sentiment_analysis_product_review_sentiment

from transformers import BertTokenizer, BertForSequenceClassification
import torch
import torch.nn.functional as F

model_name = "AventIQ-AI/sentiment_analysis_product_review_sentiment"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)
model.eval()

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = F.softmax(outputs.logits, dim=1)
        pred = torch.argmax(probs, dim=1).item()
        label_map = {0: "Negative", 1: "Neutral", 2: "Positive"}
        return f"Sentiment: {label_map[pred]} (Confidence: {probs[0][pred]:.2f})"

# Test predictions
print("\nTest Predictions:")
print(predict("We're thrilled to announce our latest update, packed with new features and performance improvements!"))