| from transformers import pipeline, AutoTokenizer | |
| # Load model | |
| model_name = "DilipKY/my-text-classifier" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| classifier = pipeline("text-classification", model=model_name, tokenizer=tokenizer) | |
| # Label mapping (adjust if necessary) | |
| label_map = {"LABEL_0": "NEGATIVE", "LABEL_1": "POSITIVE"} | |
| # Test with input text | |
| sample_text = "I love this movie!" | |
| result = classifier(sample_text) | |
| # Convert label | |
| result[0]['label'] = label_map.get(result[0]['label'], result[0]['label']) | |
| # Print the result | |
| print("\n🔍 Sentiment Classification Result:") | |
| print(result) | |