app.py

import gradio as gr
import torch
from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
from PIL import Image
import io
import requests
import os
from datetime import datetime
import re
import time
import json
from typing import List, Optional, Dict
from fastapi import FastAPI, HTTPException, BackgroundTasks
from pydantic import BaseModel
import gc
import psutil
import threading
import uuid
import hashlib
from enum import Enum
import random
import time
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

# External OCI API URL - YOUR BUCKET SAVING API
OCI_API_BASE_URL = "https://yukee1992-oci-story-book.hf.space"

# Create local directories for test images
PERSISTENT_IMAGE_DIR = "generated_test_images"
os.makedirs(PERSISTENT_IMAGE_DIR, exist_ok=True)
print(f"📁 Created local image directory: {PERSISTENT_IMAGE_DIR}")

# Initialize FastAPI app
app = FastAPI(title="Storybook Generator API")

# Add CORS middleware
from fastapi.middleware.cors import CORSMiddleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Job Status Enum
class JobStatus(str, Enum):
    PENDING = "pending"
    PROCESSING = "processing"
    COMPLETED = "completed"
    FAILED = "failed"

# Simple Story scene model
class StoryScene(BaseModel):
    visual: str
    text: str

class CharacterDescription(BaseModel):
    name: str
    description: str

class StorybookRequest(BaseModel):
    story_title: str
    scenes: List[StoryScene]
    characters: List[CharacterDescription] = []
    model_choice: str = "dreamshaper-8"
    style: str = "childrens_book"
    callback_url: Optional[str] = None
    consistency_seed: Optional[int] = None

class JobStatusResponse(BaseModel):
    job_id: str
    status: JobStatus
    progress: int
    message: str
    result: Optional[dict] = None
    created_at: float
    updated_at: float

class MemoryClearanceRequest(BaseModel):
    clear_models: bool = True
    clear_jobs: bool = False
    clear_local_images: bool = False
    force_gc: bool = True

class MemoryStatusResponse(BaseModel):
    memory_used_mb: float
    memory_percent: float
    models_loaded: int
    active_jobs: int
    local_images_count: int
    gpu_memory_allocated_mb: Optional[float] = None
    gpu_memory_cached_mb: Optional[float] = None
    status: str

# HIGH-QUALITY MODEL SELECTION - ANIME FOCUSED & WORKING
MODEL_CHOICES = {
    "dreamshaper-8": "lykon/dreamshaper-8",
    "realistic-vision": "SG161222/Realistic_Vision_V5.1", 
    "counterfeit": "gsdf/Counterfeit-V2.5",
    "pastel-mix": "andite/pastel-mix",
    "meina-mix": "Meina/MeinaMix",
    "meina-pastel": "Meina/MeinaPastel", 
    "abyss-orange": "warriorxza/AbyssOrangeMix",
    "openjourney": "prompthero/openjourney",
    "sd-1.5": "runwayml/stable-diffusion-v1-5",
}

# GLOBAL STORAGE
job_storage = {}
model_cache = {}
current_model_name = None
current_pipe = None
model_lock = threading.Lock()

# MEMORY MANAGEMENT FUNCTIONS
def get_memory_usage():
    """Get current memory usage statistics"""
    process = psutil.Process()
    memory_info = process.memory_info()
    memory_used_mb = memory_info.rss / (1024 * 1024)
    memory_percent = process.memory_percent()
    
    # GPU memory if available
    gpu_memory_allocated_mb = None
    gpu_memory_cached_mb = None
    
    if torch.cuda.is_available():
        gpu_memory_allocated_mb = torch.cuda.memory_allocated() / (1024 * 1024)
        gpu_memory_cached_mb = torch.cuda.memory_reserved() / (1024 * 1024)
    
    return {
        "memory_used_mb": round(memory_used_mb, 2),
        "memory_percent": round(memory_percent, 2),
        "gpu_memory_allocated_mb": round(gpu_memory_allocated_mb, 2) if gpu_memory_allocated_mb else None,
        "gpu_memory_cached_mb": round(gpu_memory_cached_mb, 2) if gpu_memory_cached_mb else None,
        "models_loaded": len(model_cache),
        "active_jobs": len(job_storage),
        "local_images_count": len(refresh_local_images())
    }

def clear_memory(clear_models=True, clear_jobs=False, clear_local_images=False, force_gc=True):
    """Clear memory by unloading models and cleaning up resources"""
    results = []
    
    # Clear model cache
    if clear_models:
        with model_lock:
            models_cleared = len(model_cache)
            for model_name, pipe in model_cache.items():
                try:
                    # Move to CPU first if it's on GPU
                    if hasattr(pipe, 'to'):
                        pipe.to('cpu')
                    
                    # Delete the pipeline
                    del pipe
                    results.append(f"Unloaded model: {model_name}")
                except Exception as e:
                    results.append(f"Error unloading {model_name}: {str(e)}")
            
            model_cache.clear()
            global current_pipe, current_model_name
            current_pipe = None
            current_model_name = None
            results.append(f"Cleared {models_cleared} models from cache")
    
