import os from io import BytesIO #image generation stuff from PIL import Image # gradio / hf / image gen stuff import gradio as gr from dotenv import load_dotenv from google.cloud import aiplatform import vertexai from vertexai.preview.vision_models import ImageGenerationModel from vertexai import preview # GCP credentials stuff import json import pybase64 from google.oauth2 import service_account import google.auth import json import os from dotenv import load_dotenv load_dotenv( ) # Load the decrypted JSON file or its content as environment variable decrypted_service_account_path = "decrypted.json" # Get the secret from environment variables decrypter_json = os.getenv("DECRYPTER_JSON") # with open(decrypted_service_account_path, 'r') as f: # service_account_info = json.load(f) service_account_info = json.loads(decrypter_json) # service_account_json = pybase64.b64decode(os.getenv("IMAGEN")) # service_account_info = json.loads(service_account_json) credentials = service_account.Credentials.from_service_account_info(service_account_info) project="elemental-shine-437217-t6" aiplatform.init(project=project, credentials=credentials) def generate_image(prompt, model_name): try: print(f"Generating image with prompt: {prompt}, model: {model_name}") model = ImageGenerationModel.from_pretrained(model_name) response = model.generate_images( prompt=prompt, number_of_images=1 ) print("Received response:", response) image_bytes = response[0]._image_bytes image_url = Image.open(BytesIO(image_bytes)) except Exception as e: print(f"Error: {e}") raise gr.Error(f"An error occurred while generating the image: {str(e)}") return image_url with gr.Blocks() as demo: gr.Markdown("