Create app.py

app.py

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+import streamlit as st
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer, pipeline
+
+# Load the model and tokenizer
+model = PegasusForConditionalGeneration.from_pretrained("fatihfauzan26/PEGASUS_liputan6")
+tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-cnn_dailymail")
+
+# Initialize the summarization pipeline
+summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
+
+# Streamlit interface
+st.title("Summarization App using PEGASUS")
+
+# Input article for summarization
+sample_article = st.text_area('Enter the article you want to summarize', height=300)
+
+if sample_article:
+    # Generate summary
+    input_ids = tokenizer.encode(sample_article, return_tensors='pt')
+    summary_ids = model.generate(input_ids,
+                                 min_length=30,
+                                 max_length=128,
+                                 num_beams=8,
+                                 repetition_penalty=2.0,
+                                 length_penalty=0.8,
+                                 early_stopping=True,
+                                 no_repeat_ngram_size=2,
+                                 use_cache=True,
+                                 do_sample=True,
+                                 temperature=1.2,
+                                 top_k=50,
+                                 top_p=0.95)
+
+    # Decode the summary
+    summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+    # Display results
+    st.subheader("Summary")
+    st.write(summary_text)