Adjective Comparison Workflow?

app.py

@@ -781,8 +781,13 @@ def LinPEWFformat_prompt(current_prompt, prev_messages):
 
 with gr.Blocks() as demo:
     with gr.Accordion("Config and Asset Assistance - Click to open", open=False):
+        gr.HTML("Jonas Tyroller - This problem changes your perspective on game dev - minimise the cost of exploration so you can explore more (17:00) | dont make the same game again but worse (:) <br>https://youtu.be/o5K0uqhxgsE")
         with gr.Accordion("Main problem to solve - Concept combination / integration"):
             gr.Textbox(Adjectivebasedcombinationideatexts, lines=10)
+            gr.Textbox(Adjectivebasedcombinationideatextsv2, lines=10)
+            with gr.Accordion("Mermaid Structures - click to open", open=False):
+                    for key, item in examplemermaidconceptblendingstrutures.items():
+                        gr.Code(item, label=key)
         with gr.Accordion("Guidance / Themes to consider before you attempt config (Currently Qwen 0.5B)", open=False):
             gr.HTML("Copy paste any old config to llm and ask to remix is the easiest <br>To bake 'Moral of the story' in you have to be very deliberate")
             gr.HTML("UI can be media and all items can have media")

relatively_constant_variables.py

@@ -1267,6 +1267,72 @@ mermaidstorystructures =  {
     """,
  }
 
+examplemermaidconceptblendingstrutures = {
+    "Star Wars Bakery Example":
+    """flowchart TD
+
+  A[Bakery]
+  B[Star Wars]
+  C[Adjectives]
+  D[Combinatorics]
+  E[Seed Ideas]
+  F[LLM Processing]
+
+  A -- has attributes --> C
+  B -- has attributes --> C
+  C -- generates --> D
+  D -- creates --> E
+  E -- inputs to --> F
+
+  subgraph Bakery Attributes
+    direction TB
+    A1[Sweet]
+    A2[Warm]
+    A3[Comforting]
+    A4[Artisanal]
+    A -- includes --> A1
+    A -- includes --> A2
+    A -- includes --> A3
+    A -- includes --> A4
+  end
+
+  subgraph Star Wars Attributes
+    direction TB
+    B1[Galactic]
+    B2[Epic]
+    B3[Heroic]
+    B4[Dramatic]
+    B -- includes --> B1
+    B -- includes --> B2
+    B -- includes --> B3
+    B -- includes --> B4
+  end
+
+  subgraph Combinatorics
+    direction TB
+    C1[Sweet Galactic]
+    C2[Warm Epic]
+    C3[Comforting Heroic]
+    C4[Artisanal Dramatic]
+    D -- generates --> C1
+    D -- generates --> C2
+    D -- generates --> C3
+    D -- generates --> C4
+  end
+
+  subgraph Seed Ideas
+    direction TB
+    E1[Galactic Pastries]
+    E2[Epic Bread Battles]
+    E3[Heroic Baker's Quest]
+    E4[Dramatic Cake Decor]
+    E -- includes --> E1
+    E -- includes --> E2
+    E -- includes --> E3
+    E -- includes --> E4
+  end"""
+}
+
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 ExampleGameConfigs = {
@@ -1454,6 +1520,61 @@ What specific aspects of each concept are you most interested in comparing? Are
 By clarifying your goals and focusing your analysis, you can more effectively leverage these different approaches and find the most relevant resources to guide your exploration."""
 
