app.py

import os
#https://huggingface.co/spaces/Galis/room_interior_quality/tree/main
STABILITY_HOST = os.environ["STABILITY_HOST"]
STABILITY_KEY = os.environ["STABILITY_KEY"]
cohere_key = os.environ["cohere_key"]
import cohere
import random
co = cohere.Client(cohere_key)
import io
import os
import warnings
import math
from math import sqrt
from IPython.display import display
from PIL import Image
from stability_sdk import client
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
from PIL import Image
 
stability_api = client.StabilityInference(
    key=os.environ['STABILITY_KEY'], 
    verbose=True,
)


def generate_caption_keywords(prompt, model='command-xlarge-20221108', max_tokens=200, temperature=random.uniform(0.1, 2), k=0, p=0.75, frequency_penalty=0, presence_penalty=0, stop_sequences=[]):
    
    response = co.generate(
      model=model,
      prompt=prompt,
      max_tokens=max_tokens,
      temperature=temperature,
      k=k,
      p=p,
      frequency_penalty=frequency_penalty,
      presence_penalty=presence_penalty,
      stop_sequences=stop_sequences,
      return_likelihoods='NONE')

    def highlight_keywords(text):
        keywords = []
        text = text.lower()
        text = re.sub(r'[^a-z\s]', '', text) # remove punctuation
        text = re.sub(r'\b(the|and|of)\b', '', text) # remove stop words
        words = text.split()
        for word in words:
            if word not in keywords:
                keywords.append(word)
        return keywords

    caption = response.generations[0].text
    keywords = highlight_keywords(caption)
    keywords_string = ', '.join(keywords)

    return caption, keywords_string


 
def img2img( path ,design,x_prompt,alt_prompt,strength,guidance_scale,steps):
#####
#     img = Image.open(path)
#     width, height = img.size

# # Set the maximum width and height to 1024 pixels
#     max_width = 1024
#     max_height = 1024

# # Calculate the new size of the image, making sure that the width and height are within the allowed range
#     new_width = min(width, max_width)
#     new_height = min(height, max_height)

# # Calculate the new size of the image, making sure that the width and height are multiples of 64
#     new_width = ((new_width + 63) // 64) * 64
    # new_height = ((new_height + 63) // 64) * 64

# # Resize the image
    # img = img.resize((new_width, new_height), resample=Image.Resampling.BILINEAR)

#####

    
    # max_pixels = 1048576
    img = Image.open(path)
    width, height = img.size
    num_pixels = width * height
    
    # Calculate the maximum number of pixels allowed
    max_pixels = 1048576

    # Calculate the new size of the image, making sure that the number of pixels does not exceed the maximum limit
    if width * height > max_pixels:
        # Calculate the new width and height of the image
        ratio = width / height
        new_width = int(math.sqrt(max_pixels * ratio))
        new_height = int(math.sqrt(max_pixels / ratio))
    else:
        new_width = width
        new_height = height

    # Make sure that either the width or the height of the resized image is a multiple of 64
    if new_width % 64 != 0:
        new_width = ((new_width + 63) // 64) * 64
    if new_height % 64 != 0:
        new_height = ((new_height + 63) // 64) * 64

    # Resize the image
    img = img.resize((new_width, new_height), resample=Image.BILINEAR)

    # Check if the number of pixels in the resized image is within the maximum limit
    # If not, adjust the width and height of the image to bring the number of pixels within the maximum limit
    if new_width * new_height > max_pixels:
        while new_width * new_height > max_pixels:
            new_width -= 1
            new_height = int(max_pixels / new_width)



    
            # Calculate the closest multiple of 64 for each value
            if new_width % 64 != 0:
                new_width = (new_width // 64) * 64
            if new_height % 64 != 0:
                new_height = (new_height // 64) * 64

            # Make sure that the final values are less than the original values
            if new_width > 1407:
                new_width -= 64
            if new_height > 745:
                new_height -= 64
    
    new_height ,new_width
    # Initialize the values
    widthz = new_width
    heightz = new_height

