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import gradio as gr |
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import spaces |
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import numpy as np |
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import os |
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import time |
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import torch |
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from config import Config |
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from transformers import BertConfig, BertTokenizer, BertForSequenceClassification |
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def set_seed(seed): |
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np.random.seed(seed) |
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torch.manual_seed(seed) |
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torch.cuda.manual_seed_all(seed) |
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torch.backends.cudnn.deterministic = True |
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@spaces.GPU |
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def greet(inputStr): |
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set_seed(1) |
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config = Config("./data_12345") |
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tokenizer = BertTokenizer.from_pretrained("bert-base-chinese") |
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bert_config = BertConfig.from_pretrained("bert-base-chinese", num_labels=config.num_labels) |
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model = BertForSequenceClassification.from_pretrained("bert-base-chinese", |
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config=bert_config |
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) |
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model.to(config.device) |
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model.load_state_dict(torch.load(config.saved_model)) |
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model.eval() |
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inputs = tokenizer( |
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inputStr, |
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max_length=config.max_seq_len, |
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truncation="longest_first", |
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return_tensors="pt") |
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inputs = inputs.to(config.device) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs[0] |
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label = torch.max(logits.data, 1)[1].tolist() |
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print("Classification result:" + config.label_list[label[0]]) |
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return config.label_list[label[0]] |
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demo = gr.Interface(fn=greet, inputs="text", outputs="text") |
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demo.launch() |
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