app.py

import torch
from transformers import AutoTokenizer, AutoModel

# Modell & Tokenizer laden
model_name = "allenai/scibert_scivocab_uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Explizit auf CPU setzen
device = torch.device("cpu")
model.to(device)

# Beispiel-Text für den Test
text = "This paper introduces a novel deep learning approach for cancer diagnosis."

# Tokenisierung & Modell-Durchlauf
inputs = tokenizer(text, return_tensors="pt").to(device)
outputs = model(**inputs)

print("Modell erfolgreich geladen!")
print("Shape der Ausgabe:", outputs.last_hidden_state.shape)