""" PROFESSIONAL IMAGE GENERATOR - ALL CATEGORIES VERSION ====================================================== Features: ✅ ALL Category Types Supported - No Restrictions ✅ Auto Model Selection Based on Category Keywords ✅ 100+ Category Types Enabled ✅ Intelligent Category Mapping ✅ 1920x1080 Full HD Resolution ✅ Professional Quality Output Author: Professional Image Generation System Date: 2025 """ import requests import json import time import hashlib import random import base64 from pathlib import Path from datetime import datetime from io import BytesIO import sys import re # Install PIL if needed try: from PIL import Image except ImportError: print("📦 Installing Pillow...") import subprocess subprocess.check_call([sys.executable, "-m", "pip", "install", "pillow"]) from PIL import Image print("✅ Pillow installed!\n") print("="*80) print("PROFESSIONAL IMAGE GENERATOR - ALL CATEGORIES VERSION V3.0") print("="*80) print("✅ Full HD 1920x1080 | ALL Categories Supported | Smart Model Selection") print("="*80) print() # ============================================ # CONFIGURATION # ============================================ # API Configuration API_URL = "https://image.pollinations.ai/prompt/" OUTPUT_FOLDER = "professional_images" # Quality Settings QUALITY_WIDTH = 1920 QUALITY_HEIGHT = 1080 # Safety Settings SAFETY_ENABLED = True # ============================================ # COMPREHENSIVE CATEGORY DEFINITIONS # ============================================ # Main category configurations with model mappings MAIN_CATEGORIES = { # REALISTIC STYLES 'realistic': { 'model': 'flux-realism', 'keywords': ['realistic', 'photorealistic', 'hyperrealistic', 'photo', 'photography'], 'suffix': 'ultra photorealistic, professional photography, extremely detailed, sharp focus, perfect lighting, 8K UHD, RAW photo, high quality, film grain, Fujifilm XT3, professional color grading, award winning photography', 'negative': 'cartoon, anime, illustration, painting, drawing, art, sketch, 3d render, deformed, ugly', }, # 3D & CGI STYLES '3d': { 'model': 'flux-3d', 'keywords': ['3d', 'cgi', 'render', 'rendered', '3d-render', 'octane'], 'suffix': '3D render, octane render, unreal engine 5, ray tracing, ultra detailed 3D model, CGI, volumetric lighting, ambient occlusion, subsurface scattering, physically based rendering, 8K resolution, studio lighting', 'negative': '2d, flat, cartoon, anime, painting, sketch, low poly, low quality', }, # CARTOON & ANIMATION 'cartoonistic': { 'model': 'flux-cablyai', 'keywords': ['cartoonistic', 'cartoon', 'animated', 'animation', 'disney', 'pixar', 'toon'], 'suffix': 'cartoon style, disney pixar style, animated, colorful, clean lines, vibrant colors, professional animation, studio quality, character design, expressive, polished, high quality cartoon', 'negative': 'realistic, photo, 3d render, dark, gritty, horror, ugly', }, # COMIC & GRAPHIC 'comic': { 'model': 'flux-cartoon', 'keywords': ['comic', 'comic-book', 'graphic-novel', 'pop-art', 'graphic', 'comics'], 'suffix': 'comic book style, comic art, graphic novel, bold lines, vibrant colors, dynamic composition, pop art, professional comic illustration, ink and color, halftone dots', 'negative': 'realistic, photo, 3d render, blurry, low quality', }, # ANIME & MANGA 'anime': { 'model': 'flux-anime', 'keywords': ['anime', 'manga', 'anime-style', 'manga-style', 'japanese-anime'], 'suffix': 'anime style, manga art, japanese animation, cel shaded, vibrant colors, expressive eyes, detailed hair, professional anime artwork, studio quality, beautiful lighting', 'negative': 'realistic, photo, western cartoon, 3d render, ugly, deformed', }, # CINEMATIC & FILM 'cinematic': { 'model': 'flux', 'keywords': ['cinematic', 'cinematic-shot', 'movie', 'film', 'hollywood', 'epic'], 'suffix': 'cinematic shot, movie scene, cinematic lighting, epic composition, dramatic atmosphere, film photography, anamorphic lens, depth of field, color graded, professional production, blockbuster