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import gradio as gr
import torch
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,
)
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 summary.")
summary = summarizer(article, max_length=100, min_length=30, do_sample=False)[0][
"summary_text"
]
print(f"{summary = }")
return title, 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):
summary_output = gr.Textbox(label="Summary", lines=5)
generate_button.click(
generate_blog_post,
inputs=topic_input,
outputs=[title_output, summary_output, article_output],
)
iface.launch()
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