Create app.py

app.py

@@ -0,0 +1,45 @@
+# -*- 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)
+
+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')
+
+    # Generate summary
+    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...")
+
+    # Generate button
+    if st.sidebar.button("Generate Summary"):
+        summary = generate_summary(topic)
+        st.subheader("Generated Summary:")
+        st.write(summary)
+
+
+if __name__ == "__main__":
+    main()
+