import numpy as np import gradio as gr from imports import * from huggingface_hub import login login(token="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") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") dict_ = { 0: "negative", 1: "positive", 2: "neutral"} tokenizer_sent = AutoTokenizer.from_pretrained("nam194/sentiment", use_fast=False) model_sent = AutoModelForSequenceClassification.from_pretrained("nam194/sentiment", num_labels=3, use_auth_token=True).to(device) def cvt2cls(data): data = list(set(data)) try: data.remove(20) except: pass for i, num in enumerate(data): if num == 20: continue if num>=10: data[i] -= 10 return data ner_tags = {0: 'B-chỗ để xe', 1: 'B-con người', 2: 'B-công việc', 3: 'B-cơ sở vật chất', 4: 'B-dự án', 5: 'B-lương', 6: 'B-môi trường làm việc', 7: 'B-ot/thời gian', 8: 'B-văn phòng', 9: 'B-đãi ngộ', 10: 'I-chỗ để xe', 11: 'I-con người', 12: 'I-công việc', 13: 'I-cơ sở vật chất', 14: 'I-dự án', 15: 'I-lương', 16: 'I-môi trường làm việc', 17: 'I-ot/thời gian', 18: 'I-văn phòng', 19: 'I-đãi ngộ', 20: 'O'} topic_tags = {0: 'chỗ để xe', 1: 'con người', 2: 'công việc', 3: 'cơ sở vật chất', 4: 'dự án', 5: 'lương', 6: 'môi trường làm việc', 7: 'ot/thời gian', 8: 'văn phòng', 9: 'đãi ngộ'} config = RobertaConfig.from_pretrained("nam194/ner", num_labels=21) tokenizer_topic = AutoTokenizer.from_pretrained("nam194/ner", use_fast=False) model_topic = PhoBertLstmCrf.from_pretrained("nam194/ner", config=config, from_tf=False).to(device) model_topic.resize_token_embeddings(len(tokenizer_topic)) def sentiment(sent: str): sent_ = normalize(text=sent) # segment input sentence, maybe raise ConnectionError: HTTPConnectionPool()) input_sent = torch.tensor([tokenizer_sent.encode(sent_)]).to(device) with torch.no_grad(): out_sent = model_sent(input_sent) logits_sent = out_sent.logits.softmax(dim=-1).tolist()[0] pred_sent = dict_[np.argmax(logits_sent)] sent = replace_all(text=sent) # segment input sentence, maybe raise ConnectionError: HTTPConnectionPool()) sent_segment = sent.split(".") for i, s in enumerate(sent_segment): s = s.strip() sent_segment[i] = underthesea.word_tokenize(s, format="text").split() dump = [[i, 'O'] for s in sent_segment for i in s] dump_set = NerDataset(feature_for_phobert([dump], tokenizer=tokenizer_topic, use_crf=True)) dump_iter = DataLoader(dump_set, batch_size=1) with torch.no_grad(): for idx, batch in enumerate(dump_iter): batch = { k:v.to(device) for k, v in batch.items() } outputs = model_topic(**batch) pred_topic = list(set([topic_tags[i] for i in cvt2cls(outputs["tags"][0])])) return str({"sentiment": pred_sent, "topic": pred_topic}) def flip_image(x): return np.fliplr(x) with gr.Blocks() as demo: gr.Markdown("Demo projects Review Company and Resume parser phase 1.") with gr.Tab("Review Company"): text_input = gr.Textbox(label="Input sentence:", placeholder="input here...") text_output = gr.Textbox(label="Result:") text_button = gr.Button("Predict") with gr.Tab("Extract infomation from resume"): with gr.Row(): image_input = gr.Image() image_output = gr.Image() image_button = gr.Button("Predict") # with gr.Accordion("Open for More!"): # gr.Markdown("Look at me...") text_button.click(sentiment, inputs=text_input, outputs=text_output) image_button.click(flip_image, inputs=image_input, outputs=image_output) demo.launch()