Update app.py

app.py

@@ -20,6 +20,10 @@ ROOT = Path(__file__).parent
 
 aspect_dir = ROOT / "bert-aspect-ner"
 sentiment_dir = ROOT / "absa-roberta"
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+hf_token = os.getenv("HF_TOKEN")
 # ────────────────────── modele lokalne ─────────────────────
 aspect_tokenizer = AutoTokenizer.from_pretrained(
     str(aspect_dir), local_files_only=True, use_fast=False        # ← jeśli brak tokenizer.json
@@ -36,13 +40,15 @@ sentiment_model = AutoModelForSequenceClassification.from_pretrained(
 ).to(device)
 
 # ────────────────────── modele tłumaczeń (on-line) ─────────
+pl_to_en_dir = snapshot_download("Helsinki-NLP/opus-mt-pl-en", token=hf_token)
+en_to_pl_dir = snapshot_download("gsarti/opus-mt-tc-en-pl", token=hf_token)
 
-model_name = "Helsinki-NLP/opus-mt-pl-en"
-pl_to_en_tokenizer = MarianTokenizer.from_pretrained("translation-pl-en")
-pl_to_en_model = MarianMTModel.from_pretrained("translation-pl-en").to(device)
+# 📚 Ładowanie tokenizerów i modeli
+pl_to_en_tokenizer = MarianTokenizer.from_pretrained(pl_to_en_dir)
+pl_to_en_model = MarianMTModel.from_pretrained(pl_to_en_dir).to(device)
 
-en_to_pl_tokenizer = MarianTokenizer.from_pretrained("translation-en-pl")
-en_to_pl_model = MarianMTModel.from_pretrained("translation-en-pl").to(device)
+en_to_pl_tokenizer = MarianTokenizer.from_pretrained(en_to_pl_dir)
+en_to_pl_model = MarianMTModel.from_pretrained(en_to_pl_dir).to(device)
 
 # 🔁 Funkcje tłumaczeń
 def translate_pl_to_en(texts):