import gradio as gr
import os 
import sys
from pathlib import Path
import random
import string
import time
from queue import Queue
queue = Queue()

text_gen=gr.Interface.load("spaces/Omnibus/MagicPrompt-Stable-Diffusion")
proc5=gr.Interface.load("models/dreamlike-art/dreamlike-diffusion-1.0")


import random

def add_random_noise(prompt, noise_level=0.07):
    noise_level=0.01
    # Get the percentage of characters to add as noise
    percentage_noise = noise_level * 5
    # Get the number of characters to add as noise
    num_noise_chars = int(len(prompt) * (percentage_noise/100))
    # Get the indices of the characters to add noise to
    noise_indices = random.sample(range(len(prompt)), num_noise_chars)
    # Add noise to the selected characters
    prompt_list = list(prompt)
    for index in noise_indices:
        prompt_list[index] = random.choice(string.ascii_letters + string.punctuation)
    return "".join(prompt_list)

queue_length_counter = 0

def send_it8(inputs, noise_level, proc5=proc5):
    global queue_length_counter
    prompt_list = list(inputs)
    prompt_with_noise = "".join(prompt_list)
    if queue_length_counter >= 15:
        if not queue.empty():
            queue.queue.clear()
        queue_length_counter = 0
    output8 = proc5(prompt_with_noise)
    queue_length_counter += 1
    time.sleep(3)
    return output8
    time.sleep(1)



def get_prompts(prompt_text):
    global queue_length_counter
    if queue_length_counter >= 15:
        if not queue.empty():
            queue.queue.clear()
        queue_length_counter = 0
    output = text_gen(prompt_text)
    queue_length_counter += 1
    time.sleep(3)
    return output
    time.sleep(1)


with gr.Blocks() as myface:
    with gr.Row():

        input_text=gr.Textbox(label="Short Prompt")
        see_prompts=gr.Button("Magic Prompt")
    with gr.Row():

        prompt=gr.Textbox(label="Enter Prompt")
        noise_level=gr.Slider(minimum=0.1, maximum=3, step=0.1, label="Noise Level: Controls how much randomness is added to the input before it is sent to the model. Higher noise level produces more diverse outputs, while lower noise level produces similar outputs.")
        run=gr.Button("Generate")

    with gr.Row():
        output8=gr.Image(label="Dreamlike Diffusion 1.0")

    
    run.click(send_it8, inputs=[prompt, noise_level], outputs=[output8],api_name="predict")
    see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt])



myface.queue(concurrency_count=8)
myface.launch(enable_queue=True, inline=True)
while True:
    if queue.qsize() >= 20:
        queue.queue.clear()
    time.sleep(30)