""" Model management for Frame 0 Laboratory for MIA BAGEL 7B integration via API calls """ import spaces import logging import tempfile import os from typing import Optional, Dict, Any, Tuple from PIL import Image from gradio_client import Client, handle_file from config import get_device_config from utils import clean_memory, safe_execute logger = logging.getLogger(__name__) class BaseImageAnalyzer: """Base class for image analysis models""" def __init__(self): self.is_initialized = False self.device_config = get_device_config() def initialize(self) -> bool: """Initialize the model""" raise NotImplementedError def analyze_image(self, image: Image.Image) -> Tuple[str, Dict[str, Any]]: """Analyze image and return description""" raise NotImplementedError def cleanup(self) -> None: """Clean up model resources""" clean_memory() class BagelAPIAnalyzer(BaseImageAnalyzer): """BAGEL 7B model via API calls to working Space""" def __init__(self): super().__init__() self.client = None self.space_url = "Malaji71/Bagel-7B-Demo" self.api_endpoint = "/image_understanding" def initialize(self) -> bool: """Initialize BAGEL API client""" if self.is_initialized: return True try: logger.info("Initializing BAGEL API client...") self.client = Client(self.space_url) self.is_initialized = True logger.info("BAGEL API client initialized successfully") return True except Exception as e: logger.error(f"BAGEL API client initialization failed: {e}") return False def _extract_camera_setup(self, description: str) -> Optional[str]: """Extract camera setup recommendation from BAGEL response""" try: # Look for CAMERA_SETUP section if "CAMERA_SETUP:" in description: parts = description.split("CAMERA_SETUP:") if len(parts) > 1: camera_part = parts[1].strip() # Clean up any additional formatting camera_part = camera_part.replace("\n", " ").strip() return camera_part # Alternative patterns for camera recommendations camera_patterns = [ "Shot on ", "Camera: ", "Equipment: ", "Recommended camera:", "Camera setup:" ] for pattern in camera_patterns: if pattern in description: # Extract text after the pattern idx = description.find(pattern) camera_text = description[idx:].split('.')[0] # Take first sentence if len(camera_text) > len(pattern) + 10: # Ensure meaningful content return camera_text.strip() return None except Exception as e: logger.warning(f"Failed to extract camera setup: {e}") return None def _save_temp_image(self, image: Image.Image) -> str: """Save image to temporary file for API call""" try: # Create temporary file temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png') temp_path = temp_file.name temp_file.close() # Save image if image.mode != 'RGB': image = image.convert('RGB') image.save(temp_path, 'PNG') return temp_path except Exception as e: logger.error(f"Failed to save temporary image: {e}") return None def _cleanup_temp_file(self, file_path: str): """Clean up temporary file""" try: if file_path and os.path.exists(file_path): os.unlink(file_path) except Exception as e: logger.warning(f"Failed to cleanup temp file: {e}") @spaces.GPU(duration=60) def analyze_image(self, image: Image.Image, prompt: str = None) -> Tuple[str, Dict[str, Any]]: """Analyze image using BAGEL API""" if not self.is_initialized: success = self.initialize() if not success: return "BAGEL API not available", {"error": "API initialization failed"} temp_path = None # Initialize metadata early metadata = { "model": "BAGEL-7B-API", "device": "api", "confidence": 0.9, "api_endpoint": self.api_endpoint, "space_url": self.space_url, "prompt_used": prompt, "has_camera_suggestion": False } try: # Default prompt for detailed image analysis if prompt is None: prompt = """You are analyzing a photograph for FLUX image generation. Provide a detailed analysis in two sections: 1. DESCRIPTION: Start directly with the subject (e.g., "A color photograph showing..." or "A black and white photograph depicting..."). First, determine if this is a photograph, illustration, or artwork. Then describe the visual elements, composition, lighting, colors (be specific about the color palette - warm tones, cool tones, monochrome, etc.), artistic style, mood, and atmosphere. Also mention the image format/aspect ratio (square, portrait, landscape, widescreen, etc.) and how the composition uses this format. Write as a flowing paragraph without numbered lists. 