| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") | |
| model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased") | |
| def classify_text(text): | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| predicted_class = torch.argmax(logits, dim=1).item() | |
| return f"Predicted class: {predicted_class}" | |
| import gradio as gr | |
| interface = gr.Interface( | |
| fn=classify_text, | |
| inputs="text", | |
| outputs="text", | |
| title="BERT Text Classifier" | |
| ) | |
| interface.launch() | |