from asyncio import constants
import gradio as gr
import requests
import os 
import re
import random
import numpy as np
from perlin_numpy import generate_fractal_noise_2d

def create_fractal_noise(input_seed,input_size,res,n_octaves,persistence):
    """
    Generate fractal noise using the Perlin noise algorithm.
    """
    np.random.seed(input_seed)
    n = generate_fractal_noise_2d((input_size, input_size), (res, res), n_octaves, persistence)
    #reshape
    n3=np.repeat(n[:,:,np.newaxis],3,axis=2)
    #change from [-1,1] to [0,255]
    n3=(n3+1)/2*255
    #change type to int64
    n3=n3.astype(np.int64)
    return n3

demo = gr.Blocks()

with demo:
  gr.Markdown("<h1><center>fractalNoise</center></h1>")
  gr.Markdown(
        "<div>Create fractal-like noise (useful for map generation and such)</div>"
        "<div>Using pvigier's <a href=https://github.com/pvigier/perlin-numpy>perlin-numpy</a></div>"
    )
  
  
  with gr.Row():  
    input_seed = gr.Number(label="seed",value=0,precision=0)
    input_size = seed=gr.Number(value=256, label='size',precision=0)
    res=gr.Number(value=2, label='res',precision=0)
    n_octaves=gr.Number(value=8, label='n-octaves',precision=0)
    persistence=gr.Number(value=0.5, label='persistence')

  with gr.Row():
    b0 = gr.Button("Submit")

  with gr.Row():
    output_image = gr.outputs.Image(type="filepath", label='Output')

  
  
  b0.click(create_fractal_noise,inputs=[input_seed,input_size,res,n_octaves,persistence],outputs=[output_image])
  #examples=examples

demo.launch(enable_queue=True, debug=True)