""" CompI Phase 2.B: Data/Logic Input to Image Generation This module implements data-driven AI art generation that combines: - CSV data analysis and processing - Mathematical formula evaluation - Data-to-text conversion for prompt enhancement - Data visualization for artistic conditioning - Intelligent fusion of data insights with creative prompts Features: - Support for CSV files with comprehensive data analysis - Safe mathematical formula evaluation with NumPy - Poetic text generation from data patterns - Data visualization creation for artistic inspiration - Comprehensive metadata logging and filename conventions - Batch processing capabilities for multiple datasets """ import os import sys import torch import json import pandas as pd import numpy as np from datetime import datetime from typing import Dict, List, Optional, Tuple, Union from pathlib import Path import logging # Add project root to path sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..')) from diffusers import StableDiffusionPipeline from PIL import Image from src.utils.data_utils import DataProcessor, DataToTextConverter, DataVisualizer, DataFeatures from src.utils.logging_utils import setup_logger from src.utils.file_utils import ensure_directory_exists, generate_filename # Setup logging logger = setup_logger(__name__) class CompIPhase2BDataToImage: """ CompI Phase 2.B: Data/Logic Input to Image Generation System Transforms structured data and mathematical formulas into AI-generated art """ def __init__( self, model_name: str = "runwayml/stable-diffusion-v1-5", device: str = "auto", output_dir: str = "outputs", visualization_style: str = "artistic" ): """ Initialize the data-to-image generation system Args: model_name: Stable Diffusion model to use device: Device for inference (auto, cpu, cuda) output_dir: Directory for saving generated images visualization_style: Style for data visualizations """ self.model_name = model_name self.device = self._setup_device(device) self.output_dir = Path(output_dir) ensure_directory_exists(self.output_dir) # Initialize components self.pipe = None self.data_processor = DataProcessor() self.text_converter = DataToTextConverter() self.visualizer = DataVisualizer(style=visualization_style) logger.info(f"Initialized CompI Phase 2.B on {self.device}") def _setup_device(self, device: str) -> str: """Setup and validate device""" if device == "auto": device = "cuda" if torch.cuda.is_available() else "cpu" if device == "cuda" and not torch.cuda.is_available(): logger.warning("CUDA requested but not available, falling back to CPU") device = "cpu" return device def _load_pipeline(self): """Lazy load the Stable Diffusion pipeline""" if self.pipe is None: logger.info(f"Loading Stable Diffusion model: {self.model_name}") # Custom safety checker that allows creative content def dummy_safety_checker(images, **kwargs): return images, [False] * len(images) self.pipe = StableDiffusionPipeline.from_pretrained( self.model_name, torch_dtype=torch.float16 if self.device == "cuda" else torch.float32, safety_checker=dummy_safety_checker, requires_safety_checker=False ) self.pipe = self.pipe.to(self.device) self.pipe.enable_attention_slicing() if self.device == "cuda": self.pipe.enable_model_cpu_offload() logger.info("Stable Diffusion pipeline loaded successfully") def analyze_csv_data(self, csv_path: str) -> Tuple[pd.DataFrame, DataFeatures, str, Image.Image]: """ Comprehensive CSV data analysis Args: csv_path: Path to CSV file Returns: Tuple of (DataFrame, DataFeatures, poetic_description, visualization_image) """ logger.info(f"Analyzing CSV data: {csv_path}") # Load and analyze data df = pd.read_csv(csv_path) features = self.data_processor.analyze_csv_data(df) # Generate poetic description poetic_description = self.text_converter.generate_poetic_description(features) # Create visualization visualization_image = self.visualizer.create_data_visualization(df, features) return df, features, poetic_description, visualization_image def evaluate_mathematical_formula(self, formula: str, num_points: int = 100) -> Tuple[np.ndarray, Dict, str, Image.Image]: """ Evaluate mathematical formula and create artistic interpretation Args: formula: Mathematical expression num_points: Number of points to generate Returns: Tuple of (result_array, metadata, poetic_description, visualization_image) """ logger.info(f"Evaluating mathematical formula: {formula}") # Evaluate formula result_array, metadata = self.data_processor.evaluate_formula(formula, num_points) # Generate poetic description poetic_description = self.text_converter.generate_formula_description(formula, metadata) # Create visualization visualization_image = self.visualizer.create_formula_visualization(result_array, formula, metadata) return result_array, metadata, poetic_description, visualization_image def generate_image( self, text_prompt: str, style: str = "", mood: str = "", csv_path: Optional[str] = None, formula: Optional[str] = None, num_images: int = 1, height: int = 512, width: int = 512, num_inference_steps: int = 30, guidance_scale: float = 7.5, seed: Optional[int] = None, save_data_visualization: bool = True ) -> List[Dict]: """ Generate images with data/formula conditioning Args: text_prompt: Base text prompt style: Art style mood: Mood/atmosphere csv_path: Optional path to CSV file formula: Optional mathematical formula num_images: Number of images to generate height: Image height width: Image width num_inference_steps: Number of diffusion steps guidance_scale: Guidance scale for generation seed: Random seed for reproducibility save_data_visualization: Whether to save data visualization Returns: List of generation results with metadata """ self._load_pipeline() # Process data input data_features = None poetic_description = "" data_visualization = None data_type = "none" if csv_path and os.path.exists(csv_path): df, data_features, poetic_description, data_visualization = self.analyze_csv_data(csv_path) data_type = "csv" elif formula and formula.strip(): result_array, formula_metadata, poetic_description, data_visualization = self.evaluate_mathematical_formula(formula) data_type = "formula" # Create enhanced prompt enhanced_prompt = text_prompt if style: enhanced_prompt += f", {style}" if mood: enhanced_prompt += f", {mood}" if poetic_description: enhanced_prompt += f", {poetic_description}" logger.info(f"Generating {num_images} image(s) with enhanced prompt") results = [] for i in range(num_images): # Set up generation parameters current_seed = seed if seed is not None else torch.seed() generator = torch.Generator(device=self.device).manual_seed(current_seed) # Generate image with torch.autocast(self.device) if self.device == "cuda" else torch.no_grad(): result = self.pipe( enhanced_prompt, height=height, width=width, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator ) image = result.images[0] # Create metadata metadata = { "timestamp": datetime.now().isoformat(), "text_prompt": text_prompt, "style": style, "mood": mood, "enhanced_prompt": enhanced_prompt, "poetic_description": poetic_description, "data_type": data_type, "csv_path": csv_path, "formula": formula, "generation_params": { "height": height, "width": width, "num_inference_steps": num_inference_steps, "guidance_scale": guidance_scale, "seed": current_seed, "model": self.model_name }, "device": self.device, "phase": "2B_data_to_image" } # Add data features to metadata if data_features: metadata["data_features"] = data_features.to_dict() # Generate filename filename = self._generate_filename( text_prompt, style, mood, current_seed, i + 1, data_type=data_type ) # Save image and metadata image_path = self.output_dir / f"{filename}.png" metadata_path = self.output_dir / f"{filename}_metadata.json" image.save(image_path) with open(metadata_path, 'w') as f: json.dump(metadata, f, indent=2) # Save data visualization if requested data_viz_path = None if save_data_visualization and data_visualization: data_viz_path = self.output_dir / f"{filename}_data_viz.png" data_visualization.save(data_viz_path) results.append({ "image": image, "image_path": str(image_path), "metadata_path": str(metadata_path), "data_visualization_path": str(data_viz_path) if data_viz_path else None, "data_visualization": data_visualization, "metadata": metadata, "filename": filename, "poetic_description": poetic_description }) logger.info(f"Generated image {i+1}/{num_images}: {filename}") return results def _generate_filename( self, prompt: str, style: str, mood: str, seed: int, variation: int, data_type: str = "none" ) -> str: """Generate descriptive filename following CompI conventions""" # Create prompt slug (first 5 words) prompt_words = prompt.lower().replace(',', '').split()[:5] prompt_slug = "_".join(prompt_words) # Create style and mood slugs style_slug = style.replace(" ", "").replace(",", "")[:10] if style else "standard" mood_slug = mood.replace(" ", "").replace(",", "")[:10] if mood else "neutral" # Timestamp timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") # Data type indicator data_tag = f"_{data_type.upper()}" if data_type != "none" else "" # Combine all elements filename = f"{prompt_slug}_{style_slug}_{mood_slug}_{timestamp}_seed{seed}{data_tag}_v{variation}" return filename def batch_process_csv_files( self, csv_directory: str, text_prompt: str, style: str = "", mood: str = "", **generation_kwargs ) -> List[Dict]: """ Process multiple CSV files in batch Args: csv_directory: Directory containing CSV files text_prompt: Base text prompt for all generations style: Art style mood: Mood/atmosphere **generation_kwargs: Additional generation parameters Returns: List of all generation results """ csv_dir = Path(csv_directory) if not csv_dir.exists(): raise ValueError(f"CSV directory not found: {csv_directory}") # Find CSV files csv_files = list(csv_dir.glob("*.csv")) if not csv_files: raise ValueError(f"No CSV files found in {csv_directory}") logger.info(f"Processing {len(csv_files)} CSV files") all_results = [] for csv_file in csv_files: logger.info(f"Processing: {csv_file.name}") try: results = self.generate_image( text_prompt=text_prompt, style=style, mood=mood, csv_path=str(csv_file), **generation_kwargs ) all_results.extend(results) except Exception as e: logger.error(f"Error processing {csv_file.name}: {e}") continue logger.info(f"Batch processing complete: {len(all_results)} images generated") return all_results def batch_process_formulas( self, formulas: List[str], text_prompt: str, style: str = "", mood: str = "", **generation_kwargs ) -> List[Dict]: """ Process multiple mathematical formulas in batch Args: formulas: List of mathematical formulas text_prompt: Base text prompt for all generations style: Art style mood: Mood/atmosphere **generation_kwargs: Additional generation parameters Returns: List of all generation results """ logger.info(f"Processing {len(formulas)} mathematical formulas") all_results = [] for i, formula in enumerate(formulas): logger.info(f"Processing formula {i+1}/{len(formulas)}: {formula}") try: results = self.generate_image( text_prompt=text_prompt, style=style, mood=mood, formula=formula, **generation_kwargs ) all_results.extend(results) except Exception as e: logger.error(f"Error processing formula '{formula}': {e}") continue logger.info(f"Batch processing complete: {len(all_results)} images generated") return all_results