Update app.py

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
 # Load the GPT-2 large model and tokenizer
 model_name = "gpt2-large"
@@ -7,19 +8,35 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 def generate_blogpost(topic):
-    input_text = f"Write a blog post about {topic}:"
-    inputs = tokenizer.encode(input_text, return_tensors="pt")
-    outputs = model.generate(inputs, max_length=500, num_return_sequences=1)
-    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return generated_text
+    try:
+        # Prepare input text
+        input_text = f"Write a blog post about {topic}:"
+        inputs = tokenizer.encode(input_text, return_tensors="pt")
+        st.write(f"Input IDs: {inputs}")
+
+        # Generate output
+        with torch.no_grad():
+            outputs = model.generate(inputs, max_length=500, num_return_sequences=1)
+        st.write(f"Output IDs: {outputs}")
+
+        # Decode the generated text
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return generated_text
+
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
 
 # Streamlit UI
 st.title("Blog Post Generator")
 st.write("Generate a blog post for a given topic using GPT-2 large.")
 
+# Input for the topic
 topic = st.text_input("Enter the topic:")
+
+# Generate button
 if st.button("Generate"):
     if topic:
+        # Generate and display the blog post
         blog_post = generate_blogpost(topic)
         st.write(blog_post)
     else: