app.py

import modal
import gradio as gr 
import numpy as np 
from io import BytesIO
import requests

f = modal.Cls.lookup("casa-interior-hf-v3", "DesignModel")

import requests
from io import BytesIO
from google.cloud import vision
from google.oauth2 import service_account

credentials = {
  "type": "service_account",
  "project_id": "furniture-423815",
  "private_key_id": "be5e481a8e4499c164ed0147b3f024d4ef1f42f3",
  "private_key": "-----BEGIN PRIVATE KEY-----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\n3tIueX+ZTxWFRYcI6UGbW7k=\n-----END PRIVATE KEY-----\n",
  "client_email": "furniture-service@furniture-423815.iam.gserviceaccount.com",
  "client_id": "101044092237072973103",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token",
  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/furniture-service%40furniture-423815.iam.gserviceaccount.com",
  "universe_domain": "googleapis.com"
}

class GetProduct:
    
    def __init__(self):
        creds = service_account.Credentials.from_service_account_info(credentials)
        self.client = vision.ImageAnnotatorClient(credentials=creds)
    
    def inference(self, cropped_image) -> list:
        annotations = self.annotate_image(cropped_image)
        selected_images = self.report(annotations)
        
        return selected_images

    def annotate_image(self, image): 
        
        buffer = BytesIO()
        # Convert the image to RGB mode if it is RGBA
        if image.mode == 'RGBA':
            image = image.convert('RGB')
        image.save(buffer, format="JPEG")
        content = buffer.getvalue()
    
        image = vision.Image(content=content)
        web_detection = self.client.web_detection(image=image).web_detection
        return web_detection

    def report(self, annotations) -> list:
        selected_images = []
        if annotations.visually_similar_images: 
            for page in annotations.visually_similar_images:
                try: 
                    response = requests.get(page.url)
                    img = Image.open(BytesIO(response.content))
                    selected_images.append(img)
                except:
                    pass
        return selected_images 

GP = GetProduct()

def casa_ai_run_tab1(image=None, text=None): 
    
    if image is None: 
        print('Please provide image of empty room to design')
        return None

    if text is None: 
        print('Please provide a text prompt')
        return None

    result_image = f.inference.remote("tab1", image, text)
    return result_image

def casa_ai_run_tab2(dict=None, text=None):
    
    image = dict["background"].convert("RGB")
    mask = dict["layers"][0].convert('L')

    if np.sum(np.array(mask)) == 0: 
        mask = None 
        
    if mask is None: 
        print('Please provide a mask over the object you want to generate again.')
        
    if image is None and text is None: 
        print('Please provide context in form of image, text')
        return None
    
    result_image = f.inference.remote("tab2", image, text, mask)
    return result_image

def casa_ai_run_tab3(dict=None):
    selected_crop = dict["layers"][0].convert("RGB")
    
    if selected_crop is None: 
        print('Please provide cropped object')
        return None
    
    results = GP.inference(selected_crop)
    return results

with gr.Blocks() as casa:
    title = "Casa-AI Demo"
    description = "A Gradio interface to use CasaAI for virtual staging"

    with gr.Tab("Reimagine"):
        with gr.Row():
            with gr.Column():
                inputs = [
                            gr.Image(sources='upload', type="pil", label="Upload"), 
                            gr.Textbox(label="Room description.")
                        ]
            with gr.Column():
                outputs = [gr.Image(label="Generated room image")]

        
        submit_btn = gr.Button("Generate!")
        submit_btn.click(casa_ai_run_tab1, inputs=inputs, outputs=outputs)

        
    with gr.Tab("Redesign"):
        with gr.Row():
            with gr.Column():
                inputs = [
                            gr.ImageEditor(sources='upload', brush=gr.Brush(colors=["#FFFFFF"]), elem_id="image_upload", type="pil", label="Upload", layers=False, eraser=True, transforms=[]),
                            gr.Textbox(label="Description for redesigning masked object")]
            with gr.Column():
                outputs = [gr.Image(label="Image with new designed object")]
                
        submit_btn = gr.Button("Redesign!")
        submit_btn.click(casa_ai_run_tab2, inputs=inputs, outputs=outputs)

    with gr.Tab("Recommendation"):
        with gr.Row():
            with gr.Column():
                inputs = [
                            gr.ImageEditor(sources='upload', elem_id="image_upload", type="pil", label="Upload", layers=False, eraser=False, brush=False, transforms=['crop']),
                            ]
            with gr.Column():
                outputs = [gr.Gallery(label="Similar products")]
                
        submit_btn = gr.Button("Find similar products!")
        submit_btn.click(casa_ai_run_tab3, inputs=inputs, outputs=outputs)

casa.launch()