Update app.py

app.py

@@ -1,42 +1,31 @@
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
-import streamlit as st
 
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-
-# Load pre-trained model and tokenizer
+# Load the GPT-2 large model and tokenizer
 model_name = "gpt2-large"
-model = GPT2LMHeadModel.from_pretrained(model_name)
-tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-
-# Function to generate article
-def generate_article(title, max_length=300):
-    # Encode the title and add special tokens
-    input_ids = tokenizer.encode(title, return_tensors='pt')
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Add padding token to the tokenizer
+tokenizer.pad_token = tokenizer.eos_token  # Set padding token to EOS token
+
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Function to generate a blog post based on a topic title
+def generate_blog(topic_title, max_length=200):
+    # Step 1: Encode the input
+    inputs = tokenizer.encode_plus(topic_title, return_tensors='pt', padding=True)
+    input_ids = inputs['input_ids']
+    attention_mask = inputs['attention_mask']
     
-    # Generate the article
-    output = model.generate(
-        input_ids,
-        max_length=max_length,
-        num_return_sequences=1,
-        no_repeat_ngram_size=2,
-        early_stopping=True
-    )
+    # Step 2: Generate model output
+    output_ids = model.generate(input_ids, attention_mask=attention_mask, max_length=max_length, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
     
-    # Decode the generated text
-    article = tokenizer.decode(output[0], skip_special_tokens=True)
-    return article
-
-# Title input
-#title = "The Impact of Artificial Intelligence on Modern Education"
-title = st.text_area('Enter title')
-
-# Generate and print the article
-if title:
-    article = generate_article(title)
-    st.json(article)
-
-
-
-#print(article)
+    # Step 3: Decode the output
+    blog_post = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    
+    return blog_post
 
+# Example usage
+topic_title = input("Enter a topic title for the blog post: ")
+blog_post = generate_blog(topic_title)
+print("\nGenerated Blog Post:\n")
+print(blog_post)