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| 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 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 | |
| # Open the image and retrieve its width and height | |
| img = Image.open(path) | |
| width, height = img.size | |
| # Calculate the number of pixels in the image | |
| num_pixels = width * height | |
| # Check if the number of pixels is within the allowed range | |
| if num_pixels > max_pixels: | |
| # Calculate the maximum width and height based on the number of pixels in the original image | |
| max_width = int(math.sqrt(max_pixels * width / height)) | |
| max_height = int(math.sqrt(max_pixels * height / width)) | |
| # Calculate the new dimensions of the image, making sure that the width and height are within the allowed range | |
| # and maintain the aspect ratio of the original image | |
| if width > height: | |
| new_width = max_width | |
| new_height = int(new_width * height / width) | |
| else: | |
| new_height = max_height | |
| new_width = int(new_height * width / height) | |
| else: | |
| # Set the new dimensions to the original dimensions of the image | |
| new_width = width | |
| new_height = 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_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) | |