app.py

import os
import asyncio
from io import BytesIO
from PIL import Image
from diffusers  import AutoPipelineForText2Image
import gradio as gr

print("Loading the Stable Diffusion model...")
try:
    model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
    print("Model loaded successfully.")
except Exception as e:
    print(f"Error loading model: {e}")
    model = None

def generate_image(prompt, prompt_name):
    try:
        if model is None:
            raise ValueError("Model not loaded properly.")
        
        print(f"Generating image for {prompt_name} with prompt: {prompt}")
        output = model(prompt=prompt, num_inference_steps=50, guidance_scale=7.5)
        print(f"Model output for {prompt_name}: {output}")

        if output is None:
            raise ValueError(f"Model returned None for {prompt_name}")

        if hasattr(output, 'images') and output.images:
            print(f"Image generated for {prompt_name}")
            image = output.images[0]
            buffered = BytesIO()
            image.save(buffered, format="PNG")
            image_bytes = buffered.getvalue()
            return image_bytes
        else:
            print(f"No images found in model output for {prompt_name}")
            raise ValueError(f"No images found in model output for {prompt_name}")
    except Exception as e:
        print(f"An error occurred while generating image for {prompt_name}: {e}")
        return None

def process_prompt(sentence_mapping, character_dict, selected_style):
    print("Processing prompt...")
    print(f"Sentence Mapping: {sentence_mapping}")
    print(f"Character Dict: {character_dict}")
    print(f"Selected Style: {selected_style}")
    
    prompt_results = {}
    for paragraph_number, sentences in sentence_mapping.items():
        combined_sentence = " ".join(sentences)
        prompt = f"Make an illustration in {selected_style} style from: {combined_sentence}"
        image_bytes = generate_image(prompt, f"Prompt {paragraph_number}")
        prompt_results[paragraph_number] = image_bytes

    return prompt_results

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"
).queue(concurrency_limit=10)

if __name__ == "__main__":
    print("Launching Gradio interface...")
    gradio_interface.launch()