    # Clear completed jobs
    if clear_jobs:
        jobs_to_clear = []
        for job_id, job_data in job_storage.items():
            if job_data["status"] in [JobStatus.COMPLETED, JobStatus.FAILED]:
                jobs_to_clear.append(job_id)
        
        for job_id in jobs_to_clear:
            del job_storage[job_id]
            results.append(f"Cleared job: {job_id}")
        
        results.append(f"Cleared {len(jobs_to_clear)} completed/failed jobs")
    
    # Clear local images
    if clear_local_images:
        try:
            storage_info = get_local_storage_info()
            deleted_count = 0
            if "images" in storage_info:
                for image_info in storage_info["images"]:
                    success, _ = delete_local_image(image_info["path"])
                    if success:
                        deleted_count += 1
            results.append(f"Deleted {deleted_count} local images")
        except Exception as e:
            results.append(f"Error clearing local images: {str(e)}")
    
    # Force garbage collection
    if force_gc:
        gc.collect()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            torch.cuda.synchronize()
            results.append("GPU cache cleared")
        results.append("Garbage collection forced")
    
    # Get memory status after cleanup
    memory_status = get_memory_usage()
    
    return {
        "status": "success",
        "actions_performed": results,
        "memory_after_cleanup": memory_status
    }

def load_model(model_name="dreamshaper-8"):
    """Thread-safe model loading with HIGH-QUALITY settings and better error handling"""
    global model_cache, current_model_name, current_pipe
    
    with model_lock:
        if model_name in model_cache:
            current_pipe = model_cache[model_name]
            current_model_name = model_name
            return current_pipe
        
        print(f"🔄 Loading HIGH-QUALITY model: {model_name}")
        try:
            model_id = MODEL_CHOICES.get(model_name, "lykon/dreamshaper-8")
            
            print(f"🔧 Attempting to load: {model_id}")
            
            pipe = StableDiffusionPipeline.from_pretrained(
                model_id, 
                torch_dtype=torch.float32,
                safety_checker=None,
                requires_safety_checker=False,
                local_files_only=False,  # Allow downloading if not cached
                cache_dir="./model_cache"  # Specific cache directory
            )
            
            pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
            pipe = pipe.to("cpu")
            
            model_cache[model_name] = pipe
            current_pipe = pipe
            current_model_name = model_name
            
            print(f"✅ HIGH-QUALITY Model loaded: {model_name}")
            return pipe
            
        except Exception as e:
            print(f"❌ Model loading failed for {model_name}: {e}")
            print(f"🔄 Falling back to stable-diffusion-v1-5")
            
            # Fallback to base model
            try:
                pipe = StableDiffusionPipeline.from_pretrained(
                    "runwayml/stable-diffusion-v1-5", 
                    torch_dtype=torch.float32,
                    safety_checker=None,
                    requires_safety_checker=False
                ).to("cpu")
                
                model_cache[model_name] = pipe
                current_pipe = pipe
                current_model_name = "sd-1.5"
                
                print(f"✅ Fallback model loaded: stable-diffusion-v1-5")
                return pipe
                
            except Exception as fallback_error:
                print(f"❌ Critical: Fallback model also failed: {fallback_error}")
                raise

# Initialize default model
print("🚀 Initializing Storybook Generator API...")
load_model("dreamshaper-8")
print("✅ Model loaded and ready!")

# SIMPLE PROMPT ENGINEERING - USE PURE PROMPTS ONLY
def enhance_prompt_simple(scene_visual, style="childrens_book"):
    """Simple prompt enhancement - uses only the provided visual prompt with style"""
    
    # Style templates
    style_templates = {
        "childrens_book": "children's book illustration, watercolor style, soft colors, whimsical, magical, storybook art, professional illustration",
        "realistic": "photorealistic, detailed, natural lighting, professional photography",
        "fantasy": "fantasy art, magical, ethereal, digital painting, concept art",
        "anime": "anime style, Japanese animation, vibrant colors, detailed artwork"
    }
    
    style_prompt = style_templates.get(style, style_templates["childrens_book"])
    
    # Use only the provided visual prompt with style
    enhanced_prompt = f"{style_prompt}, {scene_visual}"
    
    # Basic negative prompt for quality
    negative_prompt = (
        "blurry, low quality, bad anatomy, deformed characters, "
        "wrong proportions, mismatched features"
    )
    
    return enhanced_prompt, negative_prompt

def generate_image_simple(prompt, model_choice, style, scene_number, consistency_seed=None):
    """Generate image using pure prompts only"""
    
    # Enhance prompt with simple style addition
    enhanced_prompt, negative_prompt = enhance_prompt_simple(prompt, style)
    
    # Use seed if provided
    if consistency_seed:
        scene_seed = consistency_seed + scene_number
    else:
        scene_seed = random.randint(1000, 9999)
    
    try:
        pipe = load_model(model_choice)
        
        image = pipe(
            prompt=enhanced_prompt,
            negative_prompt=negative_prompt,
            num_inference_steps=35,
            guidance_scale=7.5,
            width=768,
            height=1024,  # Portrait for better full-body
            generator=torch.Generator(device="cpu").manual_seed(scene_seed)
        ).images[0]
        
        print(f"✅ Generated image for scene {scene_number}")
        print(f"🌱 Seed used: {scene_seed}")
        print(f"📝 Pure prompt used: {prompt}")
        
        return image
        
    except Exception as e:
        print(f"❌ Generation failed: {str(e)}")
        raise

