Create app.py

app.py

@@ -0,0 +1,26 @@
+# Load the GPT-2 model and tokenizer
+model_name = 'gpt2-large'
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
+
+def generate_blog_post(topic):
+    # Encode the input topic
+    inputs = tokenizer.encode(topic, return_tensors='pt')
+
+    # Generate the blog post
+    outputs = model.generate(inputs, max_length=500, num_return_sequences=1, no_repeat_ngram_size=2, 
+                             do_sample=True, top_k=50, top_p=0.95, temperature=0.9)
+
+    # Decode the generated text
+    blog_post = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return blog_post
+
+# Streamlit app
+st.title("Blog Post Generator")
+st.write("Enter a topic to generate a blog post.")
+
+topic = st.text_input("Topic:")
+
+if st.button("Generate"):
+    blog_post = generate_blog_post(topic)
+    st.write(blog_post)