app.py

import gradio as gr
from gradio_pdf import PDF
import numpy as np
import random
import torch
import spaces
import math
import os
import yaml
import io
import tempfile
import shutil
import uuid
import time
import json
from typing import List, Tuple, Dict, Optional
from datetime import datetime, timedelta
from pathlib import Path

from PIL import Image
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
from huggingface_hub import InferenceClient
from reportlab.lib.pagesizes import A4
from reportlab.pdfgen import canvas
from reportlab.pdfbase import pdfmetrics
from reportlab.lib.utils import ImageReader
from PyPDF2 import PdfReader, PdfWriter

# --- Style Presets Loading ---

def load_style_presets():
    """Load style presets from YAML file."""
    try:
        with open('style_presets.yaml', 'r') as f:
            data = yaml.safe_load(f)
        # Filter only enabled presets
        presets = {k: v for k, v in data['presets'].items() if v.get('enabled', True)}
        return presets
    except Exception as e:
        print(f"Error loading style presets: {e}")
        return {"no_style": {"id": "no_style", "label": "No style (custom)", "prompt_prefix": "", "prompt_suffix": "", "negative_prompt": ""}}

# Load presets at startup
STYLE_PRESETS = load_style_presets()

# --- Page Layouts Loading ---

def load_page_layouts():
    """Load page layouts from YAML file."""
    try:
        with open('page_layouts.yaml', 'r') as f:
            data = yaml.safe_load(f)
        return data['layouts']
    except Exception as e:
        print(f"Error loading page layouts: {e}")
        # Fallback to basic layouts
        return {
            1: [{"id": "full_page", "label": "Full Page", "positions": [[0.05, 0.05, 0.9, 0.9]]}],
            2: [{"id": "horizontal_split", "label": "Horizontal Split", "positions": [[0.05, 0.05, 0.425, 0.9], [0.525, 0.05, 0.425, 0.9]]}],
            3: [{"id": "grid", "label": "Grid", "positions": [[0.05, 0.05, 0.283, 0.5], [0.358, 0.05, 0.283, 0.5], [0.666, 0.05, 0.283, 0.5]]}],
            4: [{"id": "grid_2x2", "label": "2x2 Grid", "positions": [[0.05, 0.05, 0.425, 0.425], [0.525, 0.05, 0.425, 0.425], [0.05, 0.525, 0.425, 0.425], [0.525, 0.525, 0.425, 0.425]]}]
        }

# Load layouts at startup
PAGE_LAYOUTS = load_page_layouts()

def get_layout_choices(num_images: int) -> List[Tuple[str, str]]:
    """Get available layout choices for a given number of images."""
    key = f"{num_images}_image" if num_images == 1 else f"{num_images}_images"
    if key in PAGE_LAYOUTS:
        return [(layout["label"], layout["id"]) for layout in PAGE_LAYOUTS[key]]
    # Return empty list if no layouts found (shouldn't happen with our config)
    return [("Default", "default")]

def get_random_style_preset():
    """Get a random style preset (excluding 'no_style' and 'random')."""
    eligible_keys = [k for k in STYLE_PRESETS.keys() if k not in ['no_style', 'random']]
    if eligible_keys:
        return random.choice(eligible_keys)
    return 'no_style'

def apply_style_preset(prompt, style_preset_key, custom_style_text=""):
    """
    Apply style preset to the prompt.

    Args:
        prompt: The user's base prompt
        style_preset_key: The key of the selected style preset
        custom_style_text: Custom style text when 'no_style' is selected

    Returns:
        tuple: (styled_prompt, negative_prompt)
    """
    if style_preset_key == 'no_style':
        # Use custom style text if provided
        if custom_style_text and custom_style_text.strip():
            styled_prompt = f"{custom_style_text}, {prompt}"
        else:
            styled_prompt = prompt
        return styled_prompt, ""

    if style_preset_key == 'random':
        # Select a random style
        style_preset_key = get_random_style_preset()

    if style_preset_key in STYLE_PRESETS:
        preset = STYLE_PRESETS[style_preset_key]
        prefix = preset.get('prompt_prefix', '')
        suffix = preset.get('prompt_suffix', '')
        negative = preset.get('negative_prompt', '')

        # Build the styled prompt
        parts = []
        if prefix:
            parts.append(prefix)
        parts.append(prompt)
        if suffix:
            parts.append(suffix)

        styled_prompt = ', '.join(parts)
        return styled_prompt, negative

    # Fallback to original prompt if preset not found
    return prompt, ""

# --- Story Generation using Hugging Face InferenceClient ---

def generate_story_scenes(story_prompt, num_scenes, style_context=""):
    """
    Generates a sequence of scene descriptions with captions and dialogues.