# LOCAL FILE MANAGEMENT FUNCTIONS
def save_image_to_local(image, prompt, style="test"):
    """Save image to local persistent storage"""
    try:
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        safe_prompt = "".join(c for c in prompt[:50] if c.isalnum() or c in (' ', '-', '_')).rstrip()
        filename = f"image_{safe_prompt}_{timestamp}.png"
        
        # Create style subfolder
        style_dir = os.path.join(PERSISTENT_IMAGE_DIR, style)
        os.makedirs(style_dir, exist_ok=True)
        filepath = os.path.join(style_dir, filename)
        
        # Save the image
        image.save(filepath)
        print(f"💾 Image saved locally: {filepath}")
        
        return filepath, filename
        
    except Exception as e:
        print(f"❌ Failed to save locally: {e}")
        return None, None

def delete_local_image(filepath):
    """Delete an image from local storage"""
    try:
        if os.path.exists(filepath):
            os.remove(filepath)
            print(f"🗑️ Deleted local image: {filepath}")
            return True, f"✅ Deleted: {os.path.basename(filepath)}"
        else:
            return False, f"❌ File not found: {filepath}"
    except Exception as e:
        return False, f"❌ Error deleting: {str(e)}"

def get_local_storage_info():
    """Get information about local storage usage"""
    try:
        total_size = 0
        file_count = 0
        images_list = []
        
        for root, dirs, files in os.walk(PERSISTENT_IMAGE_DIR):
            for file in files:
                if file.endswith(('.png', '.jpg', '.jpeg')):
                    filepath = os.path.join(root, file)
                    if os.path.exists(filepath):
                        file_size = os.path.getsize(filepath)
                        total_size += file_size
                        file_count += 1
                        images_list.append({
                            'path': filepath,
                            'filename': file,
                            'size_kb': round(file_size / 1024, 1),
                            'created': os.path.getctime(filepath)
                        })
        
        return {
            "total_files": file_count,
            "total_size_mb": round(total_size / (1024 * 1024), 2),
            "images": sorted(images_list, key=lambda x: x['created'], reverse=True)
        }
    except Exception as e:
        return {"error": str(e)}

def refresh_local_images():
    """Get list of all locally saved images"""
    try:
        image_files = []
        for root, dirs, files in os.walk(PERSISTENT_IMAGE_DIR):
            for file in files:
                if file.endswith(('.png', '.jpg', '.jpeg')):
                    filepath = os.path.join(root, file)
                    if os.path.exists(filepath):
                        image_files.append(filepath)
        return image_files
    except Exception as e:
        print(f"Error refreshing local images: {e}")
        return []

# OCI BUCKET FUNCTIONS
def save_to_oci_bucket(image, text_content, story_title, page_number, file_type="image"):
    """Save both images and text to OCI bucket via your OCI API with retry logic"""
    try:
        if file_type == "image":
            # Convert image to bytes
            img_bytes = io.BytesIO()
            image.save(img_bytes, format='PNG')
            file_data = img_bytes.getvalue()
            filename = f"page_{page_number:03d}.png"
            mime_type = "image/png"
        else:  # text
            file_data = text_content.encode('utf-8')
            filename = f"page_{page_number:03d}.txt"
            mime_type = "text/plain"
        
        # Use your OCI API to save the file
        api_url = f"{OCI_API_BASE_URL}/api/upload"
        
        files = {'file': (filename, file_data, mime_type)}
        data = {
            'project_id': 'storybook-library',
            'subfolder': f'stories/{story_title}'
        }
        
        # Create session with retry strategy
        session = requests.Session()
        retry_strategy = Retry(
            total=3,
            status_forcelist=[429, 500, 502, 503, 504],
            allowed_methods=["POST"],
            backoff_factor=1
        )
        adapter = HTTPAdapter(max_retries=retry_strategy)
        session.mount("http://", adapter)
        session.mount("https://", adapter)
        
        # INCREASED TIMEOUT WITH RETRY LOGIC
        response = session.post(api_url, files=files, data=data, timeout=60)
        
        print(f"📨 OCI API Response: {response.status_code}")
        
        if response.status_code == 200:
            result = response.json()
            if result['status'] == 'success':
                return result.get('file_url', 'Unknown URL')
            else:
                raise Exception(f"OCI API Error: {result.get('message', 'Unknown error')}")
        else:
            raise Exception(f"HTTP Error: {response.status_code}")
            
    except Exception as e:
        raise Exception(f"OCI upload failed: {str(e)}")

def test_oci_connection():
    """Test connection to OCI API"""
    try:
        test_url = f"{OCI_API_BASE_URL}/api/health"
        print(f"🔧 Testing connection to: {test_url}")
        
        response = requests.get(test_url, timeout=10)
        print(f"🔧 Connection test response: {response.status_code}")
        
        if response.status_code == 200:
            result = response.json()
            print(f"🔧 OCI API Health: {result}")
            return True
        else:
            print(f"🔧 OCI API not healthy: {response.status_code}")
            return False
            
    except Exception as e:
        print(f"🔧 Connection test failed: {e}")
        return False