 
+Adjectivebasedcombinationideatextsv2 = """Nemotron 340B Perplexity Labs
+
+Original Prompt
+Please help me. If I want to merge the idea of a bakery with the plot of star wars I need to find points where they match and dont match. I decided to use adjectives as a proxy so the combinatorics is initiated by close adjectives to generate seeds for combination ideas that we can send to an llm for each seed. How would this look in mermaid
+
+
+
+Mermaid Struxture
+
+
+The workflow or process you've described can be generally categorized as a "concept blending" or "idea fusion" approach, which involves combining elements from two or more distinct concepts to generate new ideas. In your specific case, you're using adjectives as a proxy to facilitate the blending of a bakery concept with the plot of Star Wars. This process can also be considered a form of "creative problem-solving" or "design thinking," where you're systematically exploring and combining elements to generate innovative solutions or ideas.
+More specifically, the steps in your workflow can be broken down into the following stages:
+Concept Identification: Identifying the two distinct concepts (bakery and Star Wars) to be blended.
+Attribute Extraction: Extracting relevant attributes (adjectives) from each concept.
+Combinatorics: Systematically combining the attributes from both concepts to generate new combinations.
+Seed Idea Generation: Translating the combined attributes into seed ideas that merge the two concepts.
+LLM Processing: Utilizing a Large Language Model (LLM) to further develop and refine the seed ideas into more detailed and cohesive concepts.
+This workflow can be applied to various creative problem-solving scenarios, not just limited to the bakery and Star Wars example
+
+
+
+Indeed, design can be viewed as a search algorithm, where you explore a vast space of possibilities to find optimal or innovative solutions. Here's how you can apply this idea to five more scenarios:
+Merging a Library with a Music Festival
+Concept Identification: Library and Music Festival
+Attribute Extraction: Quiet, Knowledgeable, Organized (Library) vs. Loud, Energetic, Creative (Music Festival)
+Combinatorics: Quiet Creative Spaces, Knowledgeable Music Workshops, Organized Jam Sessions
+Seed Idea Generation: Silent Disco Library, Music Theory Workshops, Collaborative Songwriting Sessions
+LLM Processing: Develop detailed event plans, marketing materials, and logistical considerations
+Blending a Fitness Center with a Video Game Arcade
+Concept Identification: Fitness Center and Video Game Arcade
+Attribute Extraction: Active, Healthy, Competitive (Fitness Center) vs. Interactive, Fun, Social (Video Game Arcade)
+Combinatorics: Active Interactive Games, Healthy Fun Challenges, Competitive Social Workouts
+Seed Idea Generation: Exergaming Stations, Virtual Reality Fitness Challenges, Multiplayer Workout Games
+LLM Processing: Create game concepts, rules, and scoring systems, as well as marketing and branding strategies
+Fusing a Vegan Restaurant with a Science Museum
+Concept Identification: Vegan Restaurant and Science Museum
+Attribute Extraction: Plant-Based, Sustainable, Healthy (Vegan Restaurant) vs. Educational, Interactive, Innovative (Science Museum)
+Combinatorics: Plant-Based Interactive Exhibits, Sustainable Educational Dining, Healthy Innovative Cuisine
+Seed Idea Generation: Edible Plant Exhibits, Cooking Classes with a Scientific Twist, Molecular Gastronomy Menu
+LLM Processing: Develop exhibit designs, class curricula, and menu items, as well as marketing and branding strategies
+Combining a Co-working Space with a Pet Store
+Concept Identification: Co-working Space and Pet Store
+Attribute Extraction: Collaborative, Productive, Professional (Co-working Space) vs. Friendly, Playful, Nurturing (Pet Store)
+Combinatorics: Collaborative Pet-Friendly Spaces, Productive Play Areas, Professional Pet Care Services
+Seed Idea Generation: Pet-Friendly Co-working Desks, On-site Pet Daycare, Pet-themed Networking Events
+LLM Processing: Create space layouts, pet care service offerings, and event plans, as well as marketing and branding strategies
+Integrating a Fashion Boutique with an Art Gallery
+Concept Identification: Fashion Boutique and Art Gallery
+Attribute Extraction: Stylish, Trendy, Creative (Fashion Boutique) vs. Artistic, Thought-provoking, Inspirational (Art Gallery)
+Combinatorics: Stylish Artistic Installations, Trendy Thought-provoking Fashion, Creative Inspirational Clothing
+Seed Idea Generation: Art-inspired Fashion Collections, Interactive Fashion Exhibits, Artist-designed Clothing Lines
+LLM Processing: Develop fashion collection concepts, exhibit designs, and artist collaborations, as well as marketing and branding strategies
+
+"""
+
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