Added WiNGPT-Babel-2.1

app.py

@@ -13,7 +13,6 @@ df = pl.read_parquet("isolanguages.parquet")
 non_empty_isos = df.slice(1).filter(pl.col("ISO639-1") != "").rows()
 # all_langs = languagecodes.iso_languages_byname
 all_langs = {iso[0]: (iso[1], iso[2], iso[3]) for iso in non_empty_isos} # {'Romanian': ('ro', 'rum', 'ron')}
-# iso1_to_name = {codes[0]: lang for entry in all_langs for lang, codes in entry.items()} # {'ro': 'Romanian', 'de': 'German'}
 # iso1_to_name = {iso[1]: iso[0] for iso in non_empty_isos} # {'ro': 'Romanian', 'de': 'German'}
 iso1toall = {iso[1]: (iso[0], iso[2], iso[3]) for iso in non_empty_isos} # {'ro': ('Romanian', 'rum', 'ron')}
 langs = list(favourite_langs.keys())
@@ -33,7 +32,8 @@ models = ["Helsinki-NLP", "QUICKMT", "Argos", "Google", "HPLT", "HPLT-OPUS",
           "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large", "google/flan-t5-xl",
           "google/madlad400-3b-mt", "jbochi/madlad400-3b-mt",
           "NiuTrans/LMT-60-0.6B", "NiuTrans/LMT-60-1.7B", "NiuTrans/LMT-60-4B",
-          "Lego-MT/Lego-MT", "HuggingFaceTB/SmolLM3-3B", "winninghealth/WiNGPT-Babel-2",
+          "Lego-MT/Lego-MT", "HuggingFaceTB/SmolLM3-3B",
+          "winninghealth/WiNGPT-Babel-2", "winninghealth/WiNGPT-Babel-2.1",
           "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"
           ]
@@ -449,7 +449,6 @@ class Translators:
         generated_ids = [
            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
         ]
-        print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True))
         output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
         result = output.split('\n')[-1].strip() if '\n' in output else output.strip()
         return result
@@ -624,7 +623,7 @@ def translate_text(input_text: str, s_language: str, t_language: str, model_name
         elif model_name == "HuggingFaceTB/SmolLM3-3B":
             translated_text = Translators(model_name, s_language, t_language, input_text).smollm()
 
-        elif model_name == "winninghealth/WiNGPT-Babel-2":      
+        elif "winninghealth/WiNGPT" in model_name:      
             translated_text = Translators(model_name, s_language, t_language, input_text).wingpt()
 
         elif "HPLT" in model_name: