Update app.py

app.py

@@ -1,45 +1,40 @@
-# -*- coding: utf-8 -*-
-"""app.py"""
-
 import streamlit as st
 from transformers import pipeline, BartForConditionalGeneration, BartTokenizer
 
-# Load pre-trained GPT-2 model and tokenizer
-model_name = "gpt2"
-model = GPT2LMHeadModel.from_pretrained(model_name)
-tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-
+# Load pre-trained BART model and tokenizer
 model_name = "facebook/bart-large-cnn"  # BART large model for summarization
 model = BartForConditionalGeneration.from_pretrained(model_name)
 tokenizer = BartTokenizer.from_pretrained(model_name)
 
-# Define function to generate blog post
-def generate_summary(topic):
-    input_text = f"{topic}"
-    inputs = tokenizer([input_text], max_length=1024, return_tensors='pt')
+# Define function to generate summary
+def generate_summary(text):
+    inputs = tokenizer([text], max_length=1024, return_tensors='pt')
 
     # Generate summary
-    summary_ids = model.generate(inputs['input_ids'], max_length=150, num_beams=4, length_penalty=2.0, early_stopping=True)
+    summary_ids = model.generate(
+        inputs['input_ids'],
+        max_length=150,
+        num_beams=4,
+        length_penalty=2.0,
+        early_stopping=True
+    )
     
     # Decode and return summary
     generated_summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
     return generated_summary
 
-
 # Streamlit app
 def main():
     st.title("Summarization App")
     
-    # Sidebar input for topic
-    topic = st.sidebar.text_area("Enter text to summarize", "Enter your text here...")
+    # Sidebar input for text
+    text = st.sidebar.text_area("Enter text to summarize", "Enter your text here...")
 
     # Generate button
     if st.sidebar.button("Generate Summary"):
-        summary = generate_summary(topic)
+        summary = generate_summary(text)
         st.subheader("Generated Summary:")
         st.write(summary)
 
-
 if __name__ == "__main__":
     main()
-