# JOB MANAGEMENT FUNCTIONS
def create_job(story_request: StorybookRequest) -> str:
    job_id = str(uuid.uuid4())
    
    job_storage[job_id] = {
        "status": JobStatus.PENDING,
        "progress": 0,
        "message": "Job created and queued",
        "request": story_request.dict(),
        "result": None,
        "created_at": time.time(),
        "updated_at": time.time(),
        "pages": []
    }
    
    print(f"📝 Created job {job_id} for story: {story_request.story_title}")
    print(f"📄 Scenes to generate: {len(story_request.scenes)}")
    
    return job_id

def update_job_status(job_id: str, status: JobStatus, progress: int, message: str, result=None):
    if job_id not in job_storage:
        return False
    
    job_storage[job_id].update({
        "status": status,
        "progress": progress,
        "message": message,
        "updated_at": time.time()
    })
    
    if result:
        job_storage[job_id]["result"] = result
    
    # Send webhook notification if callback URL exists
    job_data = job_storage[job_id]
    request_data = job_data["request"]
    
    if request_data.get("callback_url"):
        try:
            callback_url = request_data["callback_url"]
            
            # Enhanced callback data with scene information
            callback_data = {
                "job_id": job_id,
                "status": status.value,
                "progress": progress,
                "message": message,
                "story_title": request_data["story_title"],
                "total_scenes": len(request_data["scenes"]),
                "timestamp": time.time(),
                "source": "huggingface-storybook-generator",
                "estimated_time_remaining": calculate_remaining_time(job_id, progress)
            }
            
            # Add current scene info for processing jobs
            if status == JobStatus.PROCESSING:
                # Calculate current scene based on progress
                total_scenes = len(request_data["scenes"])
                if total_scenes > 0:
                    current_scene = min((progress - 5) // (90 // total_scenes) + 1, total_scenes)
                    callback_data["current_scene"] = current_scene
                    callback_data["total_scenes"] = total_scenes
                    
                    # Add scene description if available
                    if current_scene <= len(request_data["scenes"]):
                        scene_data = request_data["scenes"][current_scene-1]
                        callback_data["scene_description"] = scene_data.get("visual", "")[:100] + "..."
                        callback_data["current_prompt"] = scene_data.get("visual", "")
            
            # Add result data for completed jobs
            if status == JobStatus.COMPLETED and result:
                callback_data["result"] = {
                    "total_pages": result.get("total_pages", 0),
                    "generation_time": result.get("generation_time", 0),
                    "oci_bucket_url": result.get("oci_bucket_url", ""),
                    "pages_generated": result.get("generated_pages", 0),
                    "consistency_seed": result.get("consistency_seed", None)
                }
            
            headers = {
                'Content-Type': 'application/json',
                'User-Agent': 'Storybook-Generator/1.0'
            }
            
            print(f"📢 Sending callback to: {callback_url}")
            print(f"📊 Callback data: {json.dumps(callback_data, indent=2)}")
            
            response = requests.post(
                callback_url,
                json=callback_data,
                headers=headers,
                timeout=30
            )
            
            print(f"📢 Callback sent: Status {response.status_code}")
                
        except Exception as e:
            print(f"⚠️ Callback failed: {str(e)}")
    
    return True

def calculate_remaining_time(job_id, progress):
    """Calculate estimated time remaining"""
    if progress == 0:
        return "Calculating..."
    
    job_data = job_storage.get(job_id)
    if not job_data:
        return "Unknown"
    
    time_elapsed = time.time() - job_data["created_at"]
    if progress > 0:
        total_estimated = (time_elapsed / progress) * 100
        remaining = total_estimated - time_elapsed
        return f"{int(remaining // 60)}m {int(remaining % 60)}s"
    
    return "Unknown"

# SIMPLE BACKGROUND TASK - USES PURE PROMPTS ONLY
def generate_storybook_background(job_id: str):
    """Background task to generate complete storybook using pure prompts only"""
    try:
        # Test OCI connection first
        print("🔧 Testing OCI API connection...")
        oci_connected = test_oci_connection()
        if not oci_connected:
            print("⚠️ OCI API connection test failed - will use local fallback")
        
        job_data = job_storage[job_id]
        story_request_data = job_data["request"]
        story_request = StorybookRequest(**story_request_data)
        
        print(f"🎬 Starting storybook generation for job {job_id}")
        print(f"📖 Story: {story_request.story_title}")
        print(f"📄 Scenes: {len(story_request.scenes)}")
        print(f"🎨 Style: {story_request.style}")
        print(f"🌱 Consistency seed: {story_request.consistency_seed}")
        
        update_job_status(job_id, JobStatus.PROCESSING, 5, "Starting storybook generation with pure prompts...")
        
        total_scenes = len(story_request.scenes)
        generated_pages = []
        start_time = time.time()
        
        for i, scene in enumerate(story_request.scenes):
            # FIXED: Better progress calculation
            progress = 5 + int(((i + 1) / total_scenes) * 90)
            
            update_job_status(
                job_id, 
                JobStatus.PROCESSING, 
                progress, 
                f"Generating page {i+1}/{total_scenes}: {scene.visual[:50]}..."
            )
            
            try:
                print(f"🖼️ Generating page {i+1}")
                print(f"📝 Pure prompt: {scene.visual}")
                