movie quality, 35mm film, IMAX quality', 'negative': 'amateur, low quality, snapshot, boring composition', }, } # Extended category mappings - maps any category to best model EXTENDED_CATEGORIES = { # REALISTIC VARIATIONS 'photorealistic': 'realistic', 'hyperrealistic': 'realistic', 'photo': 'realistic', 'photography': 'realistic', 'professional-photography': 'realistic', # 3D VARIATIONS 'cgi': '3d', 'render': '3d', '3d-render': '3d', 'octane-render': '3d', 'unreal-engine': '3d', # CARTOON VARIATIONS 'cartoon': 'cartoonistic', 'animated': 'cartoonistic', 'animation': 'cartoonistic', 'disney': 'cartoonistic', 'pixar': 'cartoonistic', 'toon': 'cartoonistic', # COMIC VARIATIONS 'comic-book': 'comic', 'graphic-novel': 'comic', 'pop-art': 'comic', 'comics': 'comic', # ANIME VARIATIONS 'manga': 'anime', 'anime-style': 'anime', 'manga-style': 'anime', # CINEMATIC VARIATIONS 'movie': 'cinematic', 'film': 'cinematic', 'hollywood': 'cinematic', 'epic': 'cinematic', 'cinematic-shot': 'cinematic', # ARTISTIC STYLES 'artistic': 'cinematic', 'art': 'cinematic', 'painting': 'cinematic', 'oil-painting': 'cinematic', 'watercolor': 'cinematic', 'digital-art': 'cinematic', 'digital-painting': 'cinematic', 'concept-art': 'cinematic', 'illustration': 'cinematic', # FANTASY & SCI-FI 'fantasy': 'cinematic', 'sci-fi': 'cinematic', 'science-fiction': 'cinematic', 'futuristic': 'cinematic', 'cyberpunk': 'cinematic', 'steampunk': 'cinematic', 'space': 'cinematic', # PORTRAIT & CHARACTER 'portrait': 'realistic', 'character': '3d', 'character-design': '3d', 'face': 'realistic', 'headshot': 'realistic', # ARCHITECTURE & ENVIRONMENT 'architectural': '3d', 'architecture': '3d', 'landscape': 'realistic', 'nature': 'realistic', 'cityscape': 'realistic', 'environment': 'cinematic', # PRODUCT & COMMERCIAL 'product': 'realistic', 'product-photography': 'realistic', 'commercial': 'realistic', 'advertising': 'realistic', 'marketing': 'realistic', # ABSTRACT & MODERN 'abstract': 'cinematic', 'modern': 'cinematic', 'contemporary': 'cinematic', 'minimalist': 'cinematic', 'geometric': 'cinematic', # SKETCH & DRAWING 'sketch': 'cinematic', 'pencil': 'cinematic', 'drawing': 'cinematic', 'line-art': 'comic', # DARK & DRAMATIC 'dark': 'cinematic', 'horror': 'cinematic', 'gothic': 'cinematic', 'noir': 'cinematic', 'dramatic': 'cinematic', # VINTAGE & RETRO 'vintage': 'realistic', 'retro': 'realistic', '80s': 'cartoonistic', '90s': 'cartoonistic', 'old-school': 'realistic', # GAME STYLES 'game': '3d', 'game-art': '3d', 'pixel-art': 'cartoonistic', 'low-poly': '3d', 'voxel': '3d', # FASHION & BEAUTY 'fashion': 'realistic', 'beauty': 'realistic', 'glamour': 'realistic', 'editorial': 'realistic', # TEXTURE & MATERIAL 'texture': '3d', 'material': '3d', 'pbr': '3d', # SPECIFIC STYLES 'impressionist': 'cinematic', 'expressionist': 'cinematic', 'surreal': 'cinematic', 'surrealism': 'cinematic', 'cubist': 'cinematic', 'baroque': 'cinematic', 'renaissance': 'realistic', # MOOD & ATMOSPHERE 'moody': 'cinematic', 'atmospheric': 'cinematic', 'dreamy': 'cinematic', 'ethereal': 'cinematic', 'mystical': 'cinematic', 'magical': 'cinematic', # TIME PERIODS 'medieval': 'cinematic', 'ancient': 'cinematic', 'modern-day': 'realistic', 'post-apocalyptic': 'cinematic', # CULTURAL STYLES 'japanese': 'anime', 'chinese': 'cinematic', 'korean': 'realistic', 'western': 'cinematic', 'eastern': 'cinematic', # SPECIFIC SUBJECTS 'animal': 'realistic', 'wildlife': 'realistic', 'food': 'realistic', 'interior': 'realistic', 'exterior': 'realistic', 'vehicle': '3d', 'weapon': '3d', 'robot': '3d', 'mecha': 'anime', } # Style enhancements for specific categories CATEGORY_ENHANCEMENTS = { 'fantasy': 'magical, mystical, enchanted, fantasy world, epic fantasy', 'sci-fi': 'futuristic, advanced technology, space age, sci-fi elements', 'cyberpunk': 'neon lights, cyberpunk aesthetic, futuristic city, blade runner style', 'horror': 'dark, eerie, creepy, horror atmosphere, dramatic shadows', 'watercolor': 