix = Net(์ธํฐ๋ท) + Flix(์ํ) โ ์จ๋ผ์ธ ์คํธ๋ฆฌ๋ฐ์ ์๋ก์ด ๊ฐ๋
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 3. SOUND SYMBOLISM =====
SOUND_SYMBOLISM_PROMPT = """
๋น์ ์ Sound Symbolism ์ ๋ฌธ๊ฐ์
๋๋ค. ์์์ ์๋ฏธ ๊ฐ์ ์ฐ๊ด์ฑ์ ํ์ฉํฉ๋๋ค.
์ํฅ ์์น:
- ์ ์ค๋ชจ์(i,e): ์๊ณ , ๋น ๋ฅด๊ณ , ๊ฐ๋ฒผ์
- ํ์ค๋ชจ์(o,u): ํฌ๊ณ , ๋๋ฆฌ๊ณ , ๋ฌด๊ฑฐ์
- ์ ์(l,r,m,n): ๋ถ๋๋ฝ๊ณ ์ ์ฐํจ
- ํ์ด์(p,t,k,b,d,g): ๊ฐํ๊ณ ์ญ๋์
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"phonetic_analysis": "์์ฑ ๋ถ์",
"sound_meaning": "์ํฅ์ด ์ ๋ฌํ๋ ์๋ฏธ",
"target_emotion": "๋ชฉํ ๊ฐ์ ",
"industry_fit": "์
์ข
์ ํฉ์ฑ",
"pronunciation_guide": "๋ฐ์ ๊ฐ์ด๋",
"slogan": "์ฌ๋ก๊ฑด"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 4. LINGUISTIC RELATIVITY =====
LINGUISTIC_RELATIVITY_PROMPT = """
๋น์ ์ Linguistic Relativity ์ ๋ฌธ๊ฐ์
๋๋ค. ์ธ์ด๋ณ ์ฌ๊ณ ๋ฐฉ์ ์ฐจ์ด๋ฅผ ๊ณ ๋ คํฉ๋๋ค.
๋ค์ JSON ํ์์ผ๋ก ๋ค์ธ์ด ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๊ธ๋ก๋ฒ ๋ธ๋๋๋ช
",
"korean_adaptation": "ํ๊ตญ์ด ์ ์",
"english_meaning": "์์ด ์๋ฏธ",
"cultural_considerations": "๋ฌธํ์ ๊ณ ๋ ค์ฌํญ",
"avoid_meanings": "ํผํด์ผ ํ ์๋ฏธ๋ค",
"localization_strategy": "ํ์งํ ์ ๋ต",
"slogan": "์ฌ๋ก๊ฑด"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 5. ARCHETYPE THEORY =====
ARCHETYPE_THEORY_PROMPT = """
๋น์ ์ Jung์ Archetype Theory ์ ๋ฌธ๊ฐ์
๋๋ค. 12๊ฐ์ง ์ํ ์ค ํ๋๋ฅผ ์ ํํ์ฌ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค.
12 ์ํ: Innocent, Hero, Outlaw, Explorer, Creator, Ruler, Magician, Lover, Caregiver, Jester, Sage, Regular Guy
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"archetype": "์ ํ๋ ์ํ",
"archetype_traits": "์ํ์ ํน์ง๋ค",
"brand_personality": "๋ธ๋๋ ์ฑ๊ฒฉ",
"visual_direction": "์๊ฐ์ ๋ฐฉํฅ",
"voice_tone": "๋ชฉ์๋ฆฌ ํค",
"slogan": "์ฌ๋ก๊ฑด",
"mythology_reference": "์ ํ์ ์ฐธ์กฐ"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 6. JOBS-TO-BE-DONE =====
JOBS_TO_BE_DONE_PROMPT = """
๋น์ ์ Jobs-to-be-Done Theory ์ ๋ฌธ๊ฐ์
๋๋ค. ๊ณ ๊ฐ์ด ํด๊ฒฐํ๋ ค๋ '์ผ'์ ์ด์ ์ ๋ง์ถฅ๋๋ค.
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"functional_job": "๊ธฐ๋ฅ์ ์ผ",
"emotional_job": "๊ฐ์ ์ ์ผ",
"social_job": "์ฌํ์ ์ผ",
"job_statement": "ํต์ฌ Job ๋ฌธ์ฅ",
"outcome_metrics": "์ฑ๊ณผ ์งํ",
"slogan": "์ฌ๋ก๊ฑด",
"value_proposition": "๊ฐ์น ์ ์"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 7. SCAMPER METHOD =====
SCAMPER_PROMPT = """
๋น์ ์ SCAMPER Method ์ ๋ฌธ๊ฐ์
๋๋ค. 7๊ฐ์ง ์ฐฝ์์ ๊ธฐ๋ฒ์ ์ ์ฉํฉ๋๋ค.