    Args:
        story_prompt: The user's story prompt
        num_scenes: Number of scenes to generate
        style_context: Optional style context to consider

    Returns:
        List of dicts with 'caption' and 'dialogue' keys
    """
    # Ensure HF_TOKEN is set
    api_key = os.environ.get("HF_TOKEN")
    if not api_key:
        print("HF_TOKEN not set, using fallback scene generation")
        # Simple fallback - just split the prompt into scenes
        fallback_scenes = []
        for i in range(num_scenes):
            fallback_scenes.append({
                "caption": f"{story_prompt} (scene {i+1} of {num_scenes})",
                "dialogue": ""
            })
        return fallback_scenes

    # Initialize the client
    client = InferenceClient(
        provider="cerebras",
        api_key=api_key,
    )

    # Create system prompt for story generation
    system_prompt = f"""You are a comic book story writer. Generate exactly {num_scenes} scenes for a comic page based on the user's story prompt.

IMPORTANT INSTRUCTIONS:
1. Output ONLY a YAML list with exactly {num_scenes} items
2. Each item must have exactly two fields:
   - caption: A detailed visual description of the scene (describe characters, clothing, location, action, expressions)
   - dialogue: Natural language description of what the character says/exclaims/shouts (can be empty string if no dialogue)
3. For captions: Be very descriptive. Repeat character descriptions in each scene (appearance, clothes, etc.)
4. For dialogue: Write it as a natural language action that will be added to the scene description
   - Format: "The [character] says: [what they say]" or "The [character] exclaims: [what they exclaim]"
   - DO NOT include character names in the dialogue text itself
   - Use verbs like: says, exclaims, shouts, whispers, asks, replies, thinks
5. Keep continuity between scenes to tell a coherent story
6. Make each scene visually distinct but connected to the narrative

Example output format:
- caption: "A young woman with long red hair wearing a blue detective coat stands in a dark alley, holding a magnifying glass up to examine mysterious glowing footprints on the wet pavement"
  dialogue: "The detective exclaims: These tracks aren't human!"
- caption: "The same red-haired woman in the blue coat backs away in shock as a massive shark fin emerges from a puddle in the alley, water splashing everywhere"
  dialogue: "The detective shouts: OH NO, SHARKS IN THE CITY!"
- caption: "The red-haired detective in blue coat runs down the alley, looking back over her shoulder at the shark fin pursuing her through the puddles"
  dialogue: "The detective thinks to herself: I need to warn everyone!"

Generate exactly {num_scenes} scenes. Output ONLY the YAML list, no other text."""

    # Format the messages
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": f"Create {num_scenes} comic scenes for this story: {story_prompt}"}
    ]

    try:
        # Call the API
        completion = client.chat.completions.create(
            model="Qwen/Qwen3-235B-A22B-Instruct-2507",
            messages=messages,
            temperature=0.7,
            max_tokens=2000,
        )
        response = completion.choices[0].message.content

        # Parse the YAML response
        scenes = parse_yaml_scenes(response, num_scenes)
        return scenes

    except Exception as e:
        print(f"Error during story generation: {e}")
        # Fallback to simple scene splitting
        fallback_scenes = []
        for i in range(num_scenes):
            fallback_scenes.append({
                "caption": f"{story_prompt} (part {i+1} of {num_scenes})",
                "dialogue": ""
            })
        return fallback_scenes

def parse_yaml_scenes(yaml_text, expected_count):
    """
    Parse YAML text to extract scene captions and dialogues.
    """
    try:
        # Clean up the text - remove markdown code blocks if present
        yaml_text = yaml_text.strip()
        if yaml_text.startswith("```yaml"):
            yaml_text = yaml_text[7:]
        if yaml_text.startswith("```"):
            yaml_text = yaml_text[3:]
        if yaml_text.endswith("```"):
            yaml_text = yaml_text[:-3]

