Updated app to use fine tuned longformer

app.py

@@ -1,21 +1,23 @@
 import gradio as gr
 import torch
-from transformers import PegasusTokenizer, PegasusForConditionalGeneration
+from transformers import AutoTokenizer, LEDForConditionalGeneration
 
 
 def summarize(Terms):
-    tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-billsum')
-    model = PegasusForConditionalGeneration.from_pretrained(
-        "arjav/TOS-Pegasus")
+    tokenizer = AutoTokenizer.from_pretrained('allenai/led-base-16384')
+    model = LEDForConditionalGeneration.from_pretrained("Arjav/TOS-Longformer")
+    
     input_tokenized = tokenizer.encode(
-        Terms, return_tensors='pt', max_length=1024, truncation=True)
+        Terms, return_tensors='pt', max_length=8192, truncation=True)
+    
     summary_ids = model.generate(input_tokenized,
                                  num_beams=9,
                                  no_repeat_ngram_size=3,
                                  length_penalty=2.0,
-                                 min_length= 150,
-                                 max_length= 200,
+                                 min_length= 200,
+                                 max_length= 400,
                                  early_stopping=True)
+   
     summary = [tokenizer.decode(g, skip_special_tokens=True,
                                 clean_up_tokenization_spaces=False) for g in summary_ids][0]