Create app.py

app.py

@@ -0,0 +1,24 @@
+import gradio as gr
+from transformers import PegasusTokenizer, PegasusForConditionalGeneration
+
+# Load Pegasus model and tokenizer
+model_name = "google/pegasus-xsum"
+tokenizer = PegasusTokenizer.from_pretrained(model_name)
+model = PegasusForConditionalGeneration.from_pretrained(model_name)
+
+# Function to summarize text
+def summarize_text(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024)
+    summary_ids = model.generate(inputs.input_ids, max_length=128, min_length=30, length_penalty=2.0, num_beams=5)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+# Gradio interface
+iface = gr.Interface(fn=summarize_text, 
+                     inputs=gr.Textbox(label="Enter text to summarize"), 
+                     outputs=gr.Textbox(label="Summary"),
+                     title="Pegasus Text Summarizer",
+                     description="This AI agent summarizes long text using the Pegasus model.")
+
+# Launch the app
+iface.launch()