import gradio as gr
from diffusers import AutoPipelineForText2Image, EulerAncestralDiscreteScheduler
from generate_propmts import generate_prompt
from PIL import Image
import asyncio
import traceback

# Load the model with a modified scheduler
model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
model.scheduler = EulerAncestralDiscreteScheduler.from_config(model.scheduler.config)
model.scheduler.config.prediction_type = "epsilon"  # Adjust prediction_type


async def generate_image(prompt):
    try:
        num_inference_steps = 25  # Or your desired value
        output = await asyncio.to_thread(
            model,
            prompt=prompt,
            num_inference_steps=num_inference_steps,  
            guidance_scale=0.0,
            output_type="pil"
        )
        if output.images:
            return output.images[0]
        else:
            raise Exception("No images returned by the model.")
    except Exception as e:
        print(f"Error generating image: {e}")
        traceback.print_exc()
        return None
 

async def inference(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(prompt)
        print(f"Generated prompt for paragraph {paragraph_number}: {prompt}")

    # Use asyncio.gather to run generate_image in parallel
    tasks = [generate_image(prompt) for prompt in prompts]
    images = await asyncio.gather(*tasks)

    # Filter out None values
    images = [image for image in images if image is not None]

    return images

gradio_interface = gr.Interface(
    fn=inference,
    inputs=[
        gr.JSON(label="Sentence Mapping"),
        gr.JSON(label="Character Dict"),
        gr.Dropdown(["oil painting", "sketch", "watercolor"], label="Selected Style")
    ],
    outputs=gr.Gallery(label="Generated Images")
)

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