        # Parse YAML
        scenes = yaml.safe_load(yaml_text)

        if not isinstance(scenes, list):
            raise ValueError("Expected a list of scenes")

        # Validate and clean scenes
        valid_scenes = []
        for scene in scenes:
            if isinstance(scene, dict) and 'caption' in scene:
                valid_scenes.append({
                    'caption': str(scene.get('caption', '')),
                    'dialogue': str(scene.get('dialogue', ''))
                })

        # Ensure we have the expected number of scenes
        while len(valid_scenes) < expected_count:
            valid_scenes.append({
                'caption': 'continuation of the story',
                'dialogue': ''
            })

        return valid_scenes[:expected_count]

    except Exception as e:
        print(f"Error parsing YAML scenes: {e}")
        # Return fallback scenes
        return [{'caption': 'scene description', 'dialogue': ''} for _ in range(expected_count)]


def get_caption_language(prompt):
    """Detects if the prompt contains Chinese characters."""
    ranges = [
        ('\u4e00', '\u9fff'),  # CJK Unified Ideographs
    ]
    for char in prompt:
        if any(start <= char <= end for start, end in ranges):
            return 'zh'
    return 'en'


# --- Model Loading ---
# Use the new lightning-fast model setup
ckpt_id = "Qwen/Qwen-Image"

# Scheduler configuration from the Qwen-Image-Lightning repository
scheduler_config = {
    "base_image_seq_len": 256,
    "base_shift": math.log(3),
    "invert_sigmas": False,
    "max_image_seq_len": 8192,
    "max_shift": math.log(3),
    "num_train_timesteps": 1000,
    "shift": 1.0,
    "shift_terminal": None,
    "stochastic_sampling": False,
    "time_shift_type": "exponential",
    "use_beta_sigmas": False,
    "use_dynamic_shifting": True,
    "use_exponential_sigmas": False,
    "use_karras_sigmas": False,
}

scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
pipe = DiffusionPipeline.from_pretrained(
    ckpt_id, scheduler=scheduler, torch_dtype=torch.bfloat16
).to("cuda")

# Load LoRA weights for acceleration
pipe.load_lora_weights(
    "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.1.safetensors"
)
pipe.fuse_lora()
#pipe.unload_lora_weights()

#pipe.load_lora_weights("flymy-ai/qwen-image-realism-lora")
#pipe.fuse_lora()
#pipe.unload_lora_weights()


# --- UI Constants and Helpers ---
MAX_SEED = np.iinfo(np.int32).max

def get_image_size_for_position(position_data, image_index, num_images):
    """Determines optimal image size based on its position in the layout.

    Args:
        position_data: Layout position data [x, y, width, height] in relative units
        image_index: Index of the current image (0-based)
        num_images: Total number of images in the layout

    Returns:
        tuple: (width, height) optimized for the position's aspect ratio, max 1024 in any dimension
    """
    if not position_data:
        return 1024, 1024  # Default square

    x_rel, y_rel, w_rel, h_rel = position_data
    aspect_ratio = w_rel / h_rel if h_rel > 0 else 1.0

    # Max dimension is 1024
    max_dim = 1024

    # Calculate dimensions maintaining aspect ratio with max of 1024
    if aspect_ratio >= 1:  # Wider than tall
        width = max_dim
        height = int(max_dim / aspect_ratio)
        # Ensure height is at least 256 for quality
        if height < 256:
            height = 256
            width = int(256 * aspect_ratio)
    else:  # Taller than wide
        height = max_dim
        width = int(max_dim * aspect_ratio)
        # Ensure width is at least 256 for quality
        if width < 256:
            width = 256
            height = int(256 / aspect_ratio)

    # Round to nearest 64 for better compatibility
    width = (width // 64) * 64
    height = (height // 64) * 64

    # Ensure we don't exceed max_dim after rounding
    if width > max_dim:
        width = max_dim
    if height > max_dim:
        height = max_dim

    # Minimum size check
    width = max(width, 256)
    height = max(height, 256)

    return width, height

def get_layout_position_for_image(layout_id, num_images, image_index):
    """Get the position data for a specific image in a layout.