                # Generate image using pure prompt only
                image = generate_image_simple(
                    scene.visual, 
                    story_request.model_choice, 
                    story_request.style,
                    i + 1,
                    story_request.consistency_seed
                )
                
                # Save locally as backup
                local_filepath, local_filename = save_image_to_local(image, scene.visual, story_request.style)
                print(f"💾 Image saved locally as backup: {local_filename}")
                
                try:
                    # Save IMAGE to OCI bucket
                    image_url = save_to_oci_bucket(
                        image, 
                        "",  # No text for image
                        story_request.story_title, 
                        i + 1, 
                        "image"
                    )
                    
                    # Save TEXT to OCI bucket
                    text_url = save_to_oci_bucket(
                        None,  # No image for text
                        scene.text, 
                        story_request.story_title, 
                        i + 1, 
                        "text"
                    )
                    
                    # Store page data
                    page_data = {
                        "page_number": i + 1,
                        "image_url": image_url,
                        "text_url": text_url,
                        "text_content": scene.text,
                        "visual_description": scene.visual,
                        "prompt_used": scene.visual,  # Store the pure prompt
                        "local_backup_path": local_filepath
                    }
                    generated_pages.append(page_data)
                    
                    print(f"✅ Page {i+1} completed")
                    
                except Exception as upload_error:
                    # If OCI upload fails, use local file as fallback
                    error_msg = f"OCI upload failed for page {i+1}, using local backup: {str(upload_error)}"
                    print(f"⚠️ {error_msg}")
                    
                    page_data = {
                        "page_number": i + 1,
                        "image_url": f"local://{local_filepath}",
                        "text_url": f"local://text_content_{i+1}",
                        "text_content": scene.text,
                        "visual_description": scene.visual,
                        "prompt_used": scene.visual,
                        "local_backup_path": local_filepath,
                        "upload_error": str(upload_error)
                    }
                    generated_pages.append(page_data)
                    
                    # Continue with next page instead of failing completely
                    continue
                
            except Exception as e:
                error_msg = f"Failed to generate page {i+1}: {str(e)}"
                print(f"❌ {error_msg}")
                update_job_status(job_id, JobStatus.FAILED, 0, error_msg)
                return
        
        # Complete the job
        generation_time = time.time() - start_time
        
        # Count successful OCI uploads vs local fallbacks
        oci_success_count = sum(1 for page in generated_pages if not page.get("upload_error"))
        local_fallback_count = sum(1 for page in generated_pages if page.get("upload_error"))
        
        result = {
            "story_title": story_request.story_title,
            "total_pages": total_scenes,
            "generated_pages": len(generated_pages),
            "generation_time": round(generation_time, 2),
            "folder_path": f"stories/{story_request.story_title}",
            "oci_bucket_url": f"https://oci.com/stories/{story_request.story_title}",
            "consistency_seed": story_request.consistency_seed,
            "pages": generated_pages,
            "file_structure": {
                "images": [f"page_{i+1:03d}.png" for i in range(total_scenes)],
                "texts": [f"page_{i+1:03d}.txt" for i in range(total_scenes)]
            },
            "upload_summary": {
                "oci_successful": oci_success_count,
                "local_fallback": local_fallback_count,
                "total_attempted": total_scenes
            }
        }
        
        status_message = f"🎉 Storybook completed! {len(generated_pages)} pages created in {generation_time:.2f}s using pure prompts."
        if local_fallback_count > 0:
            status_message += f" {local_fallback_count} pages saved locally due to OCI upload issues."
        
        update_job_status(
            job_id, 
            JobStatus.COMPLETED, 
            100, 
            status_message,
            result
        )
        
        print(f"🎉 Storybook generation finished for job {job_id}")
        print(f"📁 OCI Uploads: {oci_success_count} successful, {local_fallback_count} local fallbacks")
        print(f"📝 All prompts used exactly as provided from Telegram")
        
    except Exception as e:
        error_msg = f"Story generation failed: {str(e)}"
        print(f"❌ {error_msg}")
        update_job_status(job_id, JobStatus.FAILED, 0, error_msg)

# FASTAPI ENDPOINTS (for n8n)
@app.post("/api/generate-storybook")
async def generate_storybook(request: dict, background_tasks: BackgroundTasks):
    """Main endpoint for n8n integration - generates complete storybook using pure prompts"""
    try:
        print(f"📥 Received n8n request for story: {request.get('story_title', 'Unknown')}")
        
        # Add consistency seed if not provided
        if 'consistency_seed' not in request or not request['consistency_seed']:
            request['consistency_seed'] = random.randint(1000, 9999)
            print(f"🌱 Generated consistency seed: {request['consistency_seed']}")
        
        # Convert to Pydantic model
        story_request = StorybookRequest(**request)
        
        # Validate required fields
        if not story_request.story_title or not story_request.scenes:
            raise HTTPException(status_code=400, detail="story_title and scenes are required")
        
