Update app.py

app.py

@@ -75,6 +75,22 @@ class Translators:
         response = requests.get(url)
         return response.json()[0][0][0]
 
+    def smollm(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        model = AutoModelForCausalLM.from_pretrained(self.model_name)
+        prompt = f"""Translate the following {self.sl} text to {self.tl}, generating only the translated text and maintaining the original meaning and tone:
+        {self.input_text}
+        Translation:"""
+        inputs = tokenizer(prompt, return_tensors="pt")
+        outputs = model.generate(
+            inputs.input_ids,
+            max_length=len(inputs.input_ids[0]) + 150,
+            temperature=0.3,
+            do_sample=True
+        ) 
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return response.split("Translation:")[-1].strip()
+
 def mtom(model_name, sl, tl, input_text):
     from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
     model = M2M100ForConditionalGeneration.from_pretrained(model_name)
@@ -127,22 +143,6 @@ def HelsinkiNLP(sl, tl, input_text):
     except KeyError as error:
         return f"Error: Translation direction {sl} to {tl} is not supported by Helsinki Translation Models", error     
 
-def smollm(model_name, sl, tl, input_text):
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(model_name)
-    prompt = f"""Translate the following {sl} text to {tl}, maintaining the original meaning and tone:
-    {input_text}
-    Translation:"""
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(
-        inputs.input_ids,
-        max_length=len(inputs.input_ids[0]) + 150,
-        temperature=0.3,
-        do_sample=True
-    ) 
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response.split("Translation:")[-1].strip()
-
 def flan(model_name, sl, tl, input_text):
     tokenizer = T5Tokenizer.from_pretrained(model_name, legacy=False)
     model = T5ForConditionalGeneration.from_pretrained(model_name)
@@ -326,7 +326,7 @@ def translate_text(input_text: str, sselected_language: str, tselected_language:
             translated_text = argos(sl, tl, input_text)
     
         elif model_name == 'Google':
-            translated_text = Translators('Google', sl, tl, input_text).google()
+            translated_text = Translators(model_name, sl, tl, input_text).google()
     
         elif "m2m" in model_name.lower():
             translated_text = mtom(model_name, sl, tl, input_text)
@@ -369,7 +369,7 @@ def translate_text(input_text: str, sselected_language: str, tselected_language:
             translated_text = tfive(model_name, sselected_language, tselected_language, input_text)
             
         elif model_name == "HuggingFaceTB/SmolLM3-3B":
-            translated_text = smollm(model_name, sselected_language, tselected_language, input_text)
+            translated_text = Translators(model_name, sselected_language, tselected_language, input_text).smollm()
             
     except Exception as error:
         translated_text = error