app.py

import streamlit as st
import numpy as np
from PIL import Image
 
def main():
    st.title("Haltmann Diffusion Algorithm")
 
    slider = st.slider("Slider", 0, 255, 128) # default value=128, min=0, max=255
    st.title("Haltmann Diffusion Algorithm [C] 20XX ")
 
 

import streamlit as st

from PIL import Image

import numpy as np
 
st.text("This app generates VQ-GAN prompts for generating inpaintings.")
st.text("To use this app, simply enter the desired text prompt and hit generate.")

 
@st.cache(allow_output_mutation=True)  # This decorator ensures that the function only runs once per session.  Otherwise, each time we generate a prompt, it would run again!   This is important because we don't want to keep re-generating prompts unnecessarily.  We only want to generate a new prompt when the user enters a new one.   If we didn't cache this function, each time we generated a new prompt, it would also regenerate all of the previous prompts!   Caching is an important concept in Streamlit apps - it can help make your apps much more efficient by avoiding unnecessary computations.

def generate_prompt(text):    # This is the function that actually generates our VQ-GAN prompt    It takes in a string (the text prompt) and outputs another string (the generated VQ-GAN prompt).     We'll use this function to actually generate our VQ-GAN prompts when the user hits "Generate".

     return "Enter text here" + text + "and hit generate!"
 
     st.write(generate_prompt(""))    # We start by writing an empty string - this will be replaced with our generated prompt when the user hits "Generate"
 
def inpaint(img, mask):

    """Inpaints the given image using the given mask.

 

    Args:

        img: The image to inpaint. Must be a 3-channel RGB image.

        mask: The inpainting mask. Must be a binary 3-channel image 

            with 1s indicating the area to inpaint and 0s indicating 

            the area to leave unchanged.

 

    Returns:

        The inpainted image as a 3-channel RGB numpy array.     """
## V0.2
import streamlit as st
import numpy as np
from PIL import Image
import requests
import io


st.set_option('deprecation.showfileUploaderEncoding', False)


@st.cache(allow_output_mutation=True)
def load_image(img):
    im = Image.open(img)
    return im

    
def main():

    st.title("Dall-E Patrya ")

    uploaded_file = st.file_uploader("Choose an image", type="jpg")

st.markdown("Create images from textual descriptions with Dall-PFT!")
st.button("Edit Photo")

import streamlit as st

st.title("VQGAN Inpainting App")
st.markdown("This app uses a pre-trained VQGAN model to inpaint images.")

@st.cache(allow_output_mutation=True)
def load_model():

    # Load the pre-trained VQGAN model

    return vqgan.load_model('vqgan')

    
def inpaint(img, model, coords):

    # Inpaint the selected region of the image using the VQGAN model

    inpainted = vqgan.inpaint(img, model, coords)

    return inpainted
## [C] Haltmann Earth Divison  [C] - 20XX
import streamlit as st
from PIL import Image, ImageOps
import numpy as np


def inpaint(image, mask):

    """Inpaints the given image using the Mask."""

    
    # Convert to float32 before passing to cv2.inpaint() function. Otherwise it returns a distorted output. 
    img = np.float32(image)  

    
    dst = cv2.inpaint(img,mask,3,cv2.INPAINT_TELEA)