Update app.py

app.py

@@ -558,7 +558,7 @@ def summarize(
     text, max_len=20, min_len=10
 ):
     log(f'CALL summarize')
-    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=float('inf'), truncation=False)
+    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=float('inf'), truncation=False).input_ids
     if get_tensor_length(inputs) < 3:
         print("Summarization Error: Text is too short, 3 words minimum!")
         return text
@@ -575,7 +575,7 @@ def summarize(
         )
         inputs = torch.tensor([[*list(outputs[0]), *list(inputs[0][512:])]])
         i = i + 1
-    summary = tokenizer.decode(inputs[0])
+    summary = tokenizer.decode(inputs[0], skip_special_tokens=True)
     log(f'RET summarize with summary as {summary}')
     return summary
 
@@ -646,8 +646,8 @@ def translate(txt,to_lang="en",from_lang=False):
     for index in range(chunks_length):
         ret = ret + ("" if ret == "" else " ") + tokenizer.decode(
             model.generate(
-                torch.tensor(list(inputs[0][ index*512:index*512+512 ]))
-            )[0]
+                torch.tensor([list(inputs[0][ index*512:index*512+512 ])]).input_ids
+            )[0], skip_special_tokens=True
         )
     log(f'RET translate with ret as {ret}')
     return ret