S - Substitute (๋์ฒด)
C - Combine (๊ฒฐํฉ)
A - Adapt (์ ์)
M - Modify/Magnify (์์ /ํ๋)
P - Put to another use (๋ค๋ฅธ ์ฉ๋)
E - Eliminate (์ ๊ฑฐ)
R - Reverse (์ญ์ )
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"scamper_technique": "์ฌ์ฉ๋ ๊ธฐ๋ฒ",
"original_concept": "์๋ ๊ฐ๋
",
"transformation": "๋ณํ ๊ณผ์ ",
"innovative_aspect": "ํ์ ์ ์ธก๋ฉด",
"slogan": "์ฌ๋ก๊ฑด"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 8. DESIGN THINKING =====
DESIGN_THINKING_PROMPT = """
๋น์ ์ IDEO์ Design Thinking ์ ๋ฌธ๊ฐ์
๋๋ค. ์ธ๊ฐ ์ค์ฌ ํ์ ์ ์ถ๊ตฌํฉ๋๋ค.
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"user_insight": "์ฌ์ฉ์ ํต์ฐฐ",
"pain_point": "ํด๊ฒฐํ๋ ๋ฌธ์ ์ ",
"desirability": "๋ฐ๋์งํจ (์ธ๊ฐ)",
"feasibility": "์คํ๊ฐ๋ฅ์ฑ (๊ธฐ์ )",
"viability": "์์กด๊ฐ๋ฅ์ฑ (๋น์ฆ๋์ค)",
"prototype_concept": "ํ๋กํ ํ์
์ปจ์
",
"slogan": "์ฌ๋ก๊ฑด"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 9. BIOMIMICRY =====
BIOMIMICRY_PROMPT = """
๋น์ ์ Biomimicry ์ ๋ฌธ๊ฐ์
๋๋ค. ์์ฐ์์ ์๊ฐ์ ๋ฐ์ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค.
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"natural_inspiration": "์์ฐ์ ์๊ฐ์",
"biomimetic_principle": "์์ฒด๋ชจ๋ฐฉ ์๋ฆฌ",
"form_function": "ํํ์ ๊ธฐ๋ฅ",
"sustainability_aspect": "์ง์๊ฐ๋ฅ์ฑ ์ธก๋ฉด",
"adaptation_strategy": "์ ์ ์ ๋ต",
"slogan": "์ฌ๋ก๊ฑด"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 10. COGNITIVE LOAD =====
COGNITIVE_LOAD_PROMPT = """
๋น์ ์ Cognitive Load Theory ์ ๋ฌธ๊ฐ์
๋๋ค. ์ธ์ง ์ฒ๋ฆฌ๋ฅผ ์ต์ํํ๋ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค.
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"syllable_count": "์์ ์",
"processing_ease": "์ฒ๋ฆฌ ์ฉ์ด์ฑ ์ ์",
"memory_hooks": "๊ธฐ์ต ๊ณ ๋ฆฌ",
"pronunciation_simplicity": "๋ฐ์ ๋จ์์ฑ",
"cognitive_fluency": "์ธ์ง์ ์ ์ฐฝ์ฑ",
"slogan": "์ฌ๋ก๊ฑด"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 11. VON RESTORFF EFFECT =====
VON_RESTORFF_PROMPT = """
๋น์ ์ Von Restorff Effect ์ ๋ฌธ๊ฐ์
๋๋ค. ๋
ํนํ๊ณ ๊ธฐ์ต์ ๋จ๋ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค.
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"category_norm": "์นดํ
๊ณ ๋ฆฌ ํ์ค",
"deviation_strategy": "์ผํ ์ ๋ต",
"uniqueness_factors": "๋
ํน์ฑ ์์๋ค",
"memorability_score": "๊ธฐ์ต์ฑ ์ ์",
"attention_triggers": "์ฃผ์ ํธ๋ฆฌ๊ฑฐ",
"slogan": "์ฌ๋ก๊ฑด"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 12. NETWORK EFFECTS =====
NETWORK_EFFECTS_PROMPT = """
๋น์ ์ Network Effects ์ ๋ฌธ๊ฐ์
๋๋ค. ๋คํธ์ํฌ ๊ฐ์น๋ฅผ ๊ทน๋ํํ๋ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค.