    # Calculate the closest multiple of 64 for each value
    if widthz % 64 != 0:
        widthz = (widthz // 64) * 64
    if heightz % 64 != 0:
        heightz = (heightz // 64) * 64

    # Make sure that the final values are less than the original values
    if widthz > 1407:
        widthz -= 64
    if heightz > 745:
        heightz -= 64

    

    img = img.resize((widthz, heightz), resample=Image.BILINEAR)
 


    

########   
    max_attempts = 5 # maximum number of attempts before giving up
    attempts = 0 # current number of attempts
    while attempts < max_attempts:
        try:
            if x_prompt == True:
                prompt = alt_prompt
            else:
                try:
                    caption, keywords = generate_caption_keywords(design)
                    prompt = keywords
                except:
                    prompt = design

            # call the GRPC service to generate the image
            answers = stability_api.generate(
                prompt,
                init_image=img,
                seed=54321,
                start_schedule=strength,
            )
            for resp in answers:
                for artifact in resp.artifacts:
                    if artifact.finish_reason == generation.FILTER:
                        warnings.warn(
                            "Your request activated the API's safety filters and could not be processed."
                            "Please modify the prompt and try again.")
                    if artifact.type == generation.ARTIFACT_IMAGE:
                        img2 = Image.open(io.BytesIO(artifact.binary))
                        img2 = img2.resize((new_width, new_height), resample=Image.Resampling.BILINEAR)
                        img2.save("new_image.jpg")
                        print(type(img2))
            # if the function reaches this point, it means it succeeded, so we can return the result
            return img2
        except Exception as e:
            # if an exception is thrown, we will increment the attempts counter and try again
            attempts += 1
            print("Attempt {} failed: {}".format(attempts, e))
    # if the function reaches this point, it means the maximum number of attempts has been reached, so we will raise an exception
    raise Exception("Maximum number of attempts reached, unable to generate image")



import gradio as gr  

gr.Interface(img2img,  [gr.Image(source="upload", type="filepath", label="Input Image"),
    
                        gr.Dropdown(['interior design of living room', 
                                         'interior design of gaming room',
                                         'interior design of kitchen',
                                         'interior design of bedroom',
                                         'interior design of bathroom',
                                         'interior design of office',
                                         'interior design of meeting room',
                                         'interior design of personal room'],label="Click here to select your design by Cohere command Langauge model",value = 'interior design'), 
                        gr.Checkbox(label="Check Custom design if you already have prompt",value = False),

                        gr.Textbox(label = ' Input custom Prompt Text'),
                        gr.Slider(label='Strength , try with multiple value betweens 0.55 to 0.9 ', minimum = 0, maximum = 1, step = .01, value = .65),
                        gr.Slider(2, 15, value = 7, label = 'Guidence Scale'),
                        gr.Slider(10, 50, value = 50, step = 1, label = 'Number of Iterations')
                        ], 
                        gr.Image(), 
             examples =[['1.png','interior design of living room','False','interior design',0.6,7,50],
                  ['2.png','interior design of hall ','False','interior design',0.7,7,50],
                  ['3.png','interior design of bedroom','False','interior design',0.6,7,50]],title = "" +'**Baith-al-suroor بَیتُ الْسرور  🏡🤖**, Transform your space with the power of artificial intelligence. '+ "",
                                    description="Baith al suroor بَیتُ الْسرور  (house of happiness in Arabic)  🏡🤖  is a simple app that uses the power of artificial intelligence to transform your space. With the Cohere language Command model, it can generate descriptions of your desired design, and the Stable Diffusion algorithm creates relevant images to bring your vision to your thoughts. Give Baith AI a try and see how it can elevate your interior design.--if you want to scale / reaserch / build mobile app on this space konnect me   @[here](https://www.linkedin.com/in/sallu-mandya/)").launch( debug = True)