Create app.py

app.py

@@ -0,0 +1,29 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import gradio as gr
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("google/pegasus-xsum")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/pegasus-xsum")
+
+def summarize(text):
+    # Tokenize input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024)
+    
+    # Generate summary
+    summary_ids = model.generate(inputs["input_ids"])
+    
+    # Decode and return the summary
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=summarize,
+    inputs=gr.Textbox(lines=10, placeholder="Enter text to summarize...", label="Input Text"),
+    outputs=gr.Textbox(lines=5, label="Summary"),
+    title="Pegasus-XSum Text Summarizer",
+    description="Enter text and get an abstractive summary using Google's Pegasus-XSum model."
+)
+
+# Launch the app
+demo.launch()