Update app.py

app.py

@@ -1,46 +1,35 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
 import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-# Load GPT-2 large model and tokenizer
-@st.cache(allow_output_mutation=True)
-def load_model():
-    tokenizer = AutoTokenizer.from_pretrained("gpt2-large")
-    model = AutoModelForCausalLM.from_pretrained("gpt2-large")
-    return tokenizer, model
-
-tokenizer, model = load_model()
 
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('gpt2-large')
+model = AutoModelForCausalLM.from_pretrained('gpt2-large')
+
+def generate_blog_post(topic):
+    prompt = f"Write a blog post about {topic}."
+    inputs = tokenizer.encode(prompt, return_tensors='pt')
+    
+    # Generate text
+    outputs = model.generate(inputs, max_length=500, num_return_sequences=1, do_sample=True, top_p=0.95, top_k=60)
+    
+    # Decode the generated text
+    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return text
+
+# Streamlit interface
 st.title("Blog Post Generator")
 st.write("Generate a blog post for a given topic using GPT-2 Large.")
 
-# User input for the blog post topic
-topic = st.text_input("Enter the topic for your blog post:")
+topic = st.text_input("Enter the topic for the blog post:")
 
-# Generate blog post button
 if st.button("Generate Blog Post"):
     if topic:
-        # Refine the input prompt to guide the model towards generating a blog post
-        input_text = f"Write a detailed blog post about {topic}. The post should cover various aspects of the topic and provide valuable information to the readers. Start with an introduction and follow with detailed paragraphs."
-
-        # Encode the input text
-        inputs = tokenizer.encode(input_text, return_tensors="pt")
-
-        # Generate the blog post using GPT-2 large
-        outputs = model.generate(
-            inputs,
-            max_length=500,
-            num_return_sequences=1,
-            no_repeat_ngram_size=2,
-            early_stopping=True,
-            temperature=0.7,
-            top_p=0.9
-        )
-
-        # Decode the generated text
-        blog_post = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        
-        st.write("### Generated Blog Post:")
-        st.write(blog_post)
+        with st.spinner("Generating..."):
+            try:
+                blog_post = generate_blog_post(topic)
+                st.subheader("Generated Blog Post")
+                st.write(blog_post)
+            except Exception as e:
+                st.error(f"Error generating blog post: {e}")
     else:
-        st.write("Please enter a topic to generate a blog post.")
+        st.warning("Please enter a topic.")