## Created by ruslanmv.com
## Happy coding!
import gradio as gr
import torch
import numpy as np
from diffusers import DiffusionPipeline
from transformers import pipeline
import subprocess
import threading

# FastAPI setup
def run_fastapi():
    subprocess.run(["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "7860"])

pipe = pipeline('text-generation', model='daspartho/prompt-extend')

def extend_prompt(prompt):
    return pipe(prompt+',', num_return_sequences=1)[0]["generated_text"]

def text_it(inputs):
    return extend_prompt(inputs)

def load_pipeline(use_cuda):
    device = "cuda" if use_cuda and torch.cuda.is_available() else "cpu"
    if device == "cuda":
        torch.cuda.max_memory_allocated(device=device)
        torch.cuda.empty_cache()
        pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
        pipe.enable_xformers_memory_efficient_attention()
        pipe = pipe.to(device)
        torch.cuda.empty_cache()
    else:
        pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
        pipe = pipe.to(device)
    return pipe

def genie(prompt="sexy woman", use_details=False, steps=2, seed=398231747038484200, use_cuda=False):
    pipe = load_pipeline(use_cuda)
    generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
    if use_details:
        extended_prompt = extend_prompt(prompt)
    else:
        extended_prompt = prompt
    int_image = pipe(prompt=extended_prompt, generator=generator, num_inference_steps=steps, guidance_scale=0.0).images[0]
    return int_image, extended_prompt

with gr.Blocks() as myface:
    gr.HTML()
    with gr.Row():
        with gr.Row():
            input_text = gr.Textbox(label='Prompt Text.', lines=1)
            details_checkbox = gr.Checkbox(label="details", info="Generate Details?")
            steps_slider = gr.Slider(1, maximum=5, value=2, step=1, label='Number of Iterations')
            seed_slider = gr.Slider(minimum=0, step=1, maximum=999999999999999999, randomize=True)
            cuda_checkbox = gr.Checkbox(label="Use CUDA", info="Use GPU for inference.")
    with gr.Row():
        generate_button = gr.Button("Generate")
    with gr.Row():
        output_image = gr.Image("./imagen.png")
        output_text = gr.Textbox(label="Generated Text", lines=2)
        generate_button.click(genie, inputs=[input_text, details_checkbox, steps_slider, seed_slider, cuda_checkbox], outputs=[output_image, output_text], concurrency_limit=10)

# Define the example
example = [["sexy woman", True, 2, 398231747038484200, ""],
           ['''sexy woman, in a black bikini, white bra, highly detailed, d & d, fantasy, highly detailed, digital painting, trending on artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and fuji choko and viktoria gavrilenko''', False, 2, 304332410412655740, ""],
           ['''sexy woman, D&D, fantasy, portrait, highly detailed, headshot, digital painting, trending on artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and magali villeneuve and wlop, ilya kuvshinov, octane render, 8 ''', False, 2, 747356768820251800, ""],
           [''' sexy woman, worksafe, light blonde long hair, fully clothed, brown eyes, sitting on a chair, sitting by a reflective pool, in the style of ilya kuvshinov, very dark, cinematic dramatic atmosphere, artstation, detailed facial ''', False, 2, 398231747038484200, ""],
           ['''sexy woman, medium shot, candid, red hair, 4 k, high definition, realistic, natural, highly detailed, photo realistic smooth, sharp, unreal engine 5, cinema4d, Blender, render photo-realistic, v-ray ''', False, 2, 398231747038484200, ""],
          ]

with gr.Interface(
    fn=genie,
    inputs=[input_text, details_checkbox, steps_slider, seed_slider, cuda_checkbox],
    outputs=[output_image, output_text],
    title="Stable Diffusion Turbo with GPT",
    description="Type your text and lets create an image, check the box details if you want a creative picture",
    examples=example,
) as iface:
    iface.launch(inline=True, show_api=False, max_threads=200)

if __name__ == "__main__":
    # اجرای FastAPI در یک رشته جداگانه
    threading.Thread(target=run_fastapi).start()

    # اجرای Gradio برنامه اصلی شما
    iface.launch()