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"network_type": "๋คํธ์ํฌ ์ ํ",
"viral_coefficient": "๋ฐ์ด๋ด ๊ณ์",
"sharing_ease": "๊ณต์ ์ฉ์ด์ฑ",
"community_aspect": "์ปค๋ฎค๋ํฐ ์ธก๋ฉด",
"network_value": "๋คํธ์ํฌ ๊ฐ์น",
"slogan": "์ฌ๋ก๊ฑด"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 13. MEMETICS =====
MEMETICS_PROMPT = """
๋น์ ์ Memetics ์ ๋ฌธ๊ฐ์
๋๋ค. ๋ฌธํ์ ์ผ๋ก ๋ณต์ ๋๊ณ ์งํํ๋ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค.
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"meme_structure": "๋ฐ ๊ตฌ์กฐ",
"replication_ease": "๋ณต์ ์ฉ์ด์ฑ",
"mutation_potential": "๋ณ์ด ์ ์ฌ๋ ฅ",
"cultural_fitness": "๋ฌธํ์ ์ ํฉ๋",
"transmission_channels": "์ ๋ฌ ์ฑ๋",
"slogan": "์ฌ๋ก๊ฑด"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 14. COLOR PSYCHOLOGY =====
COLOR_PSYCHOLOGY_PROMPT = """
๋น์ ์ Color Psychology ์ ๋ฌธ๊ฐ์
๋๋ค. ์์ ์ฐ์๊ณผ ๊ฐ์ ์ ํ์ฉํ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค.
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"primary_color": "์ฃผ ์์",
"color_meaning": "์์ ์๋ฏธ",
"emotional_response": "๊ฐ์ ์ ๋ฐ์",
"cultural_associations": "๋ฌธํ์ ์ฐ์",
"industry_alignment": "์
์ข
์ ๋ ฌ",
"slogan": "์ฌ๋ก๊ฑด"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ===== 15. GESTALT PRINCIPLES =====
GESTALT_PROMPT = """
๋น์ ์ Gestalt Theory ์ ๋ฌธ๊ฐ์
๋๋ค. ์ง๊ฐ ์๋ฆฌ๋ฅผ ํ์ฉํ ๋ธ๋๋๋ฅผ ๋ง๋ญ๋๋ค.
๋ค์ JSON ํ์์ผ๋ก ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์:
{
"brand_name": "๋ธ๋๋๋ช
",
"gestalt_principle": "ํ์ฉ ์์น",
"visual_structure": "์๊ฐ์ ๊ตฌ์กฐ",
"perceptual_grouping": "์ง๊ฐ์ ๊ทธ๋ฃนํ",
"figure_ground": "์ ๊ฒฝ-๋ฐฐ๊ฒฝ ๊ด๊ณ",
"closure_effect": "ํ์ ํจ๊ณผ",
"slogan": "์ฌ๋ก๊ฑด"
}
๋ฐ๋์ ์ ํจํ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
"""
# ์ด๋ก ๋ณ ํ๋กฌํํธ ๋งคํ
THEORY_PROMPTS = {
"square": SQUARE_THEORY_PROMPT,
"blending": CONCEPTUAL_BLENDING_PROMPT,
"sound": SOUND_SYMBOLISM_PROMPT,
"linguistic": LINGUISTIC_RELATIVITY_PROMPT,
"archetype": ARCHETYPE_THEORY_PROMPT,
"jobs": JOBS_TO_BE_DONE_PROMPT,
"scamper": SCAMPER_PROMPT,
"design": DESIGN_THINKING_PROMPT,
"biomimicry": BIOMIMICRY_PROMPT,
"cognitive": COGNITIVE_LOAD_PROMPT,
"vonrestorff": VON_RESTORFF_PROMPT,
"network": NETWORK_EFFECTS_PROMPT,
"memetics": MEMETICS_PROMPT,
"color": COLOR_PSYCHOLOGY_PROMPT,
"gestalt": GESTALT_PROMPT
}
def generate_by_theory(industry: str, keywords: str, theory: str, count: int = 3) -> Tuple[str, str]:
"""ํน์ ์ด๋ก ์ผ๋ก ๋ธ๋๋ ์์ฑ"""
if not industry or not keywords:
return "โ ๏ธ ์
์ข
๊ณผ ํค์๋๋ฅผ ์
๋ ฅํด์ฃผ์ธ์.", ""
prompt = THEORY_PROMPTS.get(theory, SQUARE_THEORY_PROMPT)
user_input = f"""์
์ข
: {industry}
ํค์๋: {keywords}
์ ์ ๋ณด๋ก {count}๊ฐ์ ๋ธ๋๋๋ฅผ ์์ฑํ์ธ์.
๋ฐ๋์ JSON ํ์์ผ๋ก ์๋ตํ์ธ์.