2. CAMERA_SETUP: Based on the photographic characteristics, scene type, and aspect ratio you observe, recommend the specific camera system and lens that would realistically capture this type of scene: - For street/documentary photography: suggest cameras like Canon EOS R6, Sony A7 IV, Leica Q2 with 35mm or 24-70mm lenses - For portraits: suggest cameras like Canon EOS R5, Sony A7R V with 85mm or 135mm lenses - For landscapes/widescreen: suggest cameras like Phase One XT, Fujifilm GFX with wide-angle lenses (16-35mm, 24-70mm) - For sports/action: suggest cameras like Canon EOS-1D X, Sony A9 III with telephoto lenses - For macro: suggest specialized macro lenses - For cinematic/widescreen formats: suggest cinema cameras or full-frame with appropriate aspect ratios Be specific about focal length, aperture, and shooting style based on what you actually see in the image dimensions and content. Analyze carefully and be accurate about colors, image type, and proportions.""" # Save image to temporary file temp_path = self._save_temp_image(image) if not temp_path: return "Image processing failed", {"error": "Could not save image"} logger.info("Calling BAGEL API for image analysis...") # Call BAGEL API result = self.client.predict( image=handle_file(temp_path), prompt=prompt, show_thinking=False, do_sample=False, text_temperature=0.3, max_new_tokens=512, api_name=self.api_endpoint ) # Extract response (API returns tuple: (image_result, text_response)) if isinstance(result, tuple) and len(result) >= 2: description = result[1] if result[1] else result[0] else: description = str(result) # Clean up the description and extract camera setup if present if isinstance(description, str) and description.strip(): description = description.strip() # Store camera setup separately if found camera_setup = self._extract_camera_setup(description) if camera_setup: metadata["camera_setup"] = camera_setup metadata["has_camera_suggestion"] = True else: metadata["has_camera_suggestion"] = False else: description = "Detailed image analysis completed successfully" metadata["has_camera_suggestion"] = False # Update final metadata metadata.update({ "response_length": len(description) }) logger.info(f"BAGEL API analysis complete: {len(description)} characters") return description, metadata except Exception as e: logger.error(f"BAGEL API analysis failed: {e}") return "API analysis failed", {"error": str(e), "model": "BAGEL-7B-API"} finally: # Always cleanup temporary file if temp_path: self._cleanup_temp_file(temp_path) def analyze_for_flux_prompt(self, image: Image.Image) -> Tuple[str, Dict[str, Any]]: """Analyze image specifically for FLUX prompt generation""" flux_prompt = """You are analyzing a photograph for professional FLUX generation. Provide two sections: 1. DESCRIPTION: Determine first if this is a real photograph, digital artwork, or illustration. Then create a detailed, flowing description starting directly with the subject. Be precise about: - Image type (photograph, illustration, artwork) - Color palette (specify if color or black/white, warm/cool tones, specific colors) - Photographic style (street, portrait, landscape, documentary, artistic, etc.) - Composition, lighting, mood, and atmosphere Write as a single coherent paragraph. 2. CAMERA_SETUP: Recommend specific professional equipment that would realistically capture this exact scene: - Street/urban scenes: Canon EOS R6, Sony A7 IV, Leica Q2 with 24-70mm f/2.8 or 35mm f/1.4 - Portraits: Canon EOS R5, Sony A7R V, Hasselblad X2D with 85mm f/1.4 or 135mm f/2 - Landscapes: Phase One XT, Fujifilm GFX 100S with 16-35mm f/2.8 or 40mm f/4 - Documentary: Canon EOS-1D X, Sony A9 III with 24-105mm f/4 or 70-200mm f/2.8 - Action/Sports: Canon EOS R3, Sony A1 with 300mm f/2.8 or 400mm f/2.8 Match the equipment to what you actually observe in the scene type and shooting conditions.""" return self.analyze_image(image, flux_prompt) def cleanup(self) -> None: """Clean up API client resources""" try: if hasattr(self, 'client'): self.client = None super().cleanup() logger.info("BAGEL API resources cleaned up") except Exception as e: logger.warning(f"BAGEL API cleanup warning: {e}") class FallbackAnalyzer(BaseImageAnalyzer): """Simple fallback analyzer when BAGEL API is not available""" def __init__(self): super().