app.py

import gradio as gr
import torch
from diffusers import AutoPipelineForText2Image
from io import BytesIO
from generate_propmts import generate_prompt
from concurrent.futures import ThreadPoolExecutor
import asyncio

# Load the model once outside of the function
model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")

async def generate_image(prompt):
    try:
        # Truncate prompt if necessary
        output = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
        print(f"Model output: {output}")

        # Check if the model returned images
        if isinstance(output.images, list) and len(output.images) > 0:
            image = output.images[0]
            buffered = BytesIO()
            image.save(buffered, format="JPEG")
            image_bytes = buffered.getvalue()
            return image_bytes
        else:
            raise Exception("No images returned by the model.")
            
    except Exception as e:
        print(f"Error generating image: {e}")
        return None

async def process_prompt(sentence_mapping, character_dict, selected_style):
    images = {}
    print(f'sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}')
    prompts = []

    # Generate prompts for each paragraph
    for paragraph_number, sentences in sentence_mapping.items():
        combined_sentence = " ".join(sentences)
        prompt = generate_prompt(combined_sentence, sentence_mapping, character_dict, selected_style)
        prompts.append((paragraph_number, prompt))
        print(f"Generated prompt for paragraph {paragraph_number}: {prompt}")

    loop = asyncio.get_event_loop()
    tasks = []
    
    with ThreadPoolExecutor() as executor:
        for paragraph_number, prompt in prompts:
            tasks.append(loop.run_in_executor(executor, generate_image, prompt))

        for paragraph_number, task in zip(sentence_mapping.keys(), await asyncio.gather(*tasks)):
            try:
                image = task
                if image:
                    images[paragraph_number] = image
            except Exception as e:
                print(f"Error processing paragraph {paragraph_number}: {e}")

    return images

gradio_interface = gr.Interface(
    fn=process_prompt,
    inputs=[
        gr.JSON(label="Sentence Mapping"),
        gr.JSON(label="Character Dict"),
        gr.Dropdown(["oil painting", "sketch", "watercolor"], label="Selected Style")
    ],
    outputs="json",
    concurrency_limit=10  # Allow up to 10 concurrent executions
)

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