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| from src.Vocabulary import Vocabulary | |
| from src.RNNmodel import RNN | |
| import torch | |
| import gradio as gr | |
| import torchtext.vocab as vocab1 | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| word_embedding = vocab1.Vectors(name = "word2vec.txt", | |
| unk_init = torch.Tensor.normal_) | |
| vocab = Vocabulary() | |
| words_list = list(word_embedding.stoi.keys()) | |
| for word in words_list: | |
| vocab.add(word) | |
| INPUT_DIM = word_embedding.vectors.shape[0] | |
| EMBEDDING_DIM = 100 | |
| HIDDEN_DIM = 256 | |
| N_LAYERS = 2 | |
| BIDIRECTIONAL = True | |
| DROPOUT = 0.3 | |
| PAD_IDX = vocab["<pad>"] | |
| UNK_IDX = vocab["<unk>"] | |
| model = RNN(INPUT_DIM, | |
| EMBEDDING_DIM, | |
| HIDDEN_DIM, | |
| N_LAYERS, | |
| BIDIRECTIONAL, | |
| DROPOUT, | |
| PAD_IDX) | |
| model.embedding.weight.data.copy_(word_embedding.vectors) | |
| model.embedding.weight.data[UNK_IDX] = torch.zeros(EMBEDDING_DIM) | |
| model.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM) | |
| model.to(device) | |
| model.load_state_dict(torch.load('Best_model_RNN.pt', map_location=torch.device('cpu'))) | |
| def predict_sentiment(model, sentence, vocab, device): | |
| model.eval() | |
| corpus = [sentence] | |
| tensor = vocab.corpus_to_tensor(corpus)[0].to(device) | |
| tensor = tensor.unsqueeze(1) | |
| length = [len(tensor)] | |
| length_tensor = torch.LongTensor(length) | |
| prediction = torch.sigmoid(model(tensor, length_tensor)) | |
| return prediction.item() | |
| def predict(text): | |
| pre = predict_sentiment(model, text, vocab, device) | |
| return {'Positive':(pre), | |
| "Negative": (1- (pre))} | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=gr.components.Textbox(label='Nhập comment'), | |
| outputs=gr.components.Label(label='Prediction'), | |
| allow_flagging='never' | |
| ) | |
| def run(): | |
| demo.launch(share=True) | |
| if __name__ == "__main__": | |
| run() | |