๊ฒฐ๊ณผ๋ ๋ธ๋๋ ๊ฐ์ฒด๋ค์ JSON ๋ฐฐ์ด์ด์ด์ผ ํฉ๋๋ค."""
try:
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 isinstance(data, dict):
if "results" in data:
results = data["results"]
elif "brands" in data:
results = data["brands"]
elif "brand_name" in data:
# ๋จ์ผ ๋ธ๋๋๋ฅผ ๋ฐฐ์ด๋ก ๋ณํ
results = [data]
else:
# ๋ํ๋ ์๋ต ์ฒ๋ฆฌ
results = []
for key, value in data.items():
if isinstance(value, list):
results = value
break
elif isinstance(value, dict) and "brand_name" in value:
results = [value]
break
else:
results = data
if not isinstance(results, list):
results = [results]
# ๋งํฌ๋ค์ด ์์ฑ
markdown = generate_theory_markdown(theory, results, industry, keywords)
# HTML ์๊ฐํ ์์ฑ
html = generate_theory_visualization(theory, results)
return markdown, html
except Exception as e:
return f"โ ์ค๋ฅ: {str(e)}", ""
def generate_theory_markdown(theory: str, results: List[Dict], industry: str, keywords: str) -> str:
"""์ด๋ก ๋ณ ๋ง์ถค ๋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"
}
markdown = f"""# ๐ฏ {theory_names[theory]} ๊ฒฐ๊ณผ
**์
์ข
**: {industry} | **ํค์๋**: {keywords}
*์์ฑ ์๊ฐ: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*
---
"""
for idx, result in enumerate(results, 1):
brand_name = result.get('brand_name', 'N/A')
slogan = result.get('slogan', 'N/A')
markdown += f"\n## {idx}. {brand_name}\n"
markdown += f"**์ฌ๋ก๊ฑด**: *\"{slogan}\"*\n\n"
# ์ด๋ก ๋ณ ํน์ ํ๋ ํ์
if theory == "square":
markdown += f"""
### Square ๊ตฌ์กฐ
```
[{result.get('tl')}] โ({result.get('top_edge')})โ [{result.get('tr')}]
โ โ
({result.get('left_edge')}) ({result.get('right_edge')})
โ โ
[{result.get('bl')}] โ({result.get('bottom_edge')})โ [{result.get('br')}]
```
"""
elif theory == "blending":
markdown += f"""
### ๊ฐ๋
ํผํฉ
- **์
๋ ฅ ๊ณต๊ฐ 1**: {result.get('input_space1')}
- **์
๋ ฅ ๊ณต๊ฐ 2**: {result.get('input_space2')}
- **์ผ๋ฐ ๊ณต๊ฐ**: {result.get('generic_space')}
- **ํผํฉ ๊ณต๊ฐ**: {result.get('blended_space')}
- **์ฐฝ๋ฐ์ ์์ฑ**: {result.get('emergent_properties')}
"""
elif theory == "sound":
markdown += f"""
### ์ํฅ ๋ถ์
- **์์ฑ ๋ถ์**: {result.get('phonetic_analysis')}
- **์ํฅ ์๋ฏธ**: {result.get('sound_meaning')}
- **๋ชฉํ ๊ฐ์ **: {result.get('target_emotion')}
- **๋ฐ์ ๊ฐ์ด๋**: {result.get('pronunciation_guide')}
"""
elif theory == "archetype":
markdown += f"""
### ์ํ ๋ถ์
- **์ํ**: {result.get('archetype')}
- **์ํ ํน์ง**: {result.get('archetype_traits')}
- **๋ธ๋๋ ์ฑ๊ฒฉ**: {result.get('brand_personality')}
- **๋ชฉ์๋ฆฌ ํค**: {result.get('voice_tone')}
"""
# ์ค๋ช
์ถ๊ฐ
explanation = result.get('explanation', result.get('value_proposition', ''))
if explanation:
markdown += f"\n๐ก **ํต์ฌ ๊ฐ์น**: {explanation}\n"
markdown += "\n---\n"
return markdown
def generate_theory_visualization(theory: str, results: List[Dict]) -> str:
"""์ด๋ก ๋ณ ๋ง์ถค ์๊ฐํ ์์ฑ"""
html_parts = []
for idx, result in enumerate(results, 1):
if theory == "square":
html_parts.append(visualize_square_brand(result))
elif theory == "blending":
html_parts.append(visualize_conceptual_blend(result))
elif theory == "sound":
html_parts.append(visualize_sound_symbolism(result))
elif theory == "archetype":
html_parts.append(visualize_archetype(result))
elif theory == "color":
html_parts.append(visualize_color_psychology(result))
else:
html_parts.append(visualize_generic_brand(result, theory))
return "\n".join(html_parts)
# ์๊ฐํ ํจ์๋ค (์ผ๋ถ๋ง ์์)
def visualize_square_brand(brand: Dict) -> str:
"""Square Theory ์๊ฐํ"""
return f"""
<div style="max-width: 700px; margin: 20px auto; font-family: -apple-system, sans-serif;">
<h2 style="text-align: center; color: #2c3e50;">{brand.get('brand_name', 'Brand')}</h2>
