app.py

import os
import asyncio
import time
from generate_prompts import generate_prompt
from diffusers import AutoPipelineForText2Image
from io import BytesIO
import gradio as gr
import ray

ray.init()

@ray.remote
class ModelActor:
    def __init__(self):
        print("Loading the model...")
        self.model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
        print("Model loaded successfully.")

    def generate_image(self, prompt, prompt_name):
        start_time = time.time()
        process_id = os.getpid()
        try:
            print(f"[{process_id}] Generating response for {prompt_name} with prompt: {prompt}")
            output = self.model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
            print(f"[{process_id}] Output for {prompt_name}: {output}")

            if isinstance(output.images, list) and len(output.images) > 0:
                image = output.images[0]
                buffered = BytesIO()
                try:
                    image.save(buffered, format="JPEG")
                    image_bytes = buffered.getvalue()
                    end_time = time.time()
                    print(f"[{process_id}] Image bytes length for {prompt_name}: {len(image_bytes)}")
                    print(f"[{process_id}] Time taken for {prompt_name}: {end_time - start_time} seconds")
                    return image_bytes
                except Exception as e:
                    print(f"[{process_id}] Error saving image for {prompt_name}: {e}")
                    return None
            else:
                raise Exception(f"[{process_id}] No images returned by the model for {prompt_name}.")
        except Exception as e:
            print(f"[{process_id}] Error generating image for {prompt_name}: {e}")
            return None

async def queue_api_calls(sentence_mapping, character_dict, selected_style):
    print(f"queue_api_calls invoked with sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}")
    prompts = []

    for paragraph_number, sentences in sentence_mapping.items():
        combined_sentence = " ".join(sentences)
        print(f"combined_sentence for paragraph {paragraph_number}: {combined_sentence}")
        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}")

    num_prompts = len(prompts)
    num_actors = min(num_prompts, 20)  # Limit to a maximum of 20 actors
    model_actors = [ModelActor.remote() for _ in range(num_actors)]

    tasks = [model_actors[i % num_actors].generate_image.remote(prompt, f"Prompt {paragraph_number}") for i, (paragraph_number, prompt) in enumerate(prompts)]
    print("Tasks created for image generation.")

    responses = await asyncio.gather(*[asyncio.to_thread(ray.get, task) for task in tasks])
    print("Responses received from image generation tasks.")

    images = {paragraph_number: response for (paragraph_number, _), response in zip(prompts, responses)}
    print(f"Images generated: {images}")
    return images

def process_prompt(sentence_mapping, character_dict, selected_style):
    print(f"process_prompt called with sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}")
    try:
        loop = asyncio.get_running_loop()
    except RuntimeError:
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
    print("Event loop created.")

    cmpt_return = loop.run_until_complete(queue_api_calls(sentence_mapping, character_dict, selected_style))
    print(f"process_prompt completed with return value: {cmpt_return}")
    return cmpt_return

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"
)

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