    Args:
        layout_id: ID of the selected layout
        num_images: Total number of images
        image_index: Index of the current image (0-based)

    Returns:
        Position data [x, y, width, height] or None
    """
    key = f"{num_images}_image" if num_images == 1 else f"{num_images}_images"
    layouts = PAGE_LAYOUTS.get(key, [])
    layout = next((l for l in layouts if l["id"] == layout_id), None)

    if layout and "positions" in layout:
        positions = layout["positions"]
        if image_index < len(positions):
            return positions[image_index]

    # Fallback positions for each number of images
    fallback_positions = {
        1: [[0.05, 0.05, 0.9, 0.9]],
        2: [[0.05, 0.05, 0.425, 0.9], [0.525, 0.05, 0.425, 0.9]],
        3: [[0.05, 0.25, 0.283, 0.5], [0.358, 0.25, 0.283, 0.5], [0.666, 0.25, 0.283, 0.5]],
        4: [[0.05, 0.05, 0.425, 0.425], [0.525, 0.05, 0.425, 0.425],
            [0.05, 0.525, 0.425, 0.425], [0.525, 0.525, 0.425, 0.425]],
        5: [[0.05, 0.05, 0.9, 0.3], [0.05, 0.4, 0.283, 0.55], [0.358, 0.4, 0.283, 0.55],
            [0.666, 0.4, 0.283, 0.275], [0.666, 0.7, 0.283, 0.275]],
        6: [[0.05, 0.05, 0.425, 0.283], [0.525, 0.05, 0.425, 0.283],
            [0.05, 0.358, 0.425, 0.283], [0.525, 0.358, 0.425, 0.283],
            [0.05, 0.666, 0.425, 0.283], [0.525, 0.666, 0.425, 0.283]]
    }

    positions = fallback_positions.get(num_images, fallback_positions[1])
    if image_index < len(positions):
        return positions[image_index]
    return [0.05, 0.05, 0.9, 0.9]  # Ultimate default

# --- Session Management Functions ---

class SessionManager:
    """Manages user session data and temporary file storage."""

    def __init__(self, session_id: str = None):
        self.session_id = session_id or str(uuid.uuid4())
        self.base_dir = Path(tempfile.gettempdir()) / "gradio_comic_sessions"
        self.session_dir = self.base_dir / self.session_id
        self.session_dir.mkdir(parents=True, exist_ok=True)
        self.metadata_file = self.session_dir / "metadata.json"
        self.pdf_path = self.session_dir / "comic.pdf"
        self.load_or_create_metadata()

    def load_or_create_metadata(self):
        """Load existing metadata or create new."""
        if self.metadata_file.exists():
            with open(self.metadata_file, 'r') as f:
                self.metadata = json.load(f)
        else:
            self.metadata = {
                "created_at": datetime.now().isoformat(),
                "pages": [],
                "total_pages": 0
            }
            self.save_metadata()

    def save_metadata(self):
        """Save metadata to file."""
        with open(self.metadata_file, 'w') as f:
            json.dump(self.metadata, f, indent=2)

    def add_page(self, images: List[Image.Image], layout_id: str, seeds: List[int]):
        """Add a new page to the session."""
        page_num = self.metadata["total_pages"] + 1
        page_dir = self.session_dir / f"page_{page_num}"
        page_dir.mkdir(exist_ok=True)

        # Save images
        image_paths = []
        for i, img in enumerate(images):
            img_path = page_dir / f"image_{i+1}.jpg"
            img.save(img_path, 'JPEG', quality=95)
            image_paths.append(str(img_path))

        # Update metadata
        self.metadata["pages"].append({
            "page_num": page_num,
            "layout_id": layout_id,
            "num_images": len(images),
            "image_paths": image_paths,
            "seeds": seeds,
            "created_at": datetime.now().isoformat()
        })
        self.metadata["total_pages"] = page_num
        self.save_metadata()

        return page_num

    def get_all_pages_images(self) -> List[Tuple[List[Image.Image], str, int]]:
        """Get all images from all pages."""
        pages_data = []
        for page in self.metadata["pages"]:
            images = []
            for img_path in page["image_paths"]:
                if Path(img_path).exists():
                    images.append(Image.open(img_path))
            if images:
                pages_data.append((images, page["layout_id"], page["num_images"]))
        return pages_data

    def cleanup_old_sessions(self, max_age_hours: int = 24):
        """Clean up sessions older than max_age_hours."""
        if not self.base_dir.exists():
            return

        cutoff_time = datetime.now() - timedelta(hours=max_age_hours)

        for session_dir in self.base_dir.iterdir():
            if session_dir.is_dir():
                metadata_file = session_dir / "metadata.json"
                if metadata_file.exists():
                    try:
                        with open(metadata_file, 'r') as f:
                            metadata = json.load(f)
                        created_at = datetime.fromisoformat(metadata["created_at"])
                        if created_at < cutoff_time:
                            shutil.rmtree(session_dir)
                            print(f"Cleaned up old session: {session_dir.name}")
                    except Exception as e:
                        print(f"Error cleaning session {session_dir.name}: {e}")