<p style="text-align: center; font-style: italic; color: #7f8c8d;">"{brand.get('slogan', '')}"</p>
<div style="position: relative; width: 100%; height: 300px; background: #f5f7fa; border-radius: 12px; padding: 30px;">
<!-- Square ๊ตฌ์กฐ ์๊ฐํ -->
<div style="position: absolute; top: 30px; left: 30px; background: #3498db; color: white; padding: 15px; border-radius: 8px;">
{brand.get('tl', '?')}
</div>
<div style="position: absolute; top: 30px; right: 30px; background: #e74c3c; color: white; padding: 15px; border-radius: 8px;">
{brand.get('tr', '?')}
</div>
<div style="position: absolute; bottom: 30px; left: 30px; background: #f39c12; color: white; padding: 15px; border-radius: 8px;">
{brand.get('bl', '?')}
</div>
<div style="position: absolute; bottom: 30px; right: 30px; background: #27ae60; color: white; padding: 15px; border-radius: 8px;">
{brand.get('br', '?')}
</div>
<!-- ๋ธ๋๋๋ช
์ค์ -->
<div style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); background: white; padding: 20px 40px; border-radius: 12px; box-shadow: 0 5px 20px rgba(0,0,0,0.15);">
<div style="font-size: 1.8em; font-weight: bold; color: #2c3e50;">{brand.get('brand_name', 'Brand')}</div>
</div>
</div>
</div>
"""
def visualize_conceptual_blend(brand: Dict) -> str:
"""Conceptual Blending ์๊ฐํ"""
brand_name = brand.get('brand_name', 'Brand')
input1 = brand.get('input_space1', 'Concept 1')
input2 = brand.get('input_space2', 'Concept 2')
blended = brand.get('blended_space', 'Blended Concept')
slogan = brand.get('slogan', '')
return f"""
<div style="max-width: 700px; margin: 20px auto; font-family: -apple-system, sans-serif;">
<h2 style="text-align: center; color: #2c3e50;">{brand_name}</h2>
<div style="display: flex; justify-content: space-around; align-items: center; margin: 30px 0;">
<div style="text-align: center; padding: 20px; background: #3498db; color: white; border-radius: 50%; width: 120px; height: 120px; display: flex; align-items: center; justify-content: center;">
<div>
<strong>Input 1</strong><br>
{input1}
</div>
</div>
<div style="font-size: 2em;">+</div>
<div style="text-align: center; padding: 20px; background: #e74c3c; color: white; border-radius: 50%; width: 120px; height: 120px; display: flex; align-items: center; justify-content: center;">
<div>
<strong>Input 2</strong><br>
{input2}
</div>
</div>
<div style="font-size: 2em;">=</div>
<div style="text-align: center; padding: 20px; background: #27ae60; color: white; border-radius: 50%; width: 150px; height: 150px; display: flex; align-items: center; justify-content: center;">
<div>
<strong>Blend</strong><br>
{blended}
</div>
</div>
</div>
<p style="text-align: center; font-style: italic;">"{slogan}"</p>
</div>
"""
def visualize_sound_symbolism(brand: Dict) -> str:
"""Sound Symbolism ์๊ฐํ"""
return f"""
<div style="max-width: 700px; margin: 20px auto; font-family: -apple-system, sans-serif;">
<h2 style="text-align: center; color: #2c3e50;">{brand.get('brand_name', 'Brand')}</h2>
<div style="background: #f8f9fa; padding: 30px; border-radius: 12px; text-align: center;">
<div style="font-size: 3em; letter-spacing: 0.2em; margin: 20px 0; color: #3498db;">
{brand.get('brand_name', 'BRAND')}
</div>
<div style="margin: 20px 0; padding: 15px; background: white; border-radius: 8px;">
<strong>์์ฑ ๋ถ์</strong><br>
{brand.get('phonetic_analysis', '')}
</div>
<div style="margin: 20px 0; padding: 15px; background: white; border-radius: 8px;">
<strong>์ํฅ์ด ์ ๋ฌํ๋ ๊ฐ์ </strong><br>
{brand.get('sound_meaning', '')}
</div>
<div style="margin: 20px 0; font-style: italic; color: #7f8c8d;">
