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"""
CompI Phase 2.C: Emotional/Contextual Input to Image Generation
This module implements emotion-driven AI art generation that combines:
- Emotion detection and sentiment analysis
- Contextual mood processing
- Emoji and text-based emotion recognition
- Color palette generation based on emotions
- Intelligent fusion of emotional context with creative prompts
Features:
- Support for preset emotions, custom emotions, and emoji input
- Automatic sentiment analysis with TextBlob
- Emotion-to-color palette mapping
- Contextual prompt enhancement
- Comprehensive metadata logging and filename conventions
- Batch processing capabilities for multiple emotional contexts
"""
import os
import sys
import torch
import json
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.emotion_utils import EmotionProcessor, EmotionalPromptEnhancer, EmotionAnalysis, EmotionCategory
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 CompIPhase2CEmotionToImage:
"""
CompI Phase 2.C: Emotional/Contextual Input to Image Generation System
Transforms emotions, moods, and contextual feelings into AI-generated art
"""
def __init__(
self,
model_name: str = "runwayml/stable-diffusion-v1-5",
device: str = "auto",
output_dir: str = "outputs"
):
"""
Initialize the emotion-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
"""
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.emotion_processor = EmotionProcessor()
self.prompt_enhancer = EmotionalPromptEnhancer()
logger.info(f"Initialized CompI Phase 2.C 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_emotion(
self,
emotion_input: str,
emotion_type: str = "auto",
contextual_text: Optional[str] = None
) -> EmotionAnalysis:
"""
Comprehensive emotion analysis
Args:
emotion_input: Emotion input (preset, custom, emoji, or text)
emotion_type: Type of input ('preset', 'custom', 'emoji', 'text', 'auto')
contextual_text: Additional contextual text for analysis
Returns:
EmotionAnalysis object with complete analysis
"""
logger.info(f"Analyzing emotion input: {emotion_input}")
# Combine inputs for analysis
analysis_text = emotion_input
if contextual_text:
analysis_text += f" {contextual_text}"
# Determine selected emotion for preset types
selected_emotion = None
if emotion_type == "preset" or (emotion_type == "auto" and emotion_input.lower() in self.emotion_processor.preset_emotions):
selected_emotion = emotion_input.lower()
# Perform emotion analysis
emotion_analysis = self.emotion_processor.analyze_emotion(analysis_text, selected_emotion)
return emotion_analysis
def generate_image(
self,
text_prompt: str,
style: str = "",
emotion_input: str = "",
emotion_type: str = "auto",
contextual_text: str = "",
enhancement_strength: float = 0.7,
num_images: int = 1,
height: int = 512,
width: int = 512,
num_inference_steps: int = 30,
guidance_scale: float = 7.5,
seed: Optional[int] = None
) -> List[Dict]:
"""
Generate images with emotional conditioning
Args:
text_prompt: Base text prompt
style: Art style
emotion_input: Emotion input (preset, custom, emoji, or descriptive text)
emotion_type: Type of emotion input
contextual_text: Additional contextual description
enhancement_strength: How strongly to apply emotion (0-1)
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
Returns:
List of generation results with metadata
"""
self._load_pipeline()
# Analyze emotion if provided
emotion_analysis = None
if emotion_input.strip():
emotion_analysis = self.analyze_emotion(emotion_input, emotion_type, contextual_text)
# Create enhanced prompt
if emotion_analysis:
enhanced_prompt = self.prompt_enhancer.enhance_prompt_with_emotion(
text_prompt, style, emotion_analysis, enhancement_strength
)
else:
enhanced_prompt = text_prompt
if style:
enhanced_prompt += f", {style}"
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,
"emotion_input": emotion_input,
"emotion_type": emotion_type,
"contextual_text": contextual_text,
"enhancement_strength": enhancement_strength,
"enhanced_prompt": enhanced_prompt,
"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": "2C_emotion_to_image"
}
# Add emotion analysis to metadata
if emotion_analysis:
metadata["emotion_analysis"] = emotion_analysis.to_dict()
metadata["emotion_tags"] = self.prompt_enhancer.generate_emotion_tags(emotion_analysis)
# Generate filename
filename = self._generate_filename(
