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Create app.py
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app.py
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# Load the GPT-2 model and tokenizer
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model_name = 'gpt2-large'
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tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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model = GPT2LMHeadModel.from_pretrained(model_name)
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def generate_blog_post(topic):
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# Encode the input topic
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inputs = tokenizer.encode(topic, return_tensors='pt')
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# Generate the blog post
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outputs = model.generate(inputs, max_length=500, num_return_sequences=1, no_repeat_ngram_size=2,
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do_sample=True, top_k=50, top_p=0.95, temperature=0.9)
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# Decode the generated text
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blog_post = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return blog_post
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# Streamlit app
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st.title("Blog Post Generator")
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st.write("Enter a topic to generate a blog post.")
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topic = st.text_input("Topic:")
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if st.button("Generate"):
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blog_post = generate_blog_post(topic)
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st.write(blog_post)
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