Update app.py

app.py

@@ -18,7 +18,6 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 
 ROOT = Path(__file__).parent
 
-# Uwaga: NIE ma katalogu models/
 aspect_dir = ROOT / "bert-aspect-ner"
 sentiment_dir = ROOT / "absa-roberta"
 # ────────────────────── modele lokalne ─────────────────────
@@ -39,19 +38,19 @@ sentiment_model = AutoModelForSequenceClassification.from_pretrained(
 # ────────────────────── modele tłumaczeń (on-line) ─────────
 
 model_name = "Helsinki-NLP/opus-mt-pl-en"
-pl_to_en_tokenizer = MarianTokenizer.from_pretrained(model_name)
-pl_to_en_model = MarianMTModel.from_pretrained(model_name).to(device)
+pl_to_en_tokenizer = MarianTokenizer.from_pretrained("translation-pl-en")
+pl_to_en_model = MarianMTModel.from_pretrained("translation-pl-en").to(device)
 
+en_to_pl_tokenizer = MarianTokenizer.from_pretrained("translation-en-pl")
+en_to_pl_model = MarianMTModel.from_pretrained("translation-en-pl").to(device)
+
+# 🔁 Funkcje tłumaczeń
 def translate_pl_to_en(texts):
     inputs = pl_to_en_tokenizer(texts, return_tensors="pt", padding=True, truncation=True).to(device)
     with torch.no_grad():
         translated = pl_to_en_model.generate(**inputs)
     return pl_to_en_tokenizer.batch_decode(translated, skip_special_tokens=True)
 
-en_to_pl_model_name = "gsarti/opus-mt-tc-en-pl"
-en_to_pl_tokenizer = MarianTokenizer.from_pretrained(en_to_pl_model_name)
-en_to_pl_model = MarianMTModel.from_pretrained(en_to_pl_model_name).to(device)
-
 def translate_en_to_pl(texts):
     inputs = en_to_pl_tokenizer(texts, return_tensors="pt", padding=True, truncation=True).to(device)
     with torch.no_grad():