Update app.py

app.py

@@ -1,12 +1,15 @@
 import streamlit as st
-from transformers import pipeline
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
 # Load GPT-2 model and tokenizer
 @st.cache(allow_output_mutation=True)
 def load_model():
-    return pipeline("text-generation", model="gpt-2")
+    tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    model = AutoModelForCausalLM.from_pretrained("gpt2")
+    return tokenizer, model
 
-model = load_model()
+tokenizer, model = load_model()
 
 st.title("Blog Post Generator")
 st.write("Generate a blog post for a given topic using GPT-2.")
@@ -17,12 +20,19 @@ topic = st.text_input("Enter the topic for your blog post:")
 # Generate blog post button
 if st.button("Generate Blog Post"):
     if topic:
+        # Prepare the input for the model
+        input_text = f"Write a blog post about {topic}."
+        inputs = tokenizer.encode(input_text, return_tensors="pt")
+
         # Generate the blog post using GPT-2
-        result = model(f"Write a blog post about {topic}.", max_length=500, num_return_sequences=1)
-        blog_post = result[0]['generated_text']
+        outputs = model.generate(inputs, max_length=500, num_return_sequences=1, no_repeat_ngram_size=2, early_stopping=True)
+
+        # Decode the generated text
+        blog_post = tokenizer.decode(outputs[0], skip_special_tokens=True)
         
         st.write("### Generated Blog Post:")
         st.write(blog_post)
     else:
         st.write("Please enter a topic to generate a blog post.")
 
+