import gradio as gr

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim

import kornia.augmentation as K
from CLIP import clip
from torchvision import transforms

from PIL import Image
import numpy as np
import math

from matplotlib import pyplot as plt
from fastprogress.fastprogress import master_bar, progress_bar
from IPython.display import HTML
from base64 import b64encode


def generate(text, n_steps):
  #todo
  return np.random.random((128, 128, 3)).astype(np.uint8)

iface = gr.Interface(fn=generate, 
  inputs=[
    gr.inputs.Textbox(label="Text Input"),
    gr.inputs.Number(default=42, label="N Steps")
  ], 
  outputs=[
    gr.outputs.Image(type="numpy", label="Output Image")
  ],
  ).launch()