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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import os
model_name = "fine-tuned-model"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_safetensors=True)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
device = torch.device("cpu")
def predict_sentiment(review_text):
inputs = tokenizer(review_text, padding=True, truncation=True, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predictions = torch.softmax(logits, dim=-1)
predicted_label = torch.argmax(predictions, dim=-1).item()
sentiment = "Positive" if predicted_label == 1 else "Negative"
return sentiment, predictions[0].cpu().numpy()
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