'watercolor painting, soft colors, artistic brush strokes', 'oil-painting': 'oil painting style, classical art, museum quality', 'sketch': 'pencil sketch, hand drawn, artistic lines, sketch style', 'portrait': 'professional portrait, studio lighting, shallow depth of field', 'landscape': 'beautiful landscape, scenic view, nature photography', 'product': 'product shot, clean background, professional lighting, commercial quality', 'architectural': 'architectural photography, professional composition, clean lines', 'fashion': 'fashion photography, haute couture, editorial style, magazine quality', 'vintage': 'vintage style, retro aesthetic, aged look, nostalgic', 'abstract': 'abstract art, artistic composition, creative design', 'minimalist': 'minimalist design, clean composition, simple elegant', 'dark': 'dark atmosphere, moody lighting, dramatic shadows', 'pixel-art': 'pixel art style, retro gaming, 8-bit aesthetic', 'steampunk': 'steampunk aesthetic, Victorian era, brass and copper, mechanical', } # ============================================ # INTELLIGENT CATEGORY DETECTION # ============================================ def detect_category_type(category): """ Intelligently detect which main category to use based on input Args: category: Input category string Returns: Main category name """ category_lower = category.lower().strip() category_normalized = category_lower.replace(' ', '-').replace('_', '-') # Direct match in extended categories if category_normalized in EXTENDED_CATEGORIES: return EXTENDED_CATEGORIES[category_normalized] # Check if it's already a main category if category_normalized in MAIN_CATEGORIES: return category_normalized # Keyword matching - check if category contains keywords for main_cat, config in MAIN_CATEGORIES.items(): for keyword in config['keywords']: if keyword in category_normalized or category_normalized in keyword: return main_cat # Default fallback based on common patterns if any(word in category_normalized for word in ['real', 'photo']): return 'realistic' elif any(word in category_normalized for word in ['3d', 'cgi', 'render']): return '3d' elif any(word in category_normalized for word in ['cartoon', 'anim', 'disney']): return 'cartoonistic' elif any(word in category_normalized for word in ['comic', 'manga']): return 'comic' elif any(word in category_normalized for word in ['anime', 'manga']): return 'anime' elif any(word in category_normalized for word in ['cinema', 'movie', 'film']): return 'cinematic' # If no match, default to cinematic (most versatile) print(f" ℹ️ Unknown category '{category}', using 'cinematic' as default") return 'cinematic' def enhance_prompt_for_category(prompt, category): """ Enhance prompt based on category with intelligent additions Args: prompt: Original user prompt category: Selected category Returns: Enhanced prompt string """ category_lower = category.lower().strip().replace(' ', '-').replace('_', '-') # Detect main category main_category = detect_category_type(category) category_config = MAIN_CATEGORIES[main_category] # Base quality prefix quality_prefix = "masterpiece, best quality, ultra detailed, professional, perfect composition" # Get main category suffix style_suffix = category_config['suffix'] # Add specific category enhancement if available extra_enhancement = "" if category_lower in CATEGORY_ENHANCEMENTS: extra_enhancement = f", {CATEGORY_ENHANCEMENTS[category_lower]}" # Technical enhancers technical = "perfect anatomy, perfect perspective, golden ratio composition" # Build enhanced prompt enhanced = f"{quality_prefix}, {prompt}, {style_suffix}{extra_enhancement}, {technical}" # Add category-specific camera/render details if main_category == 'realistic': enhanced += ", shot on Canon EOS R5, 85mm lens, f/1.4, professional photography" elif main_category == '3d': enhanced += ", rendered in Octane, global illumination, HDRI lighting, 8K quality" elif main_category == 'cinematic': enhanced += ", cinematic composition, film grain, anamorphic lens" elif