Update app.py

app.py

@@ -69,8 +69,16 @@ class Translators:
         # model = AutoModel.from_pretrained(self.model_name, trust_remote_code=True)
         # model.half() # recommended for GPU
         model.eval()
+        model.float()
         # Translating from one or several sentences to a sole language
         src_tokens = tokenizer.encode_source_tokens_to_input_ids(self.input_text, target_language=self.tl)
+        # src_tokens may be a torch.Tensor or dict depending on tokenizer; ensure it's a tensor
+        if isinstance(src_tokens, torch.Tensor):
+            src_tokens = src_tokens.to(self.device)
+        else:
+            # if tokenizer returns dict-like inputs (input_ids, attention_mask)
+            for k, v in src_tokens.items():
+                src_tokens[k] = v.to(self.device)
         # src_tokens = src_tokens.to(self.device)
         # generated_tokens = model.generate(src_tokens)
         # return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0] 
@@ -78,9 +86,10 @@ class Translators:
         # src_tokens = tokenizer.encode_source_tokens_to_input_ids_with_different_tags([english_text, english_text, ], target_languages_list=["de", "zh", ])      
         # generated_tokens = model.generate(src_tokens.to(self.device))
         # results = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
-        with torch.no_grad():
+        with torch.inference_mode(): # no_grad inference_mode
             generated_tokens = model.generate(src_tokens)
-        return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+        result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+        return result
     
     def hplt(self, opus = False):
         # langs = ['ar', 'bs', 'ca', 'en', 'et', 'eu', 'fi', 'ga', 'gl', 'hi', 'hr', 'is', 'mt', 'nn', 'sq', 'sw', 'zh_hant']