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Artwork Creation System

The Artwork Creation System is responsible for generating AI-powered artwork based on chat interactions and NFT requirements.

System Architecture

graph TB
    subgraph Artwork Creation System
        CA[Creative Artist Agent]
        AC[Artwork Critic]
        DD[DALL-E Drawer]
    end

    subgraph External Services
        LLM[GPT-4]
        DALLE[DALL-E API]
        IPFS[IPFS Storage]
    end

    subgraph Input Sources
        Chat[Chat History]
        NFT[NFT Metadata]
        Style[Style Preferences]
    end

    Chat --> CA
    NFT --> CA
    Style --> CA

    CA --> AC
    AC --> DD
    DD --> DALLE
    DD --> IPFS

    CA --> LLM
    AC --> LLM

Core Components

Creative Artist Agent

from teleAgent.models.agent_model.artwork_creation import CreativeArtistAgent

artwork_creator = CreativeArtistAgent(
    character_profile=profile,
    dalle_config=dalle_config,
    llm_config=llm_config,
    wallet_address=wallet_address,
    agent_id=agent_id
)

Artwork Critic

class ArtworkCritic:
    def __init__(self, llm_config: dict):
        self.llm_config = llm_config

    async def evaluate_artwork(self, artwork: dict) -> dict:
        """
        Evaluate generated artwork based on style, composition, and theme

        Args:
            artwork: Dictionary containing artwork data and metadata

        Returns:
            Dictionary containing evaluation results
        """
        evaluation = await self._analyze_artwork(artwork)
        suggestions = await self._generate_suggestions(evaluation)
        return {
            "evaluation": evaluation,
            "suggestions": suggestions,
            "score": self._calculate_score(evaluation)
        }

DALL-E Integration

class DalleDrawer:
    def __init__(self, config: dict):
        self.api_key = config["api_key"]
        self.model = config["model"]
        self.size = config.get("size", "1024x1024")

    async def generate_image(self, prompt: str) -> dict:
        """
        Generate image using DALL-E API

        Args:
            prompt: Detailed description for image generation

        Returns:
            Dictionary containing generated image data and metadata
        """
        response = await openai.Image.create(
            prompt=prompt,
            n=1,
            size=self.size,
            model=self.model
        )

        return {
            "url": response.data[0].url,
            "prompt": prompt,
            "metadata": self._extract_metadata(response)
        }

Creation Workflow

  1. Input Analysis python async def analyze_inputs(self, chat_history: list, nft_metadata: dict): """Analyze chat history and NFT metadata to extract creation parameters""" style_elements = await self._extract_style_elements(chat_history) theme = await self._determine_theme(nft_metadata) return { "style": style_elements, "theme": theme, "color_scheme": self._generate_color_scheme(style_elements) }

  2. Prompt Generation python async def generate_prompt(self, analysis_result: dict): """Generate detailed prompt for DALL-E based on analysis""" base_prompt = self._create_base_prompt(analysis_result["theme"]) style_instructions = self._add_style_instructions( analysis_result["style"] ) color_instructions = self._add_color_instructions( analysis_result["color_scheme"] ) return f"{base_prompt}\n{style_instructions}\n{color_instructions}"

  3. Image Generation ```python async def create_artwork(self, prompt: str): """Generate artwork using DALL-E""" try: image_data = await self.dalle_drawer.generate_image(prompt) evaluation = await self.critic.evaluate_artwork(image_data)

       if evaluation["score"] < self.quality_threshold:
           return await self._regenerate_with_improvements(
               prompt, 
               evaluation["suggestions"]
           )
    
       return image_data
    

    except Exception as e: logger.error(f"Artwork generation failed: {e}") raise ArtworkCreationError(str(e)) ```

Usage Examples

Basic Artwork Creation

# Initialize the artwork creator
artwork_creator = CreativeArtistAgent(config)

# Generate artwork from chat history
artwork = await artwork_creator.create_complete_artwork(messages)
if artwork:
    artwork['drawing'].save("generated_artwork.png")

Advanced Usage with Style Control

# Create artwork with specific style preferences
style_config = {
    "artistic_style": "impressionist",
    "color_palette": "warm",
    "composition": "dynamic",
    "mood": "energetic"
}

artwork = await artwork_creator.create_artwork_with_style(
    messages,
    style_config
)

Storage and Retrieval

async def store_artwork(artwork_data: dict):
    """Store artwork and metadata in IPFS"""
    ipfs_client = IPFSClient(config["ipfs_endpoint"])

    # Store image
    image_cid = await ipfs_client.add_file(artwork_data["image"])

    # Store metadata
    metadata = {
        "image": f"ipfs://{image_cid}",
        "prompt": artwork_data["prompt"],
        "style": artwork_data["style"],
        "creation_date": datetime.now().isoformat(),
        "evaluation": artwork_data["evaluation"]
    }

    metadata_cid = await ipfs_client.add_json(metadata)
    return {
        "image_cid": image_cid,
        "metadata_cid": metadata_cid
    }

Error Handling

class ArtworkCreationError(Exception):
    """Base exception for artwork creation errors"""
    pass

async def handle_creation_error(error: Exception):
    """Handle various artwork creation errors"""
    if isinstance(error, DalleAPIError):
        return await fallback_generation_strategy()
    elif isinstance(error, StorageError):
        return await retry_storage_with_backoff()
    elif isinstance(error, ValidationError):
        return await regenerate_with_fixes()
    raise ArtworkCreationError(f"Unhandled error: {str(error)}")

Best Practices

  1. Quality Control
  2. Implement thorough artwork evaluation
  3. Use multiple generation attempts if needed
  4. Maintain style consistency

  5. Resource Management

  6. Cache frequently used elements
  7. Implement rate limiting
  8. Monitor API usage

  9. Storage Strategy

  10. Use distributed storage (IPFS)
  11. Maintain metadata relationships
  12. Implement backup mechanisms

  13. Error Recovery

  14. Implement graceful fallbacks
  15. Maintain partial results
  16. Provide clear error messages