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djrana

Update app.py

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	import gradio as gr
	from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
	from PIL import Image

	# Load the pipeline for text generation
	text_generator = pipeline(
	"text-generation",
	model="Ar4ikov/gpt2-650k-stable-diffusion-prompt-generator",
	tokenizer="gpt2"
	)

	# Load tokenizer and model for image generation
	tokenizer = AutoTokenizer.from_pretrained("stablediffusionapi/juggernaut-xl-v8")
	model = AutoModelForCausalLM.from_pretrained("stablediffusionapi/juggernaut-xl-v8")

	# Function to generate text based on input prompt
	def generate_text(prompt):
	return text_generator(prompt, max_length=77)[0]["generated_text"]

	# Function to generate image based on input text
	def generate_image(text):
	# Tokenize input text
	input_ids = tokenizer.encode(text, return_tensors="pt")

	# Generate image conditioned on input text
	output = model.generate(input_ids, do_sample=True, max_length=128, num_return_sequences=1)

	# Decode generated image tokens to get image
	image_bytes = tokenizer.decode(output[0], skip_special_tokens=True)

	# Convert image bytes to PIL image
	image = Image.open(image_bytes)

	return image

	# Create Gradio interface
	iface = gr.Interface(
	fn=[generate_text, generate_image],
	inputs=["textbox", "textbox"],
	outputs=["textbox", "image"],
	title="AI Art Prompt Generator",
	description="Art Prompt Generator is a user-friendly interface designed to optimize input for AI Art Generator or Creator. For faster generation speeds, it's recommended to load the model locally with GPUs, as the online demo at Hugging Face Spaces utilizes CPU, resulting in slower processing times.",
	theme="huggingface"
	)

	# Launch the interface
	iface.launch()