# --- PDF Generation Functions ---

def create_single_page_pdf(images: List[Image.Image], layout_id: str, num_images: int) -> bytes:
    """
    Create a single PDF page with images arranged according to the selected layout.

    Args:
        images: List of PIL images
        layout_id: ID of the selected layout
        num_images: Number of images to include

    Returns:
        PDF page as bytes
    """
    # Create a bytes buffer for the PDF
    pdf_buffer = io.BytesIO()

    # Create canvas with A4 size
    pdf = canvas.Canvas(pdf_buffer, pagesize=A4)
    page_width, page_height = A4

    # Get the layout configuration
    key = f"{num_images}_image" if num_images == 1 else f"{num_images}_images"
    layouts = PAGE_LAYOUTS.get(key, [])
    layout = next((l for l in layouts if l["id"] == layout_id), None)

    if not layout:
        # Fallback to default grid layout with proper spacing
        if num_images == 1:
            positions = [[0.02, 0.02, 0.96, 0.96]]
        elif num_images == 2:
            # Horizontal split with gap
            positions = [[0.02, 0.02, 0.47, 0.96], [0.51, 0.02, 0.47, 0.96]]
        elif num_images == 3:
            # Three horizontal panels with gaps
            positions = [[0.02, 0.2, 0.31, 0.6], [0.345, 0.2, 0.31, 0.6], [0.67, 0.2, 0.31, 0.6]]
        elif num_images == 4:
            # 2x2 grid with gaps
            positions = [[0.02, 0.02, 0.47, 0.47], [0.51, 0.02, 0.47, 0.47],
                        [0.02, 0.51, 0.47, 0.47], [0.51, 0.51, 0.47, 0.47]]
        elif num_images == 5:
            # Hero top with 4 small panels below
            positions = [[0.02, 0.02, 0.96, 0.44], [0.02, 0.48, 0.31, 0.5], [0.345, 0.48, 0.31, 0.5],
                        [0.67, 0.48, 0.31, 0.24], [0.67, 0.74, 0.31, 0.24]]
        elif num_images == 6:
            # 2x3 grid with gaps
            positions = [[0.02, 0.02, 0.47, 0.31], [0.51, 0.02, 0.47, 0.31],
                        [0.02, 0.345, 0.47, 0.31], [0.51, 0.345, 0.47, 0.31],
                        [0.02, 0.67, 0.47, 0.31], [0.51, 0.67, 0.47, 0.31]]
        else:
            # For more than 6, create a simple grid
            positions = [[0.02, 0.02, 0.96, 0.96]]
    else:
        positions = layout["positions"]

    # Draw each image according to the layout
    for i, (image, pos) in enumerate(zip(images[:num_images], positions)):
        if i >= len(images):
            break

        x_rel, y_rel, w_rel, h_rel = pos

        # Add small padding between panels (1% of page dimensions)
        padding = 0.01

        # Don't scale up - use the positions as defined in the layout
        # This prevents overlapping when there are multiple images

        # Apply padding to prevent images from touching edges
        if x_rel < padding:
            x_rel = padding
        if y_rel < padding:
            y_rel = padding
        if x_rel + w_rel > 1 - padding:
            w_rel = 1 - padding - x_rel
        if y_rel + h_rel > 1 - padding:
            h_rel = 1 - padding - y_rel

        # Convert relative positions to absolute positions
        # Note: In ReportLab, y=0 is at the bottom
        x = x_rel * page_width
        y = (1 - y_rel - h_rel) * page_height  # Flip Y coordinate
        width = w_rel * page_width
        height = h_rel * page_height

        # Calculate image aspect ratio and layout aspect ratio
        img_aspect = image.width / image.height
        layout_aspect = width / height