        # Create job immediately
        job_id = create_job(story_request)
        
        # Start background processing
        background_tasks.add_task(generate_storybook_background, job_id)
        
        # Immediate response for n8n
        response_data = {
            "status": "success",
            "message": "Storybook generation with pure prompts started successfully",
            "job_id": job_id,
            "story_title": story_request.story_title,
            "total_scenes": len(story_request.scenes),
            "consistency_seed": story_request.consistency_seed,
            "callback_url": story_request.callback_url,
            "estimated_time_seconds": len(story_request.scenes) * 35,
            "timestamp": datetime.now().isoformat()
        }
        
        print(f"✅ Job {job_id} started with pure prompts for: {story_request.story_title}")
        
        return response_data
        
    except Exception as e:
        error_msg = f"API Error: {str(e)}"
        print(f"❌ {error_msg}")
        raise HTTPException(status_code=500, detail=error_msg)

@app.get("/api/job-status/{job_id}")
async def get_job_status_endpoint(job_id: str):
    """Check job status"""
    job_data = job_storage.get(job_id)
    if not job_data:
        raise HTTPException(status_code=404, detail="Job not found")
    
    return JobStatusResponse(
        job_id=job_id,
        status=job_data["status"],
        progress=job_data["progress"],
        message=job_data["message"],
        result=job_data["result"],
        created_at=job_data["created_at"],
        updated_at=job_data["updated_at"]
    )

@app.get("/api/health")
async def api_health():
    """Health check endpoint for n8n"""
    return {
        "status": "healthy",
        "service": "storybook-generator",
        "timestamp": datetime.now().isoformat(),
        "active_jobs": len(job_storage),
        "models_loaded": list(model_cache.keys()),
        "oci_api_connected": OCI_API_BASE_URL
    }

# NEW MEMORY MANAGEMENT ENDPOINTS
@app.get("/api/memory-status")
async def get_memory_status():
    """Get current memory usage and system status"""
    memory_info = get_memory_usage()
    return MemoryStatusResponse(
        memory_used_mb=memory_info["memory_used_mb"],
        memory_percent=memory_info["memory_percent"],
        models_loaded=memory_info["models_loaded"],
        active_jobs=memory_info["active_jobs"],
        local_images_count=memory_info["local_images_count"],
        gpu_memory_allocated_mb=memory_info["gpu_memory_allocated_mb"],
        gpu_memory_cached_mb=memory_info["gpu_memory_cached_mb"],
        status="healthy"
    )

@app.post("/api/clear-memory")
async def clear_memory_endpoint(request: MemoryClearanceRequest):
    """Clear memory by unloading models and cleaning up resources"""
    try:
        result = clear_memory(
            clear_models=request.clear_models,
            clear_jobs=request.clear_jobs,
            clear_local_images=request.clear_local_images,
            force_gc=request.force_gc
        )
        
        return {
            "status": "success",
            "message": "Memory clearance completed",
            "details": result
        }
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Memory clearance failed: {str(e)}")

@app.post("/api/auto-cleanup")
async def auto_cleanup():
    """Automatic cleanup - clears completed jobs and forces GC"""
    try:
        result = clear_memory(
            clear_models=False,  # Don't clear models by default
            clear_jobs=True,     # Clear completed jobs
            clear_local_images=False,  # Don't clear images by default
            force_gc=True        # Force garbage collection
        )
        
        return {
            "status": "success",
            "message": "Automatic cleanup completed",
            "details": result
        }
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Auto cleanup failed: {str(e)}")

@app.get("/api/local-images")
async def get_local_images():
    """API endpoint to get locally saved test images"""
    storage_info = get_local_storage_info()
    return storage_info

@app.delete("/api/local-images/{filename:path}")
async def delete_local_image_api(filename: str):
    """API endpoint to delete a local image"""
    try:
        filepath = os.path.join(PERSISTENT_IMAGE_DIR, filename)
        success, message = delete_local_image(filepath)
        return {"status": "success" if success else "error", "message": message}
    except Exception as e:
        return {"status": "error", "message": str(e)}

# SIMPLE GRADIO INTERFACE
def create_gradio_interface():
    """Create simple Gradio interface for testing"""
    
    def generate_test_image_simple(prompt, model_choice, style_choice):
        """Generate a single image using pure prompt only"""
        try:
            if not prompt.strip():
                return None, "❌ Please enter a prompt", None
            
            print(f"🎨 Generating test image with pure prompt: {prompt}")
            
            # Generate the image using pure prompt
            image = generate_image_simple(
                prompt, 
                model_choice, 
                style_choice,
                1
            )
            
            # Save to local storage
            filepath, filename = save_image_to_local(image, prompt, style_choice)
            
            status_msg = f"""✅ Success! Generated: {prompt}

📁 **Local file:** {filename if filename else 'Not saved'}"""
            
            return image, status_msg, filepath
            
        except Exception as e:
            error_msg = f"❌ Generation failed: {str(e)}"
            print(error_msg)
            return None, error_msg, None
    
    with gr.Blocks(title="Simple Image Generator", theme="soft") as demo:
        gr.Markdown("# 🎨 Simple Image Generator")
        gr.Markdown("Generate images using **pure prompts only** - no automatic enhancements")
        
