Update app.py

app.py

@@ -1,15 +1,20 @@
 # Load the GPT-2 large model and tokenizer
 model_name = "gpt2-large"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Add padding token to the tokenizer
+tokenizer.pad_token = tokenizer.eos_token  # Set padding token to EOS token
+
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
 # Function to generate a blog post based on a topic title
 def generate_blog(topic_title, max_length=300):
     # Step 1: Encode the input
-    input_ids = tokenizer.encode(topic_title, return_tensors='pt')
+    inputs = tokenizer.encode_plus(topic_title, return_tensors='pt', padding=True)
+    input_ids = inputs['input_ids']
+    attention_mask = inputs['attention_mask']
     
     # Step 2: Generate model output
-    output_ids = model.generate(input_ids, max_length=max_length, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
+    output_ids = model.generate(input_ids, attention_mask=attention_mask, max_length=max_length, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
     
     # Step 3: Decode the output
     blog_post = tokenizer.decode(output_ids[0], skip_special_tokens=True)
@@ -17,7 +22,7 @@ def generate_blog(topic_title, max_length=300):
     return blog_post
 
 # Example usage
-topic_title = input("Enter a topic title for the blog post: ")
+topic_title = input("Enter title for the blog: ")
 blog_post = generate_blog(topic_title)
 print("\nGenerated Blog Post:\n")
-print(blog_post)
+print(blog_post)