import json
import gradio as gr
import torch
from diffusers import AutoPipelineForText2Image
import base64
from io import BytesIO
from generate_propmts import generate_prompt
from concurrent.futures import ThreadPoolExecutor

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

def generate_image(text, sentence_mapping, character_dict, selected_style):
    try:
        prompt, _ = generate_prompt(text, sentence_mapping, character_dict, selected_style)
        image = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0]
        buffered = BytesIO()
        buffered.write(image.tobytes())
        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
        return img_str
    except Exception as e:
        print(f"Error generating image: {e}")
        return None

def inference(sentence_mapping, character_dict, selected_style):
    images = {}
    print(f'sentence_mapping:{sentence_mapping}, character_dict:{character_dict}, selected_style:{selected_style}')
    
    # Parse sentence_mapping JSON string into a dictionary
    try:
        grouped_sentences = json.loads(sentence_mapping)
    except json.JSONDecodeError as e:
        print(f"Error parsing JSON: {e}")
        return {"error": "Invalid JSON input for sentence_mapping"}

    with ThreadPoolExecutor() as executor:
        futures = {}
        for paragraph_number, sentences in grouped_sentences.items():
            combined_sentence = " ".join(sentences)
            futures[paragraph_number] = executor.submit(generate_image, combined_sentence, grouped_sentences, character_dict, selected_style)

        for paragraph_number, future in futures.items():
            images[paragraph_number] = future.result()

    return images

gradio_interface = gr.Interface(
    fn=inference,
    inputs=[
        gr.Textbox(label="Sentence Mapping (JSON)"),
        gr.Textbox(label="Character Dict (JSON)"),
        gr.Dropdown(["Style 1", "Style 2", "Style 3"], label="Selected Style")
    ],
    outputs="text"
)

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