__init__() def initialize(self) -> bool: """Fallback is always ready""" self.is_initialized = True return True def analyze_image(self, image: Image.Image) -> Tuple[str, Dict[str, Any]]: """Provide basic image description""" try: # Basic image analysis width, height = image.size mode = image.mode # Simple descriptive text based on image properties aspect_ratio = width / height if aspect_ratio > 1.5: orientation = "landscape" camera_suggestion = "wide-angle lens, landscape photography" elif aspect_ratio < 0.75: orientation = "portrait" camera_suggestion = "portrait lens, shallow depth of field" else: orientation = "square" camera_suggestion = "standard lens, balanced composition" description = f"A {orientation} format image with professional composition. The image shows clear detail and good visual balance, suitable for high-quality reproduction. Recommended camera setup: {camera_suggestion}, professional lighting with careful attention to exposure and color balance." metadata = { "model": "Fallback", "device": "cpu", "confidence": 0.6, "image_size": f"{width}x{height}", "color_mode": mode, "orientation": orientation, "aspect_ratio": round(aspect_ratio, 2) } return description, metadata except Exception as e: logger.error(f"Fallback analysis failed: {e}") return "Professional image suitable for detailed analysis and prompt generation", {"error": str(e), "model": "Fallback"} class ModelManager: """Manager for handling image analysis models""" def __init__(self, preferred_model: str = "bagel-api"): self.preferred_model = preferred_model self.analyzers = {} self.current_analyzer = None def get_analyzer(self, model_name: str = None) -> Optional[BaseImageAnalyzer]: """Get or create analyzer for specified model""" model_name = model_name or self.preferred_model if model_name not in self.analyzers: if model_name == "bagel-api": self.analyzers[model_name] = BagelAPIAnalyzer() elif model_name == "fallback": self.analyzers[model_name] = FallbackAnalyzer() else: logger.warning(f"Unknown model: {model_name}, using fallback") model_name = "fallback" self.analyzers[model_name] = FallbackAnalyzer() return self.analyzers[model_name] def analyze_image(self, image: Image.Image, model_name: str = None, analysis_type: str = "detailed") -> Tuple[str, Dict[str, Any]]: """Analyze image with specified or preferred model""" # Try preferred model first analyzer = self.get_analyzer(model_name) if analyzer is None: return "No analyzer available", {"error": "Model not found"} # Choose analysis method based on type if analysis_type == "flux" and hasattr(analyzer, 'analyze_for_flux_prompt'): success, result = safe_execute(analyzer.analyze_for_flux_prompt, image) else: success, result = safe_execute(analyzer.analyze_image, image) if success and result[1].get("error") is None: return result else: # Fallback to simple analyzer if main model fails logger.warning(f"Primary model failed, using fallback: {result}") fallback_analyzer = self.get_analyzer("fallback") fallback_success, fallback_result = safe_execute(fallback_analyzer.analyze_image, image) if fallback_success: return fallback_result else: return "All analyzers failed", {"error": "Complete analysis failure"} def cleanup_all(self) -> None: """Clean up all model resources""" for analyzer in self.analyzers.values(): analyzer.cleanup() self.analyzers.clear() clean_memory() logger.info("All analyzers cleaned up") # Global model manager instance model_manager = ModelManager(preferred_model="bagel-api") def analyze_image(image: Image.Image, model_name: str = None, analysis_type: str = "detailed") -> Tuple[str, Dict[str, Any]]: """ Convenience function for image analysis using BAGEL API Args: image: PIL Image to analyze model_name: Optional model name ("bagel-api" or "fallback") analysis_type: Type of analysis ("detailed" or "flux") Returns: Tuple of (description, metadata) """ return model_manager.analyze_image(image, model_name, analysis_type) # Export main components __all__ = [ "BaseImageAnalyzer", "BagelAPIAnalyzer", "FallbackAnalyzer", "ModelManager", "model_manager", "analyze_image" ]