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import streamlit as st
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load pre-trained model and tokenizer
model_name = "gpt2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
def generate_blog_post(topic, max_length=500):
# Encode the input text
input_ids = tokenizer.encode(topic, return_tensors='pt')
# Generate text
outputs = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
# Decode the generated text
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return text
# Streamlit app
st.title("Blog Post Generator")
st.write("Enter a topic, and the model will generate a blog post for you.")
topic = st.text_input("Topic", value="Artificial Intelligence")
max_length = st.slider("Max Length", min_value=50, max_value=1000, value=500)
if st.button("Generate Blog Post"):
with st.spinner("Generating..."):
blog_post = generate_blog_post(topic, max_length)
st.write(blog_post)
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