main_category == 'anime': enhanced += ", trending on pixiv, studio quality" elif main_category == 'cartoonistic': enhanced += ", Disney animation studios, Pixar quality" elif main_category == 'comic': enhanced += ", professional comic artist, Marvel style" return enhanced def get_negative_prompt(category): """ Get negative prompt based on category Args: category: Category name Returns: Negative prompt string """ main_category = detect_category_type(category) return MAIN_CATEGORIES[main_category].get('negative', '') def get_model_for_category(category): """ Get the best model for a given category Args: category: Category name Returns: Model name """ main_category = detect_category_type(category) return MAIN_CATEGORIES[main_category]['model'] def get_optimized_seed(category, prompt): """ Generate optimized seed based on category and prompt Args: category: Category name prompt: Prompt text Returns: Optimized seed value """ main_category = detect_category_type(category) # Seed ranges for different categories seed_ranges = { 'realistic': (1000, 5000), '3d': (5000, 10000), 'cartoonistic': (10000, 15000), 'comic': (15000, 20000), 'anime': (20000, 25000), 'cinematic': (25000, 30000), } min_seed, max_seed = seed_ranges.get(main_category, (1000, 30000)) prompt_hash = int(hashlib.md5(prompt.encode()).hexdigest()[:8], 16) random_component = random.randint(0, 1000) base_seed = (prompt_hash + random_component) % (max_seed - min_seed) optimized_seed = min_seed + base_seed return optimized_seed # ============================================ # IMAGE GENERATOR CLASS # ============================================ class ProfessionalImageGenerator: """Professional image generation system supporting ALL category types""" def __init__(self): self.generation_count = 0 self.create_output_folders() print("✅ ProfessionalImageGenerator initialized") print(f"📁 Output folder: {OUTPUT_FOLDER}/") print(f"🎨 ALL Categories Supported - No Restrictions") print(f"🤖 Intelligent Model Selection Enabled") def create_output_folders(self): """Create organized output folder structure""" base_path = Path(OUTPUT_FOLDER) base_path.mkdir(exist_ok=True) # Create folders for main categories for category in MAIN_CATEGORIES.keys(): (base_path / category).mkdir(exist_ok=True) # Create general folder for other categories (base_path / "other").mkdir(exist_ok=True) (base_path / "metadata").mkdir(exist_ok=True) (base_path / "tests").mkdir(exist_ok=True) def generate_single_image_bytes(self, prompt, category, model_name, width=None, height=None): """ Generate single image and return bytes for FastAPI Accepts ANY category type - no restrictions Args: prompt: The text prompt category: ANY category name (used for prompt enhancement) model_name: Specific model to use (flux, nano-banana, turbo) width: Image width (default 1920) height: Image height (default 1080) Returns: Image bytes or None if failed """ # Use default dimensions if not specified if width is None: width = QUALITY_WIDTH if height is None: height = QUALITY_HEIGHT self.generation_count += 1 # Normalize category category = category.lower().strip() # Detect main category for prompt enhancement main_category = detect_category_type(category) # Use provided model_name directly model = model_name # Enhance prompt enhanced_prompt = enhance_prompt_for_category(prompt, category) negative_prompt = get_negative_prompt(category) # Generate optimized seed seed = get_optimized_seed(category, prompt) # Build URL url = f"{API_URL}{requests.utils.quote(enhanced_prompt)}" # Build parameters params = { 'width': width, 'height': height, 'model': model, 'seed': seed, 'nologo': 'true', 'enhance': 'true', 'safe': str(SAFETY_ENABLED).lower(), 'nofeed': 'true' } if negative_prompt: params['negative_prompt'] = negative_prompt print(f"\n🎨 Generating Image #{self.generation_count}") print(f" Category: {category.upper()} → Main: {main_category.upper()}") print(f" Model: {model}") print(f" Resolution: {width}x{height}") print(f" Seed: {seed}") print(f" Prompt: {prompt[:60]}...") print(f" ⏳ Processing (15-30 seconds)...") try: response = requests.get(url, params=params, timeout=120) if response.status_code == 200: # Load and process image image = Image.open(BytesIO(response.content)) # Convert to RGB if needed if image.mode != 'RGB': image = image.convert('RGB') # Save to BytesIO output = BytesIO() image.save(output, format='PNG', quality=100, optimize=False) output.seek(0) image_bytes = output.getvalue() image_size_mb = len(image_bytes) / (1024 * 1024) print(f" ✅ SUCCESS") print(f" 📊 Size: {image_size_mb:.2f} MB") print(f" 🎯 Quality: Professional HD") return image_bytes else: print(f" ❌ API Error: HTTP {response.status_code}") print(f" Response: {response.text[:200]}") return None except requests.exceptions.Timeout: print(f" ❌ Timeout: Request took longer than 120 seconds") return None except Exception as e: print(f" ❌ Generation Failed: {str(e)}") import traceback traceback.print_exc() return None def generate_multiple_images(self, prompt, category, model_name, width=None, height=None, image_count=1): """ Generate multiple images and return list with names and base64 data Args: prompt: The text prompt category: Category name for enhancement model_name: Model to use width: Image width height: Image height image_count: Number of images to generate Returns: List of dicts with 'name' and 'base64' keys """ images = [] for i in range(image_count): print(f"\n📸 Generating image {i+1}/{image_count}") # Generate image bytes image_bytes = self.generate_single_image_bytes( prompt=prompt, category=category, model_name=model_name, width=width, height=height ) if image_bytes is None: print(f" ❌ Failed to generate image {i+1}") continue # Generate unique name timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")[:-3] prompt_hash = hashlib.md5(f"{prompt}_{i}".encode()).hexdigest()[:6] prompt_words = prompt.split()[:3] prompt_brief = "_".join(prompt_words).lower().replace(",", "").replace(".", "")[:15] image_name = f"{category}_{prompt_brief}_{timestamp}_{i+1}_{prompt_hash}" # Convert to base64 base64_data = base64.b64encode(image_bytes).decode('utf-8') images.append({ "name": image_name, "base64": base64_data }) print(f" ✅ Generated: {image_name}") # Small delay between generations if i < image_count - 1: time.sleep(1) return images def generate_and_save_image(self, prompt, category, model_name="flux", width=None, height=None): """ Generate image and save to disk Args: prompt: The text prompt category: Category name model_name: Model to use width: Image width height: Image height Returns: Dictionary with result info """ if width is None: width = QUALITY_WIDTH if height is None: height = QUALITY_HEIGHT image_bytes = self.generate_single_image_bytes(prompt, category, model_name, width, height) if image_bytes is None: return {"success": False, "error": "Image generation failed"} # Determine folder main_category = detect_category_type(category) if main_category in MAIN_CATEGORIES: folder = main_category else: folder = "other" # Generate filename timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")[:-3] prompt_hash = hashlib.md5(prompt.encode()).hexdigest()[:6] prompt_words = prompt.split()[:3] prompt_brief = "_".join(prompt_words).lower().replace(",", "").replace(".", "")[:20] filename = f"{category}_{prompt_brief}_{timestamp}_{prompt_hash}.png" # Save to disk output_path = Path(OUTPUT_FOLDER) / folder / filename with open(output_path, 'wb') as f: f.write(image_bytes) # Save metadata metadata = { "generation_info": { "timestamp": datetime.now().isoformat(), "generation_number": self.generation_count, }, "prompt_data": { "original": prompt, "enhanced": enhance_prompt_for_category(prompt, category), "negative": get_negative_prompt(category) }, "technical_details": { "category": category, "main_category": main_category, "model": get_model_for_category(category), "resolution": f"{width}x{height}", "file_size_mb": len(image_bytes) / (1024 * 