Update app.py

app.py

@@ -44,7 +44,7 @@ models = ["Helsinki-NLP", "QUICKMT", "Argos", "Lego-MT/Lego-MT", "HPLT", "HPLT-O
           "t5-small", "t5-base", "t5-large",
           "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large", "google/flan-t5-xl",
           "google/madlad400-3b-mt", "Heng666/madlad400-3b-mt-ct2", "Heng666/madlad400-3b-mt-ct2-int8", "Heng666/madlad400-7b-mt-ct2-int8",
-          "BSC-LT/salamandraTA-2b-instruct", "BSC-LT/salamandraTA-7b-instruct", "BSC-LT/salamandraTA-2B-academic", "BSC-LT/salamandraTA-7B-academic",
+          "BSC-LT/salamandraTA-2b-instruct", "BSC-LT/salamandraTA-7b-instruct",
           "utter-project/EuroLLM-1.7B", "utter-project/EuroLLM-1.7B-Instruct",
          "Unbabel/Tower-Plus-2B", "Unbabel/TowerInstruct-7B-v0.2", "Unbabel/TowerInstruct-Mistral-7B-v0.2",
           "HuggingFaceTB/SmolLM3-3B",
@@ -214,35 +214,6 @@ class Translators:
         pipe = pipeline("text-generation", model=self.model_name)
         messages = [{"role": "user", "content": f"Translate the following text from {self.sl} into {self.tl}.\n{self.sl}: {self.input_text} \n{self.tl}:"}]
         return pipe(messages, max_new_tokens=512, early_stopping=True, num_beams=5)[0]["generated_text"][1]["content"]
-
-    def salamandrata(self):
-        text = f"Translate the following text from {self.sl} into {self.tl}.\n{self.sl}: {self.input_text} \n{self.tl}:"
-        tokenizer = AutoTokenizer.from_pretrained(self.model_name)        
-        model = AutoModelForCausalLM.from_pretrained(
-            self.model_name,
-            device_map="auto",
-            dtype=torch.bfloat16
-          )
-        message = [{"role": "user", "content": text}]
-        from datetime import datetime
-        date_string = datetime.today().strftime('%Y-%m-%d')
-        prompt = tokenizer.apply_chat_template(
-            message,
-            tokenize=False,
-            add_generation_prompt=True,
-            date_string=date_string
-        )
-        inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
-        attention_mask = inputs["attention_mask"]        
-        input_length = inputs.shape[1]
-        outputs = model.generate(input_ids=inputs.to(model.device),
-                                max_new_tokens=512,
-                                early_stopping=True,
-                                num_beams=5,
-                                attention_mask=attention_mask,
-                                pad_token_id=tokenizer.eos_token_id,
-                                eos_token_id=tokenizer.eos_token_id)
-        return tokenizer.decode(outputs[0, input_length:], skip_special_tokens=True)
     
     def HelsinkiNLP_mulroa(self):
         try:
@@ -621,9 +592,6 @@ def translate_text(model_name: str, s_language: str, t_language: str, input_text
         elif model_name == 'Google':
             translated_text = Translators(model_name, sl, tl, input_text).google()
     
-        elif "academic" in model_name.lower():
-            translated_text = Translators(model_name, s_language, t_language, input_text).salamandrata()
-        
         elif "salamandra" in model_name.lower():
             translated_text = Translators(model_name, s_language, t_language, input_text).salamandratapipe()