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Why would not recommend https://www.national-broadband.co.uk/

 Firm could not tell difference between man and woman found kind odd then claimed my trustpilot was not real or correct. I got a pretty common first name it has being proved most primary school kids can spell five letter names but they could not found a bit odd. I also found it odd guy claimed to be Scottish but could not say my surname right making him come across dumber more we talked.  https://www.theengineer.info/2025/08/starlink-promises-not-delivered-myth-of.html Makes me wonder if all the reviews they got  were fake by how he claim across to me on the phone. Always amazes me how many broadband firms got shit customer care foir why want a refund over extreme dumbness from team. internet service came in a cheap plastic bag did not look good had holes over them using very cheap plastic as cover.

Create Python code to make bulk generate AI images

 To generate AI images in bulk, we can use a deep learning library like TensorFlow and a pre-trained image generation model like DALL-E or GPT-3's image generation API. Here's a general outline of the code:

  1. Install necessary libraries: TensorFlow, Pillow (Python Imaging Library)

  2. Load the pre-trained image generation model. For example, if using DALL-E, we can use the OpenAI API to generate images.

  3. Define a function that takes in a list of text prompts and generates an image for each prompt.

  4. The function should call the image generation model for each prompt and save the resulting image to a specified folder.

Here's the Python code that implements this:

python
import requests import json from PIL import Image # API key for OpenAI API_KEY = 'YOUR_API_KEY_HERE' # Function to generate image from text prompt def generate_image(prompt, model): url = 'https://api.openai.com/v1/images/generations' headers = { 'Content-Type': 'application/json', 'Authorization': f'Bearer {API_KEY}' } data = { 'model': model, 'prompt': prompt, 'num_images': 1, 'size': '512x512' } response = requests.post(url, headers=headers, data=json.dumps(data)) if response.status_code != 200: print('Error:', response.json()['error']) return None else: image_url = response.json()['data'][0]['url'] image_response = requests.get(image_url) image = Image.open(BytesIO(image_response.content)) return image # List of text prompts to generate images for prompts = ['cat', 'dog', 'tree'] # Model to use for image generation model = 'image-alpha-001' # Folder to save generated images folder_path = 'generated_images/' # Generate image for each prompt and save to folder for prompt in prompts: image = generate_image(prompt, model) if image is not None: file_name = prompt + '.jpg' image.save(folder_path + file_name)

https://www.theengineer.info/This code uses the OpenAI API to generate images based on text prompts, and saves the resulting images to a specified folder. You can modify the prompts, model, and folder_path variables to suit your needs. Note that you'll need an API key from OpenAI to use their image generation API.



Saysimport requests import json from PIL import Image # API key for OpenAI API_KEY = 'YOUR_API_KEY_HERE' # Function to generate image from text prompt def generate_image(prompt, model): url = 'https://api.openai.com/v1/images/generations' headers = { 'Content-Type': 'application/json', 'Authorization': f'Bearer {API_KEY}' } data = { 'model': model, 'prompt': prompt, 'num_images': 1, 'size': '512x512' } response = requests.post(url, headers=headers, data=json.dumps(data)) if response.status_code != 200: print('Error:', response.json()['error']) return None else: image_url = response.json()['data'][0]['url'] image_response = requests.get(image_url) image = Image.open(BytesIO(image_response.content)) return image # List of text prompts to generate images for prompts = ['cat', 'dog', 'tree'] # Model to use for image generation model = 'image-alpha-001' # Folder to save generated images folder_path = 'generated_images/' # Generate image for each prompt and save to folder for prompt in prompts: image = generate_image(prompt, model) if image is not None: file_name = prompt + '.jpg' image.save(folder_path + file_name)




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