text_prompt, style, emotion_analysis, current_seed, i + 1
)
# 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)
results.append({
"image": image,
"image_path": str(image_path),
"metadata_path": str(metadata_path),
"metadata": metadata,
"filename": filename,
"emotion_analysis": emotion_analysis
})
logger.info(f"Generated image {i+1}/{num_images}: {filename}")
return results
def _generate_filename(
self,
prompt: str,
style: str,
emotion_analysis: Optional[EmotionAnalysis],
seed: int,
variation: int
) -> 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 slug
style_slug = style.replace(" ", "").replace(",", "")[:10] if style else "standard"
# Create emotion slug
if emotion_analysis:
emotion_slug = f"{emotion_analysis.primary_emotion.value}_{emotion_analysis.intensity_level}"[:15]
else:
emotion_slug = "neutral"
# Timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
# Combine all elements
filename = f"{prompt_slug}_{style_slug}_{emotion_slug}_{timestamp}_seed{seed}_EMO_v{variation}"
return filename
def batch_process_emotions(
self,
text_prompt: str,
style: str,
emotions: List[str],
emotion_type: str = "auto",
**generation_kwargs
) -> List[Dict]:
"""
Process multiple emotions in batch
Args:
text_prompt: Base text prompt for all generations
style: Art style
emotions: List of emotions to process
emotion_type: Type of emotion input
**generation_kwargs: Additional generation parameters
Returns:
List of all generation results
"""
logger.info(f"Processing {len(emotions)} emotions in batch")
all_results = []
for i, emotion in enumerate(emotions):
logger.info(f"Processing emotion {i+1}/{len(emotions)}: {emotion}")
try:
results = self.generate_image(
text_prompt=text_prompt,
style=style,
emotion_input=emotion,
emotion_type=emotion_type,
**generation_kwargs
)
all_results.extend(results)
except Exception as e:
logger.error(f"Error processing emotion '{emotion}': {e}")
continue
logger.info(f"Batch processing complete: {len(all_results)} images generated")
return all_results
def generate_emotion_palette_art(
self,
text_prompt: str,
style: str,
emotion_input: str,
use_color_conditioning: bool = True,
**generation_kwargs
) -> List[Dict]:
"""
Generate art using emotion-derived color palettes
Args:
text_prompt: Base text prompt
style: Art style
emotion_input: Emotion input
use_color_conditioning: Whether to add color palette to prompt
**generation_kwargs: Additional generation parameters
Returns:
List of generation results with color palette conditioning
"""
# Analyze emotion to get color palette
emotion_analysis = self.analyze_emotion(emotion_input)
# Enhance prompt with color information if requested
if use_color_conditioning and emotion_analysis:
color_names = self._hex_to_color_names(emotion_analysis.color_palette)
color_prompt = f"with a color palette of {', '.join(color_names)}"
enhanced_text_prompt = f"{text_prompt}, {color_prompt}"
else:
enhanced_text_prompt = text_prompt
return self.generate_image(
text_prompt=enhanced_text_prompt,
style=style,
emotion_input=emotion_input,
**generation_kwargs
)
def _hex_to_color_names(self, hex_colors: List[str]) -> List[str]:
"""Convert hex colors to approximate color names"""
color_mapping = {
"#FFD700": "golden", "#FFA500": "orange", "#FF69B4": "pink",
"#00CED1": "turquoise", "#32CD32": "lime", "#4169E1": "blue",
"#6495ED": "cornflower", "#708090": "slate", "#2F4F4F": "dark slate",
"#191970": "midnight blue", "#DC143C": "crimson", "#B22222": "firebrick",
"#8B0000": "dark red", "#FF4500": "orange red", "#FF6347": "tomato",
"#800080": "purple", "#4B0082": "indigo", "#2E2E2E": "dark gray",
"#696969": "dim gray", "#A9A9A9": "dark gray", "#FF1493": "deep pink",
"#FFB6C1": "light pink", "#FFC0CB": "pink", "#FFFF00": "yellow",
"#C71585": "medium violet", "#DB7093": "pale violet", "#20B2AA": "light sea green",
"#48D1CC": "medium turquoise", "#40E0D0": "turquoise", "#AFEEEE": "pale turquoise",
"#9370DB": "medium purple", "#8A2BE2": "blue violet", "#7B68EE": "medium slate blue",
"#6A5ACD": "slate blue", "#483D8B": "dark slate blue", "#808080": "gray",
"#C0C0C0": "silver", "#D3D3D3": "light gray", "#DCDCDC": "gainsboro"
}
color_names = []
for hex_color in hex_colors:
color_name = color_mapping.get(hex_color.upper(), "colorful")
color_names.append(color_name)
return color_names
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