1024), "seed": get_optimized_seed(category, prompt) } } metadata_path = Path(OUTPUT_FOLDER) / "metadata" / f"{filename.replace('.png', '.json')}" with open(metadata_path, 'w') as f: json.dump(metadata, f, indent=2) print(f" 💾 Saved: {output_path}") print(f" 📄 Metadata: {metadata_path}") return { "success": True, "path": str(output_path), "metadata_path": str(metadata_path), "metadata": metadata } def get_all_categories_info(self): """Get information about all supported categories""" return { "main_categories": { name: { "model": config['model'], "description": config.get('description', ''), "keywords": config['keywords'] } for name, config in MAIN_CATEGORIES.items() }, "extended_categories": EXTENDED_CATEGORIES, "total_supported": len(EXTENDED_CATEGORIES) + len(MAIN_CATEGORIES), "message": "All category types are supported. Unknown categories will be intelligently mapped to the best model." } def test_multiple_categories(self, prompt, categories_list): """ Test generation with multiple categories Args: prompt: Prompt to use categories_list: List of categories to test Returns: List of results """ print("\n" + "="*80) print(f"🧪 TESTING {len(categories_list)} CATEGORIES") print("="*80) print(f"📝 Prompt: {prompt}") print("="*80) results = [] for idx, category in enumerate(categories_list, 1): print(f"\n[{idx}/{len(categories_list)}] Testing: {category}") print("-" * 60) result = self.generate_and_save_image(prompt, category) results.append({ "category": category, "result": result }) if idx < len(categories_list): print("\n⏳ Waiting 2 seconds...") time.sleep(2) # Summary successful = sum(1 for r in results if r['result'].get('success', False)) print("\n" + "="*80) print("✅ TEST COMPLETE") print("="*80) print(f"Success: {successful}/{len(results)}") print(f"Failed: {len(results) - successful}/{len(results)}") print("="*80) return results # ============================================ # TESTING FUNCTIONS # ============================================ def test_popular_categories(): """Test most popular categories""" print("="*80) print("TESTING POPULAR CATEGORIES") print("="*80) generator = ProfessionalImageGenerator() test_cases = [ {"prompt": "A beautiful sunset over mountains", "category": "realistic"}, {"prompt": "A futuristic robot character", "category": "3d"}, {"prompt": "A cute cartoon dog playing", "category": "cartoonistic"}, {"prompt": "A superhero in action pose", "category": "comic"}, {"prompt": "An anime girl with blue hair", "category": "anime"}, {"prompt": "An epic battle scene", "category": "cinematic"}, {"prompt": "A magical fantasy castle", "category": "fantasy"}, {"prompt": "A cyberpunk city at night", "category": "cyberpunk"}, {"prompt": "A professional portrait photo", "category": "portrait"}, {"prompt": "A watercolor painting of flowers", "category": "watercolor"}, ] results = [] for idx, test in enumerate(test_cases, 1): print(f"\n[{idx}/{len(test_cases)}] {test['category'].upper()}") print(f"Prompt: {test['prompt']}") print("-" * 60) result = generator.generate_and_save_image( prompt=test['prompt'], category=test['category'] ) results.append(result) if idx < len(test_cases): print("\n⏳ Waiting 2 seconds...") time.sleep(2) successful = sum(1 for r in results if r.get('success', False)) print("\n" + "="*80) print("✅ POPULAR CATEGORIES TEST COMPLETE") print("="*80) print(f"Success: {successful}/{len(results)}") print("="*80) def test_all_category_types(): """Test various category types to demonstrate flexibility""" print("="*80) print("TESTING ALL CATEGORY TYPES") print("="*80) generator = ProfessionalImageGenerator() # Test diverse categories test_categories = [ "realistic", "3d", "cartoonistic", "comic", "anime", "cinematic", "fantasy", "sci-fi", "cyberpunk", "watercolor", "oil-painting", "sketch", "portrait", "landscape", "product", "architectural", "horror", "vintage", "pixel-art", "fashion" ] prompt = "A majestic dragon in a mystical setting" results = generator.test_multiple_categories(prompt, test_categories) return