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import gradio as gr
from transformers import pipeline
from diffusers import StableDiffusionPipeline
import torch
# Initialize the pipelines
device = "cuda" if torch.cuda.is_available() else "cpu"
text_generator = pipeline("text-generation", model="gpt2", device=device)
summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=device)
title_generator = pipeline("text2text-generation", model="fabiochiu/t5-small-medium-title-generation", device=device)
# Initialize the Stable Diffusion pipeline
stable_diffusion = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
stable_diffusion.to(device)
# Function to generate blog post
def generate_blog_post(query):
# Generate the article
article = text_generator(query, max_length=500, num_return_sequences=1)[0]['generated_text']
# Generate a title for the article
title = title_generator(article, max_length=30, num_return_sequences=1)[0]['generated_text']
# Generate a cover image using Stable Diffusion
cover_image = stable_diffusion(query, num_inference_steps=50, guidance_scale=7.5).images[0]
# Generate a summary of the article
summary = summarizer(article, max_length=100, min_length=30, do_sample=False)[0]['summary_text']
return title, article, cover_image, summary
# Create the Gradio interface
iface = gr.Interface(
fn=generate_blog_post,
inputs=gr.Textbox(lines=2, placeholder="Enter your blog post topic..."),
outputs=[
gr.Textbox(label="Generated Title"),
gr.Textbox(label="Generated Article"),
gr.Image(label="Cover Image"),
gr.Textbox(label="Article Summary")
],
title="Blog Post Generator",
description="Enter a topic, and I'll generate a blog post with a title, cover image, and summary!"
)
# Launch the app
iface.launch()
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