        # Preserve aspect ratio while fitting in the allocated space
        if img_aspect > layout_aspect:
            # Image is wider than the layout space
            new_height = width / img_aspect
            y_offset = (height - new_height) / 2
            actual_width = width
            actual_height = new_height
            actual_x = x
            actual_y = y + y_offset
        else:
            # Image is taller than the layout space
            new_width = height * img_aspect
            x_offset = (width - new_width) / 2
            actual_width = new_width
            actual_height = height
            actual_x = x + x_offset
            actual_y = y

        # Convert PIL image to format suitable for ReportLab
        img_buffer = io.BytesIO()
        # Save with good quality
        image.save(img_buffer, format='JPEG', quality=95)
        img_buffer.seek(0)

        # Draw the image on the PDF preserving aspect ratio
        pdf.drawImage(ImageReader(img_buffer), actual_x, actual_y,
                     width=actual_width, height=actual_height,
                     preserveAspectRatio=True, mask='auto')

    # Save the PDF
    pdf.save()

    # Get the PDF bytes
    pdf_buffer.seek(0)
    pdf_bytes = pdf_buffer.read()

    return pdf_bytes

def create_multi_page_pdf(session_manager: SessionManager) -> str:
    """
    Create a multi-page PDF from all pages in the session.

    Args:
        session_manager: SessionManager instance with page data

    Returns:
        Path to the created PDF file
    """
    pages_data = session_manager.get_all_pages_images()

    if not pages_data:
        return None

    # Create PDF writer
    pdf_writer = PdfWriter()

    # Create each page
    for images, layout_id, num_images in pages_data:
        page_pdf_bytes = create_single_page_pdf(images, layout_id, num_images)

        # Read the single page PDF
        page_pdf_reader = PdfReader(io.BytesIO(page_pdf_bytes))

        # Add the page to the writer
        for page in page_pdf_reader.pages:
            pdf_writer.add_page(page)

    # Write to file
    pdf_path = session_manager.pdf_path
    with open(pdf_path, 'wb') as f:
        pdf_writer.write(f)

    return str(pdf_path)

# --- Main Inference Function (with session support) ---
@spaces.GPU(duration=180)  # Increased duration for up to 6 images
def infer_page(
    prompt,
    guidance_scale=1.0,
    num_inference_steps=8,
    style_preset="no_style",
    custom_style_text="",
    num_images=1,
    layout="default",
    session_state=None,
    progress=gr.Progress(track_tqdm=True),
):
    """
    Generates images for a new page and adds them to the PDF.

    Args:
        prompt (str): The text prompt to generate images from.
        guidance_scale (float): Corresponds to `true_cfg_scale`.
        num_inference_steps (int): The number of denoising steps.
        style_preset (str): The key of the style preset to apply.
        custom_style_text (str): Custom style text when 'no_style' is selected.
        num_images (int): Number of images to generate (1-6).
        layout (str): The layout ID for arranging images in the PDF.
        session_state: Current session state dictionary.
        progress (gr.Progress): A Gradio Progress object to track generation.

    Returns:
        tuple: Updated session state, PDF path, and updated button label.
    """
    # Initialize or retrieve session
    if session_state is None or "session_id" not in session_state:
        session_state = {"session_id": str(uuid.uuid4()), "page_count": 0}

    session_manager = SessionManager(session_state["session_id"])

    # Clean up old sessions periodically
    if random.random() < 0.1:  # 10% chance to cleanup on each request
        session_manager.cleanup_old_sessions()

    # Check page limit
    if session_manager.metadata["total_pages"] >= 128:
        return session_state, None, None, f"Page limit reached"

    generated_images = []
    used_seeds = []

    # Generate story scenes
    progress(0, f"Generating story with {num_images} scenes...")
    scenes = generate_story_scenes(prompt, int(num_images), style_preset)

    # Generate the requested number of images
    for i in range(int(num_images)):
        progress((i + 0.5) / num_images, f"Generating image {i+1} of {num_images} for page {session_manager.metadata['total_pages'] + 1}")

        current_seed = random.randint(0, MAX_SEED)  # Always randomize seed

        # Get optimal aspect ratio based on position in layout
        position_data = get_layout_position_for_image(layout, int(num_images), i)

        # Use scene caption and dialogue for this image
        scene_prompt = scenes[i]['caption']
        scene_dialogue = scenes[i]['dialogue']

