Update app.py

app.py

@@ -3,19 +3,32 @@ from transformers import GPT2Tokenizer, GPT2LMHeadModel
 
 # Define model and tokenizer
 model_name = 'gpt2-large'
+st.write("Loading model and tokenizer...")
 model = GPT2LMHeadModel.from_pretrained(model_name)
 tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+st.write("Model and tokenizer loaded.")
 
 def generate_blogpost(topic):
-    inputs = tokenizer.encode(topic, return_tensors='pt')
-    attention_mask = tokenizer.encode_plus(topic, return_tensors='pt')['attention_mask']
-    outputs = model.generate(inputs, attention_mask=attention_mask, max_length=500, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
-    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return text
+    try:
+        inputs = tokenizer.encode(topic, return_tensors='pt')
+        attention_mask = tokenizer.encode_plus(topic, return_tensors='pt')['attention_mask']
+        outputs = model.generate(
+            inputs,
+            attention_mask=attention_mask,
+            max_length=500,
+            num_return_sequences=1,
+            pad_token_id=tokenizer.eos_token_id
+        )
+        text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return text
+    except Exception as e:
+        return f"Error: {e}"
 
 # Streamlit app
 st.title('Blog Post Generator')
 topic = st.text_input('Enter a topic:')
+
 if topic:
+    st.write("Generating blog post...")
     blogpost = generate_blogpost(topic)
     st.write(blogpost)