        # Storage info display
        storage_info = gr.Textbox(
            label="📊 Local Storage Information",
            interactive=False,
            lines=2
        )
        
        # Memory status display
        memory_status = gr.Textbox(
            label="🧠 Memory Status",
            interactive=False,
            lines=3
        )
        
        def update_storage_info():
            info = get_local_storage_info()
            if "error" not in info:
                return f"📁 Local Storage: {info['total_files']} images, {info['total_size_mb']} MB used"
            return "📁 Local Storage: Unable to calculate"
        
        def update_memory_status():
            memory_info = get_memory_usage()
            status_text = f"🧠 Memory Usage: {memory_info['memory_used_mb']} MB ({memory_info['memory_percent']}%)\n"
            status_text += f"📦 Models Loaded: {memory_info['models_loaded']}\n"
            status_text += f"⚡ Active Jobs: {memory_info['active_jobs']}"
            
            if memory_info['gpu_memory_allocated_mb']:
                status_text += f"\n🎮 GPU Memory: {memory_info['gpu_memory_allocated_mb']} MB allocated"
            
            return status_text
        
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("### 🎯 Quality Settings")
                
                model_dropdown = gr.Dropdown(
                    label="AI Model",
                    choices=list(MODEL_CHOICES.keys()),
                    value="dreamshaper-8"
                )
                
                style_dropdown = gr.Dropdown(
                    label="Art Style",
                    choices=["childrens_book", "realistic", "fantasy", "anime"],
                    value="anime"
                )
                
                prompt_input = gr.Textbox(
                    label="Pure Prompt",
                    placeholder="Enter your exact prompt...",
                    lines=3
                )
                
                generate_btn = gr.Button("✨ Generate Image", variant="primary")
                
                # Current image management
                current_file_path = gr.State()
                delete_btn = gr.Button("🗑️ Delete This Image", variant="stop")
                delete_status = gr.Textbox(label="Delete Status", interactive=False, lines=2)
                
                # Memory management section
                gr.Markdown("### 🧠 Memory Management")
                with gr.Row():
                    auto_cleanup_btn = gr.Button("🔄 Auto Cleanup", size="sm")
                    clear_models_btn = gr.Button("🗑️ Clear Models", variant="stop", size="sm")
                
                memory_clear_status = gr.Textbox(label="Memory Clear Status", interactive=False, lines=2)
                
                gr.Markdown("### 📚 API Usage for n8n")
                gr.Markdown("""
                **For complete storybooks (OCI bucket):**
                - Endpoint: `POST /api/generate-storybook`
                - Input: `story_title`, `scenes[]`, `characters[]`
                - Output: Uses pure prompts only from your script
                
                **Memory Management APIs:**
                - `GET /api/memory-status` - Check memory usage
                - `POST /api/clear-memory` - Clear memory
                - `POST /api/auto-cleanup` - Auto cleanup jobs
                """)
            
            with gr.Column(scale=2):
                image_output = gr.Image(label="Generated Image", height=500, show_download_button=True)
                status_output = gr.Textbox(label="Status", interactive=False, lines=4)
        
        # Local file management section
        with gr.Accordion("📁 Manage Local Test Images", open=True):
            gr.Markdown("### Locally Saved Images")
            
            with gr.Row():
                refresh_btn = gr.Button("🔄 Refresh List")
                clear_all_btn = gr.Button("🗑️ Clear All Images", variant="stop")
            
            file_gallery = gr.Gallery(
                label="Local Images",
                show_label=True,
                elem_id="gallery",
                columns=4,
                height="auto"
            )
            
            clear_status = gr.Textbox(label="Clear Status", interactive=False)
        
        def delete_current_image(filepath):
            """Delete the currently displayed image"""
            if not filepath:
                return "❌ No image to delete", None, None, refresh_local_images()
            
            success, message = delete_local_image(filepath)
            updated_files = refresh_local_images()
            
            if success:
                status_msg = f"✅ {message}"
                return status_msg, None, "Image deleted successfully!", updated_files
            else:
                return f"❌ {message}", None, "Delete failed", updated_files

        def clear_all_images():
            """Delete all local images"""
            try:
                storage_info = get_local_storage_info()
                deleted_count = 0
                
                if "images" in storage_info:
                    for image_info in storage_info["images"]:
                        success, _ = delete_local_image(image_info["path"])
                        if success:
                            deleted_count += 1
                
                updated_files = refresh_local_images()
                return f"✅ Deleted {deleted_count} images", updated_files
            except Exception as e:
                return f"❌ Error: {str(e)}", refresh_local_images()
        
        def perform_auto_cleanup():
            """Perform automatic cleanup"""
            try:
                result = clear_memory(
                    clear_models=False,
                    clear_jobs=True,
                    clear_local_images=False,
                    force_gc=True
                )
                return f"✅ Auto cleanup completed: {len(result['actions_performed'])} actions"
            except Exception as e:
                return f"❌ Auto cleanup failed: {str(e)}"
        
        def clear_models():
            """Clear all loaded models"""
            try:
                result = clear_memory(
                    clear_models=True,
                    clear_jobs=False,
                    clear_local_images=False,
                    force_gc=True
                )
                return f"✅ Models cleared: {len(result['actions_performed'])} actions"
            except Exception as e:
                return f"❌ Model clearance failed: {str(e)}"

