Update app.py

app.py

@@ -1,18 +1,23 @@
 from transformers import PegasusTokenizer, PegasusForConditionalGeneration
-import gradio as gr
 
-# Load model and tokenizer based on the provided configuration
+# Load tokenizer and model
 tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-cnn_dailymail")
 model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-cnn_dailymail")
 
-def summarize(text, prompt):
-    # Prepend the prompt to the input text
-    inputs = tokenizer(prompt + " " + text, return_tensors="pt", max_length=1024, truncation=True)
-    # Generate the summary
-    summary_ids = model.generate(inputs.input_ids, max_length=128, min_length=32, length_penalty=0.8, num_beams=8, early_stopping=True)
+def summarize(text):
+    inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = model.generate(
+        inputs.input_ids,
+        max_length=1500,
+        min_length=100,
+        length_penalty=0.9,
+        num_beams=6,
+        early_stopping=True
+    )
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
     return summary
 
+
 # Gradio interface
 interface = gr.Interface(
     fn=summarize,