Create app.py

app.py

@@ -0,0 +1,37 @@
+import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import streamlit as st
+
+def generate_blog(title, model_name='gpt2', max_length=500):
+    # Check if a GPU is available
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    st.write(f"Using device: {device}")
+
+    # Load the tokenizer and model
+    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+    model = GPT2LMHeadModel.from_pretrained(model_name).to(device)
+
+    # Prepare the input
+    input_ids = tokenizer.encode(title, return_tensors='pt').to(device)
+
+    # Generate text
+    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1, no_repeat_ngram_size=2, early_stopping=True)
+
+    # Decode the generated text
+    blog_post = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    return blog_post
+
+st.title("AI Blog Writer")
+st.write("Enter a blog title, and the AI will generate a blog post for you!")
+
+title = st.text_input("Enter the blog title:")
+
+if st.button("Generate Blog"):
+    if title:
+        with st.spinner("Generating blog post..."):
+            blog_post = generate_blog(title)
+        st.subheader("Generated Blog Post")
+        st.write(blog_post)
+    else:
+        st.warning("Please enter a blog title.")