import gradio as gr
import os
import sys
import random
import string
import time
from queue import Queue
from threading import Thread
import requests

model_interface = gr.Interface.load("models/segmind/SSD-1B")
API_URL = "https://api-inference.huggingface.co/models/segmind/SSD-1B" 


def restart_script_periodically():
    while True:
        random_time = random.randint(540, 600)
        time.sleep(random_time)
        os.execl(sys.executable, sys.executable, *sys.argv)

restart_thread = Thread(target=restart_script_periodically, daemon=True)
restart_thread.start()

queue = Queue()
queue_threshold = 100

def add_random_noise(prompt, noise_level=0.00):
    if noise_level == 0:
        noise_level = 0.00
    percentage_noise = noise_level * 5
    num_noise_chars = int(len(prompt) * (percentage_noise / 100))
    noise_indices = random.sample(range(len(prompt)), num_noise_chars)
    prompt_list = list(prompt)
    noise_chars = list(string.ascii_letters + string.punctuation + ' ' + string.digits)
    noise_chars.extend(['😍', '💩', '😂', '🤔', '😊', '🤗', '😭', '🙄', '😷', '🤯', '🤫', '🥴', '😴', '🤩', '🥳', '😔', '😩', '🤪', '😇', '🤢', '😈', '👹', '👻', '🤖', '👽', '💀', '🎃', '🎅', '🎄', '🎁', '🎂', '🎉', '🎈', '🎊', '🎮', '❤️', '💔', '💕', '💖', '💗', '🐶', '🐱', '🐭', '🐹', '🦊', '🐻', '🐨', '🐯', '🦁', '🐘', '🔥', '🌧️', '🌞', '🌈', '💥', '🌴', '🌊', '🌺', '🌻', '🌸', '🎨', '🌅', '🌌', '☁️', '⛈️', '❄️', '☀️', '🌤️', '⛅️', '🌥️', '🌦️', '🌧️', '🌩️', '🌨️', '🌫️', '☔️', '🌬️', '💨', '🌪️', '🌈'])
    for index in noise_indices:
        prompt_list[index] = random.choice(noise_chars)
    return "".join(prompt_list)

# Existing code...

import uuid  # Import the UUID library

# Existing code...

# Existing code...

request_counter = 0  # Global counter to track requests
# Defining the image styles and default style
IMAGE_STYLES = [
    "No style",
    "Cinematic",
    "Photographic",
    "Anime",
    "Manga",
    "Digital Art",
    "Pixel art",
    "Fantasy art",
    "Neonpunk",
    "3D Model"
]

DEFAULT_IMAGE_STYLE = "Cinematic"


# ... (Previous code remains unchanged)
def generate_txt2img(model_interface, prompt, is_negative=False, image_style="None style", steps=50, cfg_scale=7, seed=None):
    if image_style == "None style":
        payload = {
            "inputs": prompt + ", 8k",
            "is_negative": is_negative,
            "steps": steps,
            "cfg_scale": cfg_scale,
            "seed": seed if seed is not None else random.randint(-1, 2147483647)
        }
    elif image_style == "Cinematic":
        payload = {
            "inputs": prompt + ", realistic, detailed, textured, skin, hair, eyes, by Alex Huguet, Mike Hill, Ian Spriggs, JaeCheol Park, Marek Denko",
            "is_negative": is_negative + ", abstract, cartoon, stylized",
            "steps": steps,
            "cfg_scale": cfg_scale,
            "seed": seed if seed is not None else random.randint(-1, 2147483647)
        }
    elif image_style == "Digital Art":
        payload = {
            "inputs": prompt + ", faded , vintage , nostalgic , by Jose Villa , Elizabeth Messina , Ryan Brenizer , Jonas Peterson , Jasmine Star",
            "is_negative": is_negative + ", sharp , modern , bright",
            "steps": steps,
            "cfg_scale": cfg_scale,
            "seed": seed if seed is not None else random.randint(-1, 2147483647)
        }
    elif image_style == "Portrait":
        payload = {
            "inputs": prompt + ", soft light, sharp, exposure blend, medium shot, bokeh, (hdr:1.4), high contrast, (cinematic, teal and orange:0.85), (muted colors, dim colors, soothing tones:1.3), low saturation, (hyperdetailed:1.2), (noir:0.4), (natural skin texture, hyperrealism, soft light, sharp:1.2)",
            "is_negative": is_negative,
            "steps": steps,
            "cfg_scale": cfg_scale,
            "seed": seed if seed is not None else random.randint(-1, 2147483647)
        }

    image_bytes = requests.post(API_URL, headers=headers, json=payload).content
    image = Image.open(io.BytesIO(image_bytes))
    return image


# ... (Previous code remains unchanged)

with gr.Blocks(css=".gradio-container {background-color: #fdf7e6;} footer{display:none !important;}",) as demo:

    with gr.Column(elem_id="col-container"):
        
        with gr.Row(variant="compact"):
            prompt = gr.Textbox(
                lines=8,
                label="Enter your prompt",
                show_label=False,
                max_lines=10,
                placeholder="Full Prompt",
            ).style(
                container=False,
                textarea={'height': '400px'}
            )

            run = gr.Button("Generate Images").style(full_width=False)
        
        with gr.Accordion("Advanced options", open=False):
            with gr.Row():
                style_selection = gr.Radio(
                    show_label=True, container=True, interactive=True,
                    choices=IMAGE_STYLES,
                    value=DEFAULT_IMAGE_STYLE,
                    label='Image Style'
                )   
                
                negative_prompt = gr.Textbox(label="Negative Prompt (Optional)", placeholder="Example: blurry, unfocused", lines=2)

        with gr.Row():
            with gr.Row():
                noise_level = gr.Slider(minimum=0.0, maximum=3, step=0.1, label="Noise Level")

        with gr.Row():
            with gr.Row():
                output1 = gr.Image(label="", show_label=False, show_share_button=False)
                output2 = gr.Image(label="", show_label=False, show_share_button=False)

        # Adjust the click event to use generate_txt2img function
        run.click(generate_txt2img, inputs=[prompt, negative_prompt, style_selection, noise_level], outputs=[output1])
        run.click(generate_txt2img, inputs=[prompt, negative_prompt, style_selection, noise_level], outputs=[output2])
        
    demo.launch(enable_queue=True, inline=True)