        # Generate single image with automatic aspect ratio
        image, used_seed = infer_single_auto(
            prompt=scene_prompt,
            seed=current_seed,
            randomize_seed=False,  # We handle randomization here
            position_data=position_data,
            image_index=i,
            num_images=int(num_images),
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            dialogue=scene_dialogue,  # Pass dialogue separately
            style_preset=style_preset,
            custom_style_text=custom_style_text,
        )

        generated_images.append(image)
        used_seeds.append(used_seed)

    # Add page to session
    progress(0.8, "Adding page to document...")
    page_num = session_manager.add_page(generated_images, layout, used_seeds)

    # Create multi-page PDF
    progress(0.9, "Creating PDF...")
    pdf_path = create_multi_page_pdf(session_manager)

    progress(1.0, "Done!")

    # Update session state
    session_state["page_count"] = page_num

    # Next button label
    next_page_num = page_num + 1
    button_label = f"Generate page {next_page_num}" if next_page_num <= 128 else "Page limit reached"

    return session_state, pdf_path, pdf_path, button_label

# New inference function with automatic aspect ratio
def infer_single_auto(
    prompt,
    seed=42,
    randomize_seed=False,
    position_data=None,
    image_index=0,
    num_images=1,
    guidance_scale=1.0,
    num_inference_steps=8,
    dialogue="",  # New parameter for dialogue
    style_preset="no_style",
    custom_style_text="",
):
    """
    Generates an image with automatically determined aspect ratio based on layout position.
    """
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    # Automatically determine image size based on position
    width, height = get_image_size_for_position(position_data, image_index, num_images)

    # Set up the generator for reproducibility
    generator = torch.Generator(device="cuda").manual_seed(seed)

    print(f"Original prompt: '{prompt}'")
    print(f"Style preset: '{style_preset}'")
    print(f"Auto-selected size based on layout: {width}x{height}")

    # Apply style preset first
    styled_prompt, style_negative_prompt = apply_style_preset(prompt, style_preset, custom_style_text)

    # Add dialogue to the prompt if present
    if dialogue and dialogue.strip():
        # Simply append the dialogue as it's already properly formatted from the LLM
        styled_prompt = f"{styled_prompt}. {dialogue.strip()}"

    # Use style negative prompt if available, otherwise default
    negative_prompt = style_negative_prompt if style_negative_prompt else " "

    print(f"Final Prompt: '{styled_prompt}'")
    print(f"Negative Prompt: '{negative_prompt}'")
    print(f"Seed: {seed}, Size: {width}x{height}, Steps: {num_inference_steps}, True CFG Scale: {guidance_scale}")

    # Generate the image
    image = pipe(
        prompt=styled_prompt,
        negative_prompt=negative_prompt,
        width=width,
        height=height,
        num_inference_steps=num_inference_steps,
        generator=generator,
        true_cfg_scale=guidance_scale, # Use true_cfg_scale for this model
    ).images[0]

    # Convert to grayscale if using manga_no_color style
    if style_preset == "manga_no_color":
        # Convert to grayscale while preserving quality
        image = image.convert('L').convert('RGB')

    return image, seed

# Keep the old infer function for backward compatibility (simplified)
infer = infer_single_auto

# --- Examples and UI Layout ---
examples = [
        "A capybara wearing a suit holding a sign that reads Hello World",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 1024px;
}
#logo-title {
    text-align: center;
}
#logo-title img {
    width: 400px;
}
"""

with gr.Blocks(css=css) as demo:
    # Session state
    session_state = gr.State(value={"session_id": str(uuid.uuid4()), "page_count": 0})

    with gr.Column(elem_id="col-container"):
        gr.HTML("""
        <div id="logo-title">
            <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_logo.png" alt="Qwen-Image Logo" width="400" style="display: block; margin: 0 auto;">
            <h2 style="font-style: italic;color: #5b47d1;margin-top: -33px !important;margin-left: 133px;">AiComicFactory-GradioEdition</h2>
        </div>
        """)
        gr.Markdown("This demo uses [Qwen-Image-Lightning](https://huggingface.co/lightx2v/Qwen-Image-Lightning). Hugigng Face PRO users can perform more generations.")

