app.py

import gradio as gr
import torch
from PIL import Image, ImageDraw, ImageFont
from diffusers import QwenImageEditPlusPipeline
import os

# Load the pipeline with CPU optimizations
model_id = "unsloth/Qwen-Image-Edit-2511-GGUF"
device = "cpu"  # Force CPU usage
print(f"Loading pipeline on {device}...")

try:
    pipeline = QwenImageEditPlusPipeline.from_pretrained(
        model_id,
        torch_dtype=torch.float32,  # Use float32 for CPU
        low_cpu_mem_usage=True,  # Reduce memory usage
        device_map="cpu"  # Explicitly map to CPU
    )
    pipeline.to(device)
    
    # Enable CPU optimizations
    pipeline.enable_attention_slicing()
    
    print("Pipeline loaded successfully on CPU.")
except Exception as e:
    print(f"Error loading pipeline: {e}")
    pipeline = None

def create_default_background():
    """Create a simple notebook background if assets aren't available"""
    width, height = 800, 1000
    img = Image.new('RGB', (width, height), color='#f5f5dc')  # Beige color
    draw = ImageDraw.Draw(img)
    
    # Draw horizontal lines
    for y in range(100, height, 40):
        draw.line([(50, y), (width - 50, y)], fill='#add8e6', width=1)
    
    # Draw left margin
    draw.line([(80, 0), (80, height)], fill='#ff69b4', width=2)
    
    return img

def generate_notes_image(notes, background_image=None):
    if pipeline is None:
        return None, "Error: Pipeline not loaded. Please check the model availability."
    
    if not notes:
        return None, "Please enter some notes."
    
    # Use default background if none provided
    if background_image is None:
        if os.path.exists("assets/notebook_blank.png"):
            background_image = Image.open("assets/notebook_blank.png")
        else:
            background_image = create_default_background()
    
    # Ensure image is RGB
    if background_image.mode != 'RGB':
        background_image = background_image.convert('RGB')
    
    # Resize if too large (to reduce memory usage on CPU)
    max_size = 768
    if max(background_image.size) > max_size:
        ratio = max_size / max(background_image.size)
        new_size = tuple(int(dim * ratio) for dim in background_image.size)
        background_image = background_image.resize(new_size, Image.Resampling.LANCZOS)
    
    # Construct the prompt
    prompt = f"Write the following notes on the notebook page in a neat, realistic handwriting style: {notes}"
    
    inputs = {
        "image": [background_image],
        "prompt": prompt,
        "true_cfg_scale": 4.0,
        "num_inference_steps": 20,  # Reduced for CPU performance
        "guidance_scale": 1.0,
        "num_images_per_prompt": 1,
    }
    
    try:
        with torch.inference_mode():
            output = pipeline(**inputs)
        
        output_image = output.images[0]
        return output_image, "Success! (Note: CPU inference may be slow)"
    except Exception as e:
        return None, f"Error during generation: {str(e)}"

# Gradio Interface
with gr.Blocks(title="Qwen Notes to Notebook") as demo:
    gr.Markdown("# 📝 Qwen Notes to Notebook")
    gr.Markdown("Transform your digital notes into a realistic notebook page using the **Qwen-Image-Edit-2511** model.")
    gr.Markdown("⚠️ **Note**: Running on CPU. Generation may take several minutes per image.")
    
    with gr.Row():
        with gr.Column():
            notes_input = gr.Textbox(
                label="Enter your notes",
                placeholder="Type your notes here...",
                lines=10
            )
            bg_input = gr.Image(
                label="Optional: Upload a custom notebook background",
                type="pil"
            )
            generate_btn = gr.Button("Generate Notebook Image", variant="primary")
        
        with gr.Column():
            output_image = gr.Image(label="Generated Notebook Page")
            status_text = gr.Textbox(label="Status", interactive=False)
    
    generate_btn.click(
        fn=generate_notes_image,
        inputs=[notes_input, bg_input],
        outputs=[output_image, status_text]
    )
    
    gr.Examples(
        examples=[
            ["Meeting Agenda:\n1. Project Kickoff\n2. Budget Review\n3. Timeline Discussion", None],
            ["Shopping List:\n- Milk\n- Eggs\n- Bread\n- Coffee beans", None],
            ["To-do List:\n- Finish the report\n- Call the client\n- Gym at 6 PM", None]
        ],
        inputs=[notes_input, bg_input]
    )

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