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"""
CompI Phase 2.D: Real-Time Data Feeds to Image Generation
This module implements real-time data-driven AI art generation that combines:
- Weather data integration from multiple APIs
- News headlines and RSS feed processing
- Financial market data incorporation
- Real-time context analysis and summarization
- Intelligent fusion of real-time data with creative prompts
Features:
- Support for weather, news, and financial data feeds
- Automatic data caching and rate limiting
- Context-aware prompt enhancement
- Temporal and thematic analysis
- Comprehensive metadata logging and filename conventions
- Batch processing capabilities for multiple data sources
"""
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.realtime_data_utils import (
RealTimeDataProcessor, RealTimeContext, DataFeedType, RealTimeDataPoint
)
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 CompIPhase2DRealTimeToImage:
"""
CompI Phase 2.D: Real-Time Data Feeds to Image Generation System
Transforms real-time data feeds into AI-generated art
"""
def __init__(
self,
model_name: str = "runwayml/stable-diffusion-v1-5",
device: str = "auto",
output_dir: str = "outputs"
):
"""
Initialize the real-time 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
"""
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 = RealTimeDataProcessor()
logger.info(f"Initialized CompI Phase 2.D 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 fetch_realtime_context(
self,
include_weather: bool = False,
weather_city: str = "New York",
weather_api_key: Optional[str] = None,
include_news: bool = False,
news_category: str = "general",
max_news: int = 3,
news_api_key: Optional[str] = None,
include_financial: bool = False
) -> RealTimeContext:
"""
Fetch real-time context from various data sources
Args:
include_weather: Whether to include weather data
weather_city: City for weather data
weather_api_key: Optional weather API key
include_news: Whether to include news data
news_category: Category of news to fetch
max_news: Maximum number of news items
news_api_key: Optional news API key
include_financial: Whether to include financial data
Returns:
RealTimeContext with processed data
"""
logger.info("Fetching real-time context for art generation")
return self.data_processor.fetch_realtime_context(
include_weather=include_weather,
weather_city=weather_city,
include_news=include_news,
news_category=news_category,
max_news=max_news,
include_financial=include_financial,
weather_api_key=weather_api_key,
news_api_key=news_api_key
)
def generate_image(
self,
text_prompt: str,
style: str = "",
mood: str = "",
include_weather: bool = False,
weather_city: str = "New York",
weather_api_key: Optional[str] = None,
include_news: bool = False,
news_category: str = "general",
max_news: int = 3,
news_api_key: Optional[str] = None,
include_financial: bool = False,
context_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 real-time data conditioning
Args:
text_prompt: Base text prompt
style: Art style
mood: Mood/atmosphere
include_weather: Whether to include weather data
weather_city: City for weather data
weather_api_key: Optional weather API key
include_news: Whether to include news data
news_category: Category of news to fetch
max_news: Maximum number of news items
news_api_key: Optional news API key
include_financial: Whether to include financial data
context_strength: How strongly to apply real-time context (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()
# Fetch real-time context if any data sources are enabled
realtime_context = None
if include_weather or include_news or include_financial:
realtime_context = self.fetch_realtime_context(
include_weather=include_weather,
weather_city=weather_city,
weather_api_key=weather_api_key,
include_news=include_news,
news_category=news_category,
max_news=max_news,
news_api_key=news_api_key,
include_financial=include_financial
)
# Create enhanced prompt
enhanced_prompt = self._create_enhanced_prompt(
text_prompt, style, mood, realtime_context, context_strength
)
logger.info(f"Generating {num_images} image(s) with real-time context")
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,
"context_strength": context_strength,
"data_sources": {
"weather": include_weather,
"news": include_news,
"financial": include_financial
},
"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": "2D_realtime_to_image"
}
# Add real-time context to metadata
if realtime_context:
metadata["realtime_context"] = realtime_context.to_dict()
# Generate filename
filename = self._generate_filename(