        # First row: prompt input, generate button, reset button
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                placeholder="Enter your prompt",
                container=False,
                scale=4
            )
            run_button = gr.Button("Generate page 1", variant="primary", scale=1)
            reset_button = gr.Button("Reset", variant="secondary", scale=1)

        # Second row: 1/3 controls on left, 2/3 PDF preview on right
        with gr.Row():
            # Left column (1/3) - Controls
            with gr.Column(scale=1):
                # Create dropdown choices from loaded presets
                style_choices = [(preset["label"], key) for key, preset in STYLE_PRESETS.items()]
                style_preset = gr.Dropdown(
                    label="Style Preset",
                    choices=style_choices,
                    value="no_style",
                    interactive=True
                )

                custom_style_text = gr.Textbox(
                    label="Custom Style Text",
                    placeholder="Enter custom style (e.g., 'oil painting')",
                    visible=False,
                    lines=1
                )

                # Number of images slider
                num_images_slider = gr.Slider(
                    label="Images per page",
                    minimum=1,
                    maximum=6,
                    step=1,
                    value=1,
                    info="Number of images to generate for the PDF (1-6)"
                )

                # Page layout dropdown
                layout_dropdown = gr.Dropdown(
                    label="Page Layout",
                    choices=[("Full Page", "full_page")],
                    value="full_page",
                    interactive=True,
                    info="How images are arranged on the page"
                )

                # Advanced settings accordion
                with gr.Accordion("Advanced Settings", open=False):
                    guidance_scale = gr.Slider(
                        label="Guidance scale (True CFG Scale)",
                        minimum=1.0,
                        maximum=5.0,
                        step=0.1,
                        value=1.0,
                    )

                    num_inference_steps = gr.Slider(
                        label="Number of inference steps",
                        minimum=4,
                        maximum=28,
                        step=1,
                        value=8,
                    )

                # Download link
                pdf_output = gr.File(label="Download PDF", show_label=True, elem_id="pdf-download")

                gr.Markdown("""**Note:** Your images and PDF are saved for up to 24 hours.
                You can continue adding pages (up to 128) by clicking the generate button.""")

                # Examples section in the left column
                with gr.Accordion("Examples", open=True):
                    styled_examples = [
                        ["A capybara wearing a suit holding a sign that reads Hello World", "no_style", "", 1],
                        ["sharks raining down on san francisco", "anime", "", 2],
                        ["A beautiful landscape with mountains and a lake", "watercolor", "", 3],
                        ["A knight fighting a dragon", "medieval", "", 4],
                        ["Space battle with laser beams", "sci-fi", "", 5],
                        ["Detective investigating a mystery", "noir", "", 6],
                    ]

                    gr.Examples(
                        examples=styled_examples,
                        inputs=[prompt, style_preset, custom_style_text, num_images_slider],
                        outputs=None,  # Don't show outputs for examples
                        fn=None,
                        cache_examples=False
                    )

            # Right column (2/3) - PDF Preview
            with gr.Column(scale=2):
                pdf_preview = PDF(label="PDF Preview", show_label=True, height=700, elem_id="pdf-preview")

        # Add interaction to show/hide custom style text field
        def toggle_custom_style(style_value):
            return gr.update(visible=(style_value == "no_style"))

        style_preset.change(
            fn=toggle_custom_style,
            inputs=[style_preset],
            outputs=[custom_style_text]
        )

        # Update layout dropdown when number of images changes
        def update_layout_choices(num_images):
            choices = get_layout_choices(int(num_images))
            return gr.update(choices=choices, value=choices[0][1] if choices else "default")

        num_images_slider.change(
            fn=update_layout_choices,
            inputs=[num_images_slider],
            outputs=[layout_dropdown]
        )

    # Define the main generation event
    generation_event = gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer_page,
        inputs=[
            prompt,
            guidance_scale,
            num_inference_steps,
            style_preset,
            custom_style_text,
            num_images_slider,
            layout_dropdown,
            session_state,
        ],
        outputs=[session_state, pdf_output, pdf_preview, run_button],
    )

    # Reset button functionality
    def reset_session():
        new_state = {"session_id": str(uuid.uuid4()), "page_count": 0}
        return new_state, None, None, "Generate page 1"

    # Connect the reset button
    reset_button.click(
        fn=reset_session,
        inputs=[],
        outputs=[session_state, pdf_output, pdf_preview, run_button]
    )

if __name__ == "__main__":
    demo.launch(mcp_server=True)