        # Connect buttons to functions
        generate_btn.click(
            fn=generate_test_image_simple,
            inputs=[prompt_input, model_dropdown, style_dropdown],
            outputs=[image_output, status_output, current_file_path]
        ).then(
            fn=refresh_local_images,
            outputs=file_gallery
        ).then(
            fn=update_storage_info,
            outputs=storage_info
        ).then(
            fn=update_memory_status,
            outputs=memory_status
        )
        
        delete_btn.click(
            fn=delete_current_image,
            inputs=current_file_path,
            outputs=[delete_status, image_output, status_output, file_gallery]
        ).then(
            fn=update_storage_info,
            outputs=storage_info
        ).then(
            fn=update_memory_status,
            outputs=memory_status
        )
        
        refresh_btn.click(
            fn=refresh_local_images,
            outputs=file_gallery
        ).then(
            fn=update_storage_info,
            outputs=storage_info
        ).then(
            fn=update_memory_status,
            outputs=memory_status
        )
        
        clear_all_btn.click(
            fn=clear_all_images,
            outputs=[clear_status, file_gallery]
        ).then(
            fn=update_storage_info,
            outputs=storage_info
        ).then(
            fn=update_memory_status,
            outputs=memory_status
        )
        
        # Memory management buttons
        auto_cleanup_btn.click(
            fn=perform_auto_cleanup,
            outputs=memory_clear_status
        ).then(
            fn=update_memory_status,
            outputs=memory_status
        )
        
        clear_models_btn.click(
            fn=clear_models,
            outputs=memory_clear_status
        ).then(
            fn=update_memory_status,
            outputs=memory_status
        )
        
        # Initialize on load
        demo.load(fn=refresh_local_images, outputs=file_gallery)
        demo.load(fn=update_storage_info, outputs=storage_info)
        demo.load(fn=update_memory_status, outputs=memory_status)
    
    return demo

# Create simple Gradio app
demo = create_gradio_interface()

# Simple root endpoint
@app.get("/")
async def root():
    return {
        "message": "Simple Storybook Generator API is running!",
        "api_endpoints": {
            "health_check": "GET /api/health",
            "generate_storybook": "POST /api/generate-storybook",
            "check_job_status": "GET /api/job-status/{job_id}",
            "local_images": "GET /api/local-images",
            "memory_status": "GET /api/memory-status",
            "clear_memory": "POST /api/clear-memory",
            "auto_cleanup": "POST /api/auto-cleanup"
        },
        "features": {
            "pure_prompts": "✅ Enabled - No automatic enhancements",
            "n8n_integration": "✅ Enabled",
            "memory_management": "✅ Enabled"
        },
        "web_interface": "GET /ui"
    }

# Add a simple test endpoint
@app.get("/api/test")
async def test_endpoint():
    return {
        "status": "success", 
        "message": "API with pure prompts is working correctly",
        "pure_prompts": "✅ Enabled - Using exact prompts from Telegram",
        "memory_management": "✅ Enabled - Memory clearance available",
        "timestamp": datetime.now().isoformat()
    }

# For Hugging Face Spaces deployment
def get_app():
    return app

if __name__ == "__main__":
    import uvicorn
    import os
    
    # Check if we're running on Hugging Face Spaces
    HF_SPACE = os.environ.get('SPACE_ID') is not None
    
    if HF_SPACE:
        print("🚀 Running on Hugging Face Spaces - Integrated Mode")
        print("📚 API endpoints available at: /api/*")
        print("🎨 Web interface available at: /ui")
        print("📝 PURE PROMPTS enabled - no automatic enhancements")
        print("🧠 MEMORY MANAGEMENT enabled - automatic cleanup available")
        
        # Mount Gradio without reassigning app
        gr.mount_gradio_app(app, demo, path="/ui")
        
        # Run the combined app
        uvicorn.run(
            app, 
            host="0.0.0.0", 
            port=7860, 
            log_level="info"
        )
    else:
        # Local development - run separate servers
        print("🚀 Running locally - Separate API and UI servers")
        print("📚 API endpoints: http://localhost:8000/api/*")
        print("🎨 Web interface: http://localhost:7860/ui")
        print("📝 PURE PROMPTS enabled - no automatic enhancements")
        print("🧠 MEMORY MANAGEMENT enabled - automatic cleanup available")
        
        def run_fastapi():
            """Run FastAPI on port 8000 for API calls"""
            uvicorn.run(
                app, 
                host="0.0.0.0", 
                port=8000, 
                log_level="info",
                access_log=False
            )
        
        def run_gradio():
            """Run Gradio on port 7860 for web interface"""
            demo.launch(server_name="0.0.0.0", server_port=7860, share=False)
        
        # Run both servers in separate threads
        import threading
        fastapi_thread = threading.Thread(target=run_fastapi, daemon=True)
        gradio_thread = threading.Thread(target=run_gradio, daemon=True)
        
        fastapi_thread.start()
        gradio_thread.start()
        
        try:
            # Keep main thread alive
            while True:
                time.sleep(1)
        except KeyboardInterrupt:
            print("🛑 Shutting down servers...")