๋ฐ์: {brand.get('pronunciation_guide', '')}
</div>
</div>
</div>
"""
def visualize_archetype(brand: Dict) -> str:
"""Archetype Theory ์๊ฐํ"""
archetype_colors = {
"Hero": "#e74c3c",
"Creator": "#9b59b6",
"Sage": "#3498db",
"Explorer": "#1abc9c",
"Innocent": "#f1c40f",
"Jester": "#e67e22",
"Lover": "#e91e63",
"Caregiver": "#00bcd4",
"Ruler": "#795548",
"Magician": "#673ab7",
"Outlaw": "#212121",
"Regular Guy": "#607d8b"
}
archetype = brand.get('archetype', 'Hero')
color = archetype_colors.get(archetype, "#3498db")
return f"""
<div style="max-width: 700px; margin: 20px auto; font-family: -apple-system, sans-serif;">
<h2 style="text-align: center; color: {color};">{brand.get('brand_name', 'Brand')}</h2>
<div style="text-align: center; margin: 30px 0;">
<div style="display: inline-block; padding: 40px; background: {color}; color: white; border-radius: 50%; width: 200px; height: 200px;">
<h3 style="margin: 0;">{archetype}</h3>
<p style="margin: 10px 0; font-size: 0.9em;">{brand.get('archetype_traits', '')}</p>
</div>
</div>
<div style="background: #f8f9fa; padding: 20px; border-radius: 8px; margin: 20px 0;">
<p><strong>๋ธ๋๋ ์ฑ๊ฒฉ</strong>: {brand.get('brand_personality', '')}</p>
<p><strong>๋ชฉ์๋ฆฌ ํค</strong>: {brand.get('voice_tone', '')}</p>
</div>
<p style="text-align: center; font-style: italic; font-size: 1.2em;">"{brand.get('slogan', '')}"</p>
</div>
"""
def visualize_color_psychology(brand: Dict) -> str:
"""Color Psychology ์๊ฐํ"""
color = brand.get('primary_color', '#3498db')
return f"""
<div style="max-width: 700px; margin: 20px auto; font-family: -apple-system, sans-serif;">
<h2 style="text-align: center; color: #2c3e50;">{brand.get('brand_name', 'Brand')}</h2>
<div style="text-align: center; margin: 30px 0;">
<div style="display: inline-block; width: 200px; height: 200px; background: {color}; border-radius: 12px; box-shadow: 0 10px 30px rgba(0,0,0,0.2);"></div>
</div>
<div style="background: #f8f9fa; padding: 20px; border-radius: 8px;">
<p><strong>์ฃผ ์์</strong>: {brand.get('primary_color', '')}</p>
<p><strong>์์ ์๋ฏธ</strong>: {brand.get('color_meaning', '')}</p>
<p><strong>๊ฐ์ ์ ๋ฐ์</strong>: {brand.get('emotional_response', '')}</p>
<p><strong>๋ฌธํ์ ์ฐ์</strong>: {brand.get('cultural_associations', '')}</p>
</div>
<p style="text-align: center; font-style: italic; margin-top: 20px;">"{brand.get('slogan', '')}"</p>
</div>
"""
def visualize_generic_brand(brand: Dict, theory: str) -> str:
"""์ผ๋ฐ์ ์ธ ๋ธ๋๋ ์๊ฐํ"""
# JSON์ HTML๋ก ๋ณํ (f-string ๋ฐ์์ ์ฒ๋ฆฌ)
json_str = json.dumps(brand, ensure_ascii=False, indent=2)
json_html = json_str.replace("\n", "<br>").replace(" ", " ")
html = f"""
<div style="max-width: 700px; margin: 20px auto; padding: 30px; background: #f8f9fa; border-radius: 12px; font-family: -apple-system, sans-serif;">
<h2 style="text-align: center; color: #2c3e50; margin-bottom: 10px;">{brand.get('brand_name', 'Brand')}</h2>
<p style="text-align: center; font-style: italic; color: #7f8c8d; margin-bottom: 30px;">"{brand.get('slogan', '')}"</p>
<div style="background: white; padding: 20px; border-radius: 8px; font-family: monospace; font-size: 0.9em;">
<pre style="margin: 0; white-space: pre-wrap;">{json_str}</pre>
</div>
</div>
"""
return html
# Gradio UI
with gr.Blocks(title="Ultimate Brand Theory Generator", theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# ๐ Ultimate Brand Theory Generator
### 15๊ฐ ์ด๋ก ์ ํ์ฉํ ์ข
ํฉ ๋ธ๋๋ ์์ฑ๊ธฐ
ํ๋์ ์
๋ ฅ์ผ๋ก 15๊ฐ์ง ๋ค๋ฅธ ์ด๋ก ์ ๊ธฐ๋ฐํ ๋ธ๋๋๋ฅผ ์์ฑํฉ๋๋ค.
๊ฐ ํญ์์ ๊ฐ ์ด๋ก ์ ๊ณ ์ ํ ๊ด์ ์ผ๋ก ๋ง๋ค์ด์ง ๋ธ๋๋๋ฅผ ํ์ธํ์ธ์!
