Update app.py

app.py

@@ -29,21 +29,8 @@ def summ_t5(text):
     return summary_text
     
 def summ_bert(text):
-    encoding = tokenizer(text, padding='longest', return_tensors="pt")
-    outputs = model.generate(
-                input_ids=encoding["input_ids"], attention_mask=encoding["attention_mask"],
-                max_length=512,
-                do_sample=True,
-                top_k=200,
-                top_p=0.95,
-                early_stopping=True,
-                num_return_sequences=5)
-    summary_text = tokenizer_bert.decode(summary_ids[0], skip_special_tokens=True)
-    return summary_text
-
-def para_t5(text):
-    input_ids = t5_para_tokenizer.encode(text, return_tensors='pt')
-    outputs = t5_para_model .generate(input_ids,
+    encoding = tokenizer_bert(text, return_tensors="pt")
+    summary_ids= model_bert.generate(input_ids,
                 min_length=20,
                 max_length=100,
                 num_beams=10,
@@ -56,6 +43,20 @@ def para_t5(text):
                 temperature = 0.8,
                 top_k = 50,
                 top_p = 0.95)
+
+    summary_text = tokenizer_bert.decode(summary_ids[0], skip_special_tokens=True)
+    return summary_text
+
+def para_t5(text):
+    input_ids = t5_para_tokenizer.encode(text, padding='longest', return_tensors='pt')
+    outputs = t5_para_model.generate(
+                input_ids=encoding["input_ids"], attention_mask=encoding["attention_mask"],
+                max_length=512,
+                do_sample=True,
+                top_k=200,
+                top_p=0.95,
+                early_stopping=True,
+                num_return_sequences=5)
     return [
         t5_para_tokenizer.decode(
             output, skip_special_tokens=True, clean_up_tokenization_spaces=True