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)