| import streamlit as st |
| from transformers import GPT2LMHeadModel, GPT2Tokenizer |
|
|
| |
| model_name = "gpt2" |
| tokenizer = GPT2Tokenizer.from_pretrained(model_name) |
| model = GPT2LMHeadModel.from_pretrained(model_name) |
|
|
| |
| st.title("Blog Post Generator") |
| topic = st.text_input("Enter a topic for your blog post:") |
|
|
| if st.button("Generate Blog Post"): |
| if topic: |
| |
| input_ids = tokenizer.encode(topic, return_tensors='pt') |
| |
| |
| output = model.generate(input_ids, max_length=500, num_return_sequences=1) |
| |
| |
| blog_post = tokenizer.decode(output[0], skip_special_tokens=True) |
| |
| |
| st.subheader("Generated Blog Post:") |
| st.write(blog_post) |
| else: |
| st.warning("Please enter a topic.") |
