Update app.py

app.py

@@ -41,28 +41,25 @@ 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)
-
-# 📚 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(en_to_pl_dir)
-en_to_pl_model = MarianMTModel.from_pretrained(en_to_pl_dir).to(device)
+HF_CACHE_DIR = "/tmp/hf_cache"
+os.makedirs(HF_CACHE_DIR, exist_ok=True)
+os.environ["HF_HOME"] = HF_CACHE_DIR
+os.environ["TRANSFORMERS_CACHE"] = HF_CACHE_DIR
+
+#  Pobieramy modele
+pl_to_en_dir = snapshot_download(
+    "Helsinki-NLP/opus-mt-pl-en", token=hf_token, cache_dir=HF_CACHE_DIR
+)
+en_to_pl_dir = snapshot_download(
+    "gsarti/opus-mt-tc-en-pl", token=hf_token, cache_dir=HF_CACHE_DIR
+)
 
-# 🔁 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)
+# Ładujemy
+pl_to_en_tok = MarianTokenizer.from_pretrained(pl_to_en_dir)
+pl_to_en_mod = MarianMTModel.from_pretrained(pl_to_en_dir).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():
-        translated = en_to_pl_model.generate(**inputs)
-    return en_to_pl_tokenizer.batch_decode(translated, skip_special_tokens=True)
+en_to_pl_tok = MarianTokenizer.from_pretrained(en_to_pl_dir)
+en_to_pl_mod = MarianMTModel.from_pretrained(en_to_pl_dir).to(device)
 # ────────────────────── schemy Pydantic ────────────────────
 class Comment(BaseModel):
     text: str