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import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the GPT-2 large model and tokenizer
model_name = "gpt2-large"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def generate_blogpost(topic):
try:
# Prepare input text
input_text = f"Write a blog post about {topic}:"
inputs = tokenizer.encode(input_text, return_tensors="pt")
st.write(f"Input IDs: {inputs}")
# Generate output
with torch.no_grad():
outputs = model.generate(inputs, max_length=500, num_return_sequences=1)
st.write(f"Output IDs: {outputs}")
# Decode the generated text
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_text
except Exception as e:
return f"An error occurred: {str(e)}"
# Streamlit UI
st.title("Blog Post Generator")
st.write("Generate a blog post for a given topic using GPT-2 large.")
# Input for the topic
topic = st.text_input("Enter the topic:")
# Generate button
if st.button("Generate"):
if topic:
# Generate and display the blog post
blog_post = generate_blogpost(topic)
st.write(blog_post)
else:
st.write("Please enter a topic to generate a blog post.")
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