results def quick_test(): """Quick test with 3 different styles""" print("="*80) print("QUICK TEST - 3 CATEGORIES") print("="*80) generator = ProfessionalImageGenerator() test_cases = [ {"prompt": "A red sports car", "category": "realistic"}, {"prompt": "A cute robot character", "category": "3d"}, {"prompt": "A magical forest scene", "category": "fantasy"} ] for idx, test in enumerate(test_cases, 1): print(f"\n[{idx}/{len(test_cases)}] {test['category'].upper()}") result = generator.generate_and_save_image( prompt=test['prompt'], category=test['category'] ) if idx < len(test_cases): time.sleep(2) print("\n" + "="*80) print("✅ QUICK TEST COMPLETE") print("="*80) def test_custom_categories(): """Test with completely custom category names""" print("="*80) print("TESTING CUSTOM CATEGORY NAMES") print("="*80) print("Testing system's ability to handle any category name...") print("="*80) generator = ProfessionalImageGenerator() custom_categories = [ "epic-battle-scene", "professional-headshot", "game-character-design", "book-cover-art", "movie-poster", "album-cover", "t-shirt-design", "logo-design", "children-book-illustration", "scientific-diagram" ] prompt = "A creative and professional design" results = generator.test_multiple_categories(prompt, custom_categories) return results # ============================================ # MAIN FUNCTION # ============================================ def main(): """Main function with interactive menu""" print("\n" + "="*80) print("PROFESSIONAL IMAGE GENERATOR - ALL CATEGORIES VERSION") print("="*80) print("\n✅ ALL CATEGORY TYPES SUPPORTED") print("✅ NO RESTRICTIONS - ANY CATEGORY NAME ACCEPTED") print("✅ INTELLIGENT MODEL SELECTION") print("✅ 100+ CATEGORY MAPPINGS") print("\n" + "="*80) print("AVAILABLE MAIN CATEGORIES") print("="*80) for name, config in MAIN_CATEGORIES.items(): print(f" • {name.upper()}: {config['model']}") print("\n" + "="*80) print("EXAMPLE SUPPORTED CATEGORIES") print("="*80) example_categories = [ "realistic", "3d", "cartoonistic", "comic", "anime", "cinematic", "fantasy", "sci-fi", "cyberpunk", "watercolor", "oil-painting", "sketch", "portrait", "landscape", "horror", "vintage", "pixel-art", "fashion", "product", "architectural", "steampunk", "dark", "retro", "abstract", "minimalist", "game", "manga", "disney", "pixar" ] print(", ".join(example_categories)) print("\n...and MANY MORE! System accepts ANY category name.") print("\n" + "="*80) print("TESTING MENU") print("="*80) print("1️⃣ Quick Test (3 images)") print("2️⃣ Test Popular Categories (10 images)") print("3️⃣ Test All Category Types (20 images)") print("4️⃣ Test Custom Categories (10 images)") print("5️⃣ Custom Single Generation") print("6️⃣ Show All Supported Categories") print("7️⃣ Exit") print("="*80) choice = input("\nSelect option (1-7): ").strip() if choice == '1': quick_test() elif choice == '2': test_popular_categories() elif choice == '3': test_all_category_types() elif choice == '4': test_custom_categories() elif choice == '5': prompt = input("\nEnter prompt: ").strip() category = input("Enter category (any name): ").strip() generator = ProfessionalImageGenerator() generator.generate_and_save_image(prompt, category) elif choice == '6': generator = ProfessionalImageGenerator() info = generator.get_all_categories_info() print("\n" + "="*80) print("ALL SUPPORTED CATEGORIES") print("="*80) print(f"\nMain Categories: {len(info['main_categories'])}") for name in info['main_categories'].keys(): print(f" • {name}") print(f"\nExtended Categories: {len(info['extended_categories'])}") categories_list = list(info['extended_categories'].keys()) for i in range(0, len(categories_list), 5): print(f" {', '.join(categories_list[i:i+5])}") print(f"\n{info['message']}") print("="*80) elif choice == '7': print("\n👋 Goodbye!") return else: print("\n❌ Invalid option") return print("\n" + "="*80) print("🎉 OPERATION COMPLETE") print(f"📁 Check images in: {OUTPUT_FOLDER}/") print("="*80) if __name__ == "__main__": main()