Update app.py

app.py

@@ -3,7 +3,7 @@ from pathlib import Path
 from fastapi import FastAPI
 from pydantic import BaseModel
 from typing import List
-
+from transformers import MarianMTModel, MarianTokenizer
 import torch
 from transformers import (
     AutoTokenizer,
@@ -13,12 +13,6 @@ from transformers import (
 )
 import os
 
-
-print("📁 ZAWARTOŚĆ /app/bert-aspect-ner:")
-print(os.listdir("/app/bert-aspect-ner") if os.path.exists("/app/bert-aspect-ner") else "❌ NIE ISTNIEJE")
-
-print("📁 ZAWARTOŚĆ /app/absa-roberta:")
-print(os.listdir("/app/absa-roberta") if os.path.exists("/app/absa-roberta") else "❌ NIE ISTNIEJE")
 # ────────────────────── konfiguracja ──────────────────────
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -43,17 +37,26 @@ sentiment_model = AutoModelForSequenceClassification.from_pretrained(
 ).to(device)
 
 # ────────────────────── modele tłumaczeń (on-line) ─────────
-pl_to_en = pipeline(
-    "translation",
-    model="Helsinki-NLP/opus-mt-pl-en",
-    device=0 if device == "cuda" else -1,
-)
-en_to_pl = pipeline(
-    "translation",
-    model="gsarti/opus-mt-tc-en-pl",
-    device=0 if device == "cuda" else -1,
-)
 
+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)
+
+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():
+        translated = en_to_pl_model.generate(**inputs)
+    return en_to_pl_tokenizer.batch_decode(translated, skip_special_tokens=True)
 # ────────────────────── schemy Pydantic ────────────────────
 class Comment(BaseModel):
     text: str