Update app.py

app.py

@@ -1,10 +1,31 @@
-import streamlit as st
-from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-pipe = pipeline('sentiment-analysis')
-text = st.text_area('Enter some text')
+# Load the GPT-2 large model and tokenizer
+model_name = "gpt2-large"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Add padding token to the tokenizer
+tokenizer.pad_token = tokenizer.eos_token  # Set padding token to EOS token
 
-if text:
-    out = pipe(text)
-    st.json(out)
-    
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Function to generate a blog post based on a topic title
+def generate_blog(topic_title, max_length=300):
+    # 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']
+    
+    # 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)
+    
+    # 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)