| import gradio as gr | |
| import numpy as np | |
| import torch.nn.functional as F | |
| from model import * | |
| from transformers import BertTokenizer | |
| DEVICE = torch.device('cpu') | |
| PATH = 'checkpoints/' | |
| model = torch.load(PATH + 'model.pt', map_location=DEVICE) | |
| model.load_state_dict(torch.load(PATH + 'model_state_dict.pt', | |
| map_location=DEVICE)) | |
| model.eval() | |
| tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') | |
| def evaluate(text) : | |
| encoding = tokenizer.encode_plus( | |
| text, max_length=150, | |
| padding='max_length', | |
| truncation=True, | |
| add_special_tokens=True, | |
| return_token_type_ids=False, | |
| return_attention_mask=True, | |
| return_tensors='pt' | |
| ) | |
| input_id = encoding['input_ids'] | |
| attention_mask = encoding['attention_mask'] | |
| result = F.softmax(model(input_id, attention_mask), dim=1) | |
| with torch.no_grad() : | |
| result = np.round(result.numpy(), 2).tolist() | |
| return {'Liberal': result[0][0], 'Conservative': result[0][1]} | |
| iface = gr.Interface(fn=evaluate, | |
| inputs='text', | |
| outputs=gr.components.Label(num_top_classes=2), | |
| examples=[["Biden speech draws 38.2 million U.S. TV viewers"], | |
| ["Biden's first State of the Union address in 67 seconds"]], | |
| title='Political Sentiment Classification Using BERT Transformer') | |
| iface.launch() |