Update app.py

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
+
 # Load the model and tokenizer
 model_name = "gpt2-large"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -18,17 +19,14 @@ if st.button("Generate Blog Post"):
         # Prepare the prompt
         prompt = f"Write a blog post about {topic}:\n\n"
             
-        # Generate text
-        generation_config = GenerationConfig(max_new_tokens=50, do_sample=True, temperature=0.7)
-
         # Tokenize the input
         inputs_encoded = tokenizer.encode(prompt, return_tensors="pt")
 
-        # Model output
-        model_output = model.generate(inputs_encoded["input_ids"], generation_config=generation_config)[0]
+        # Generate text
+        model_output = model.generate(inputs_encoded, max_new_tokens=50, do_sample=True, temperature=0.7)
         
         # Decode the output
-        output = tokenizer.decode(model_output, skip_special_tokens=True)
+        output = tokenizer.decode(model_output[0], skip_special_tokens=True)
         
         # Display the generated blog post
         st.subheader("Generated Blog Post:")