Update app.py

app.py

@@ -1,36 +1,46 @@
-import torch
-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 using auto tokenizer and auto 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.")
 
-topic = st.text_input("Enter the topic for the blog post:")
+# User input for the blog post topic
+topic = st.text_input("Enter the topic for your blog post:")
 
+# Generate blog post button
 if st.button("Generate Blog Post"):
     if topic:
-        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}")
+        # 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)
     else:
-        st.warning("Please enter a topic.")
+        st.write("Please enter a topic to generate a blog post.")