|  | import gradio as gr | 
					
						
						|  | import torch | 
					
						
						|  | from diffusers import StableDiffusionPipeline | 
					
						
						|  | from transformers import pipeline | 
					
						
						|  |  | 
					
						
						|  | device = "cuda" if torch.cuda.is_available() else "cpu" | 
					
						
						|  |  | 
					
						
						|  | text_generator = pipeline( | 
					
						
						|  | "text-generation", model="openchat/openchat-3.5-0106", device=device | 
					
						
						|  | ) | 
					
						
						|  | summarizer = pipeline( | 
					
						
						|  | "summarization", model="sshleifer/distilbart-cnn-12-6", device=device | 
					
						
						|  | ) | 
					
						
						|  | title_generator = pipeline( | 
					
						
						|  | "text2text-generation", | 
					
						
						|  | model="fabiochiu/t5-small-medium-title-generation", | 
					
						
						|  | device=device, | 
					
						
						|  | ) | 
					
						
						|  |  | 
					
						
						|  | stable_diffusion = StableDiffusionPipeline.from_pretrained("prompthero/openjourney-v4") | 
					
						
						|  | stable_diffusion.to(device) | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | def generate_blog_post(query): | 
					
						
						|  |  | 
					
						
						|  | print("Generating article.") | 
					
						
						|  | article = text_generator(query, max_length=500, num_return_sequences=1)[0][ | 
					
						
						|  | "generated_text" | 
					
						
						|  | ] | 
					
						
						|  | print(f"{article = }") | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | print("Generating the title.") | 
					
						
						|  | title = title_generator(article, max_length=30, num_return_sequences=1)[0][ | 
					
						
						|  | "generated_text" | 
					
						
						|  | ] | 
					
						
						|  | print(f"{title = }") | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | print("Generating the cover.") | 
					
						
						|  | cover = stable_diffusion(title, num_inference_steps=20, guidance_scale=7.5).images[ | 
					
						
						|  | 0 | 
					
						
						|  | ] | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | print("Generating the summary.") | 
					
						
						|  | summary = summarizer(article, max_length=100, min_length=30, do_sample=False)[0][ | 
					
						
						|  | "summary_text" | 
					
						
						|  | ] | 
					
						
						|  | print(f"{summary = }") | 
					
						
						|  |  | 
					
						
						|  | return title, cover, summary, article | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | with gr.Blocks() as iface: | 
					
						
						|  | gr.Markdown("# Blog Post Generator") | 
					
						
						|  | gr.Markdown( | 
					
						
						|  | "Enter a topic, and I'll generate a blog post with a title, cover image, and summary!" | 
					
						
						|  | ) | 
					
						
						|  |  | 
					
						
						|  | with gr.Row(): | 
					
						
						|  | topic_input = gr.Textbox(lines=2, placeholder="Enter your blog post topic...") | 
					
						
						|  |  | 
					
						
						|  | generate_button = gr.Button("Generate Blog Post", size="sm") | 
					
						
						|  |  | 
					
						
						|  | with gr.Row(): | 
					
						
						|  | with gr.Column(scale=2): | 
					
						
						|  | title_output = gr.Textbox(label="Title") | 
					
						
						|  | article_output = gr.Textbox(label="Article", lines=10) | 
					
						
						|  |  | 
					
						
						|  | with gr.Column(scale=1): | 
					
						
						|  | cover_output = gr.Image(label="Cover") | 
					
						
						|  | summary_output = gr.Textbox(label="Summary", lines=5) | 
					
						
						|  |  | 
					
						
						|  | generate_button.click( | 
					
						
						|  | generate_blog_post, | 
					
						
						|  | inputs=topic_input, | 
					
						
						|  | outputs=[title_output, cover_output, summary_output, article_output], | 
					
						
						|  | ) | 
					
						
						|  |  | 
					
						
						|  | iface.launch() | 
					
						
						|  |  |