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
-
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) }
-
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}"
-
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
- Quality Control
- Implement thorough artwork evaluation
- Use multiple generation attempts if needed
-
Maintain style consistency
-
Resource Management
- Cache frequently used elements
- Implement rate limiting
-
Monitor API usage
-
Storage Strategy
- Use distributed storage (IPFS)
- Maintain metadata relationships
-
Implement backup mechanisms
-
Error Recovery
- Implement graceful fallbacks
- Maintain partial results
- Provide clear error messages