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import streamlit as st | |
from transformers import pipeline | |
# Load the pre-trained language model | |
generator = pipeline("text-generation", model="gpt2") | |
# Streamlit app | |
st.title("Blog Post Generator") | |
st.write("Generate a blog post for a given topic using GPT-2.") | |
# Input for the blog post topic | |
topic = st.text_input("Enter a blog post topic:") | |
if st.button("Generate"): | |
if topic: | |
# Generate a blog post based on the given topic | |
with st.spinner("Generating blog post..."): | |
result = generator(f"Blog post topic: {topic}\n\nBlog post content:", max_length=500) | |
blog_post = result[0]['generated_text'] | |
st.subheader("Generated Blog Post") | |
st.write(blog_post) | |
else: | |
st.warning("Please enter a topic to generate the blog post.") | |