| from transformers import AutoTokenizer | |
| from transformers import AutoModelForSequenceClassification | |
| import torch | |
| import gradio as gr | |
| import numpy as np | |
| labels = ['Not Acceptale', "Acceptable"] | |
| model_name = "abdulmatinomotoso/English_Grammar_Checker" | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| def check_grammar(sentence): | |
| input_tensor = tokenizer.encode(sentence, return_tensors="pt") | |
| logits = model(input_tensor).logits | |
| softmax = torch.nn.Softmax(dim=1) | |
| probs = softmax(logits)[0] | |
| probs = probs.cpu().detach().numpy() | |
| max_index = np.argmax(probs) | |
| result = labels[max_index] | |
| return result | |
| demo = gr.Interface(check_grammar, inputs=['text'], | |
| outputs="text", | |
| title = "English Grammar Checker") | |
| if __name__ == "__main__": | |
| demo.launch(debug=True) |