from transformers import GPT2Tokenizer, GPT2ForSequenceClassification
import torch

model_name = "ebrukilic/absa-gpt-2"
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token
model = GPT2ForSequenceClassification.from_pretrained(model_name)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def predict_sentiment(text, aspect, model, tokenizer):
    inputs = tokenizer.encode_plus(
        text=f"{aspect} hakkında: {text}",
        add_special_tokens=True,
        max_length=128,
        padding="max_length",
        truncation=True,
        return_attention_mask=True,
        return_tensors="pt"
    )

    input_ids = inputs["input_ids"].to(device)
    attention_mask = inputs["attention_mask"].to(device)
    with torch.no_grad():
        outputs = model(input_ids, attention_mask=attention_mask)

    predicted_class = torch.argmax(outputs.logits, dim=1).item()
    return predicted_class

new_text = "Ürünün kumaşı çok güzel, bayıldım!"
aspect = "kumaş"
predicted_sentiment = predict_sentiment(new_text, aspect, model, tokenizer)

print("Yorum:", new_text)
print("Aspect:", aspect)
print("Tahmin Edilen Duygu:", predicted_sentiment)