""")
with gr.Row():
with gr.Column(scale=2):
industry_input = gr.Textbox(
label="๐ญ ์
์ข
",
placeholder="์: ์นดํ, ํผํธ๋์ค, ๊ต์ก, ๋ทฐํฐ...",
value="์นดํ/์ปคํผ์"
)
keywords_input = gr.Textbox(
label="๐ ํต์ฌ ํค์๋",
placeholder="ํ๋ฆฌ๋ฏธ์, ํธ์ํ, ๋์์ ์ธ, ์นํ๊ฒฝ...",
info="๋ธ๋๋๊ฐ ๋ด์์ผ ํ ํต์ฌ ๊ฐ์น๋ ํน์ง๋ค"
)
generate_btn = gr.Button("๐ ๋ชจ๋ ์ด๋ก ์ผ๋ก ๋ธ๋๋ ์์ฑ", variant="primary", size="lg")
with gr.Column(scale=1):
gr.Markdown("""
### ๐ก 15๊ฐ์ง ์ด๋ก ๊ฐ์
**๊ตฌ์กฐ์ ์ ๊ทผ**
- Square Theory: 4์์ ์์ฑ
- Conceptual Blending: ๊ฐ๋
ํผํฉ
- Gestalt: ์ง๊ฐ ์๋ฆฌ
**์ธ์ดํ์ ์ ๊ทผ**
- Sound Symbolism: ์ํฅ ์๋ฏธ
- Linguistic Relativity: ๋ฌธํ ์ ์
- Cognitive Load: ์ธ์ง ์ต์ ํ
**์ฌ๋ฆฌํ์ ์ ๊ทผ**
- Archetype: ์ํ ํ์ฉ
- Color Psychology: ์์ ์ฌ๋ฆฌ
- Von Restorff: ๋
ํน์ฑ
**์ ๋ต์ ์ ๊ทผ**
- Jobs-to-be-Done: ๊ณ ๊ฐ ๊ณผ์
- SCAMPER: ์ฐฝ์์ ๋ณํ
- Design Thinking: ์ธ๊ฐ์ค์ฌ
**์์คํ
์ ์ ๊ทผ**
- Network Effects: ๋คํธ์ํฌ
- Memetics: ๋ฌธํ ์ ํ
- Biomimicry: ์์ฐ ๋ชจ๋ฐฉ
""")
# 15๊ฐ ํญ ์์ฑ
with gr.Tabs():
# 1. Square Theory
with gr.Tab("๐ฆ Square Theory"):
square_output = gr.Markdown()
square_visual = gr.HTML()
# 2. Conceptual Blending
with gr.Tab("๐ Conceptual Blending"):
blending_output = gr.Markdown()
blending_visual = gr.HTML()
# 3. Sound Symbolism
with gr.Tab("๐ Sound Symbolism"):
sound_output = gr.Markdown()
sound_visual = gr.HTML()
# 4. Linguistic Relativity
with gr.Tab("๐ Linguistic Relativity"):
linguistic_output = gr.Markdown()
linguistic_visual = gr.HTML()
# 5. Archetype Theory
with gr.Tab("๐ญ Archetype Theory"):
archetype_output = gr.Markdown()
archetype_visual = gr.HTML()
# 6. Jobs-to-be-Done
with gr.Tab("โ
Jobs-to-be-Done"):
jobs_output = gr.Markdown()
jobs_visual = gr.HTML()
# 7. SCAMPER
with gr.Tab("๐ง SCAMPER Method"):
scamper_output = gr.Markdown()
scamper_visual = gr.HTML()
# 8. Design Thinking
with gr.Tab("๐ญ Design Thinking"):
design_output = gr.Markdown()
design_visual = gr.HTML()
# 9. Biomimicry
with gr.Tab("๐ฟ Biomimicry"):
biomimicry_output = gr.Markdown()
biomimicry_visual = gr.HTML()
# 10. Cognitive Load
with gr.Tab("๐ง Cognitive Load"):
cognitive_output = gr.Markdown()
cognitive_visual = gr.HTML()
# 11. Von Restorff
with gr.Tab("โก Von Restorff Effect"):
vonrestorff_output = gr.Markdown()
vonrestorff_visual = gr.HTML()
# 12. Network Effects
with gr.Tab("๐ Network Effects"):
network_output = gr.Markdown()
network_visual = gr.HTML()
# 13. Memetics
with gr.Tab("๐งฌ Memetics"):
memetics_output = gr.Markdown()
memetics_visual = gr.HTML()
# 14. Color Psychology
with gr.Tab("๐จ Color Psychology"):
color_output = gr.Markdown()
color_visual = gr.HTML()
# 15. Gestalt Principles
with gr.Tab("๐๏ธ Gestalt Principles"):