text_prompt, style, realtime_context, 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,
"realtime_context": realtime_context
})
logger.info(f"Generated image {i+1}/{num_images}: {filename}")
return results
def _create_enhanced_prompt(
self,
text_prompt: str,
style: str,
mood: str,
realtime_context: Optional[RealTimeContext],
context_strength: float
) -> str:
"""
Create enhanced prompt with real-time context
Args:
text_prompt: Base text prompt
style: Art style
mood: Mood/atmosphere
realtime_context: Real-time context data
context_strength: How strongly to apply context (0-1)
Returns:
Enhanced prompt with real-time context
"""
enhanced_prompt = text_prompt.strip()
# Add style
if style:
enhanced_prompt += f", {style}"
# Add mood
if mood:
enhanced_prompt += f", {mood}"
# Add real-time context based on strength
if realtime_context and context_strength > 0:
if context_strength > 0.7:
# Strong context integration
enhanced_prompt += f", {realtime_context.artistic_inspiration}"
if realtime_context.mood_indicators:
mood_text = ", ".join(realtime_context.mood_indicators[:2])
enhanced_prompt += f", with {mood_text} influences"
elif context_strength > 0.4:
# Moderate context integration
enhanced_prompt += f", {realtime_context.artistic_inspiration}"
else:
# Subtle context integration
if realtime_context.key_themes:
theme = realtime_context.key_themes[0]
enhanced_prompt += f", inspired by {theme}"
return enhanced_prompt
def _generate_filename(
self,
prompt: str,
style: str,
realtime_context: Optional[RealTimeContext],
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 context slug
if realtime_context and realtime_context.data_points:
context_types = []
for dp in realtime_context.data_points:
context_types.append(dp.feed_type.value[:3]) # First 3 chars
context_slug = "_".join(set(context_types))[:15]
else:
context_slug = "static"
# Timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
# Combine all elements
filename = f"{prompt_slug}_{style_slug}_{context_slug}_{timestamp}_seed{seed}_RTDATA_v{variation}"
return filename
def batch_process_data_sources(
self,
text_prompt: str,
style: str,
data_source_configs: List[Dict],
**generation_kwargs
) -> List[Dict]:
"""
Process multiple data source configurations in batch
Args:
text_prompt: Base text prompt for all generations
style: Art style
data_source_configs: List of data source configuration dictionaries
**generation_kwargs: Additional generation parameters
Returns:
List of all generation results
"""
logger.info(f"Processing {len(data_source_configs)} data source configurations")
all_results = []
for i, config in enumerate(data_source_configs):
logger.info(f"Processing configuration {i+1}/{len(data_source_configs)}")
try:
results = self.generate_image(
text_prompt=text_prompt,
style=style,
**config,
**generation_kwargs
)
all_results.extend(results)
except Exception as e:
logger.error(f"Error processing configuration {i+1}: {e}")
continue
logger.info(f"Batch processing complete: {len(all_results)} images generated")
return all_results
def generate_temporal_series(
self,
text_prompt: str,
style: str,
data_config: Dict,
time_intervals: List[int],
**generation_kwargs
) -> List[Dict]:
"""
Generate a series of images with real-time data at different time intervals
Args:
text_prompt: Base text prompt
style: Art style
data_config: Data source configuration
time_intervals: List of time intervals in minutes between generations
**generation_kwargs: Additional generation parameters
Returns:
List of generation results across time
"""
import time
logger.info(f"Generating temporal series with {len(time_intervals)} intervals")
all_results = []
for i, interval in enumerate(time_intervals):
if i > 0: # Don't wait before first generation
logger.info(f"Waiting {interval} minutes before next generation...")
time.sleep(interval * 60) # Convert minutes to seconds
logger.info(f"Generating image {i+1}/{len(time_intervals)}")
try:
# Clear cache to ensure fresh data
self.data_processor.cache.cache.clear()
results = self.generate_image(
text_prompt=text_prompt,
style=style,
**data_config,
**generation_kwargs
)
# Add temporal metadata
for result in results:
result["metadata"]["temporal_series"] = {
"series_index": i,
"total_in_series": len(time_intervals),
"interval_minutes": interval if i > 0 else 0
}
all_results.extend(results)
except Exception as e:
logger.error(f"Error in temporal generation {i+1}: {e}")
continue
logger.info(f"Temporal series complete: {len(all_results)} images generated")
return all_results
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