Create application.py

application.py

@@ -0,0 +1,28 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Set page title and header
+st.set_page_config(page_title="Text Summarizer", page_icon=":memo:")
+st.header("Text Summarizer using Arjun9/bart_samsum")
+
+# Load model and tokenizer
+model_name = "Arjun9/bart_samsum"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Create text input area
+input_text = st.text_area("Enter the text you want to summarize:", "")
+
+# Create a function to generate summary
+def generate_summary(text):
+    inputs = tokenizer.encode_plus(text, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(inputs["input_ids"], num_beams=4, max_length=128, early_stopping=True)
+    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return summary
+
+# Display summary if input text is provided
+if input_text:
+    summary = generate_summary(input_text)
+    st.write("**Summary:**", summary)
+
+streamlit run app.py