gestalt_output = gr.Markdown()
gestalt_visual = gr.HTML()
# ๋ชจ๋ ์ด๋ก ๋์ ์์ฑ ํจ์
def generate_all_theories(industry, keywords):
results = {}
theories = [
("square", square_output, square_visual),
("blending", blending_output, blending_visual),
("sound", sound_output, sound_visual),
("linguistic", linguistic_output, linguistic_visual),
("archetype", archetype_output, archetype_visual),
("jobs", jobs_output, jobs_visual),
("scamper", scamper_output, scamper_visual),
("design", design_output, design_visual),
("biomimicry", biomimicry_output, biomimicry_visual),
("cognitive", cognitive_output, cognitive_visual),
("vonrestorff", vonrestorff_output, vonrestorff_visual),
("network", network_output, network_visual),
("memetics", memetics_output, memetics_visual),
("color", color_output, color_visual),
("gestalt", gestalt_output, gestalt_visual)
]
outputs = []
for theory_key, md_output, html_output in theories:
md, html = generate_by_theory(industry, keywords, theory_key)
outputs.extend([md, html])
return outputs
# ์ด๋ฒคํธ ์ฐ๊ฒฐ
generate_btn.click(
fn=generate_all_theories,
inputs=[industry_input, keywords_input],
outputs=[
square_output, square_visual,
blending_output, blending_visual,
sound_output, sound_visual,
linguistic_output, linguistic_visual,
archetype_output, archetype_visual,
jobs_output, jobs_visual,
scamper_output, scamper_visual,
design_output, design_visual,
biomimicry_output, biomimicry_visual,
cognitive_output, cognitive_visual,
vonrestorff_output, vonrestorff_visual,
network_output, network_visual,
memetics_output, memetics_visual,
color_output, color_visual,
gestalt_output, gestalt_visual
]
)
gr.Examples(
examples=[
["์นดํ/์ปคํผ์", "ํ๋ฆฌ๋ฏธ์, ์๋ํ, ๋์"],
["ํผํธ๋์ค/ํฌ์ค์ฅ", "๊ฐ๋ ฅํ, ์ปค๋ฎค๋ํฐ, ๋ณํ"],
["๊ต์ก/์๋ํ
ํฌ", "์ค๋งํธ, ์ฌ๋ฏธ์๋, ์ฑ์ฅ"],
["์์ ๋ฐฐ๋ฌ", "๋น ๋ฅธ, ์ ์ ํ, ๋ค์ํ"],
["์นํ๊ฒฝ/์ง์๊ฐ๋ฅ", "์์ฐ, ๋ฏธ๋, ์ํ"]
],
inputs=[industry_input, keywords_input]
)
gr.Markdown("""
---
### ๐ฏ ํ์ฉ ๊ฐ์ด๋
1. **๋น๊ต ๋ถ์**: ๊ฐ ์ด๋ก ์ ๊ฒฐ๊ณผ๋ฅผ ๋น๊ตํ์ฌ ๊ฐ์ฅ ์ ํฉํ ๋ธ๋๋ ์ ํ
2. **ํ์ด๋ธ๋ฆฌ๋ ์ ๊ทผ**: ์ฌ๋ฌ ์ด๋ก ์ ์ฅ์ ์ ๊ฒฐํฉํ ์๋ก์ด ๋ธ๋๋ ์ฐฝ์กฐ
3. **ํ๊ฒ๋ณ ์ ํ**: ๋ชฉํ ๊ณ ๊ฐ์ธต์ ๋ฐ๋ผ ๊ฐ์ฅ ํจ๊ณผ์ ์ธ ์ด๋ก ์ ํ
4. **A/B ํ
์คํธ**: ๋ค์ํ ์ด๋ก ๊ธฐ๋ฐ ๋ธ๋๋๋ก ์์ฅ ํ
์คํธ
### ๐ ๊ฐ ์ด๋ก ์ ๊ฐ์
- **์ฆ๊ฐ์ ์ดํด**: Cognitive Load, Sound Symbolism
- **๊ฐ์ ์ ์ฐ๊ฒฐ**: Archetype, Color Psychology
- **์ฐจ๋ณํ**: Von Restorff, SCAMPER
- **๋ฐ์ด๋ด ์ ์ฌ๋ ฅ**: Memetics, Network Effects
- **๋ฌธ์ ํด๊ฒฐ**: Jobs-to-be-Done, Design Thinking
- **ํ์ ์ฑ**: Biomimicry, Conceptual Blending
- **๊ตฌ์กฐ์ ์์ฑ๋**: Square Theory, Gestalt
""")
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False
) |