Update app.py

app.py

@@ -1,4 +1,5 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
+import streamlit as st
+from transformers import GPT2LMHeadModel, AutoTokenizer, AutoModelForCausalLM
 import torch
 
 # Initialize the tokenizer and model
@@ -22,9 +23,15 @@ def generate_blog(topic, max_length=500, num_return_sequences=1):
     generated_texts = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
     return generated_texts
 
-# Example usage
-topic = input(str("Enter the topic:"))
-generated_blogs = generate_blog(topic)
+# Streamlit UI
+st.title("Blog Generator")
+topic = st.text_input("Enter the topic:")
 
-for i, blog in enumerate(generated_blogs):
-    print(f"Blog {i+1}:\n{blog}\n")
+if st.button("Generate Blog"):
+    if topic:
+        generated_blogs = generate_blog(topic)
+        for i, blog in enumerate(generated_blogs):
+            st.subheader(f"Blog {i+1}")
+            st.write(blog)
+    else:
+        st.write("Please enter a topic to generate a blog.")