import gradio as gr from imports import * from parse_info import * #os.system("apt-get install poppler-utils") 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 "Sentiment: " + pred_sent + "\n" + "Topic in sentence: " + ". ".join([i.capitalize() for i in pred_topic]) # str({"sentiment": pred_sent, "topic": pred_topic}) processor = transformers.AutoProcessor.from_pretrained("nam194/resume_parsing_layoutlmv3_large_custom_label", use_auth_token=True, apply_ocr=False) model = transformers.LayoutLMv3ForTokenClassification.from_pretrained("nam194/resume_parsing_layoutlmv3_large_custom_label").to(device) label_list = ['person_name', 'dob_key', 'dob_value', 'gender_key', 'gender_value', 'phonenumber_key', 'phonenumber_value', 'email_key', 'email_value', 'address_key', 'address_value', 'socical_address_value', 'education', 'education_name', 'education_time', 'experience', 'experience_name', 'experience_time', 'information', 'undefined', 'designation_key', 'designation_value', 'degree_key', 'degree_value', 'skill_key', 'skill_value'] id2label = {0: 'person_name', 1: 'dob_key', 2: 'dob_value', 3: 'gender_key', 4: 'gender_value', 5: 'phonenumber_key', 6: 'phonenumber_value', 7: 'email_key', 8: 'email_value', 9: 'address_key', 10: 'address_value', 11: 'socical_address_value', 12: 'education', 13: 'education_name', 14: 'education_time', 15: 'experience', 16: 'experience_name', 17: 'experience_time', 18: 'information', 19: 'undefined', 20: 'designation_key', 21: 'designation_value', 22: 'degree_key', 23: 'degree_value', 24: 'skill_key', 25: 'skill_value'} key_list = ["person_name","dob_value","gender_value","phonenumber_value","email_value","address_value", "socical_address_value","education_name","education_time","experience_name","experience_time", "designation_value","degree_value","skill_value"] label2id = {v: k for k, v in id2label.items()} def pred_resume(pdf_path) -> dict: global key_list, device result = {} for i in key_list: result[i] = [] DPI = 200/77 global label_list, id2label, label2id # read pdf, convert to img doc = fitz.open(pdf_path.name) num_pages = len(doc) images = pdf2image.convert_from_path(pdf_path.name) block_dict = {} # get all data in pdf page_num = 1 for page in doc: file_dict = page.get_text('dict') block = file_dict['blocks'] block_dict[page_num] = block page_num += 1 # predict each page in pdf for page_num, blocks in block_dict.items(): bboxes, words = [], [] # store bounding boxes, text in a page image = images[page_num-1] for block in blocks: if block['type'] == 0: for line in block['lines']: for span in line['spans']: xmin, ymin, xmax, ymax = [int(i)*DPI for i in list(span['bbox'])] text = unidecode(span['text']).strip() if text.replace(" ","") != "": bboxes.append(normalize_bbox([xmin, ymin, xmax, ymax], image.size)) words.append(decontracted(text)) fake_label = ["O"] * len(words) encoding = processor(image, words, boxes=bboxes, word_labels=fake_label, truncation=True, stride=256, padding="max_length", max_length=512, return_overflowing_tokens=True, return_offsets_mapping=True) labels = encoding["labels"] offset_mapping = encoding.pop('offset_mapping') overflow_to_sample_mapping = encoding.pop('overflow_to_sample_mapping') encoding = {k: torch.tensor(v) for k,v in encoding.items() if k != "labels"} x = [] for i in range(0, len(encoding['pixel_values'])): x.append(encoding['pixel_values'][i]) x = torch.stack(x) encoding['pixel_values'] = x # forawrd to model with torch.no_grad(): outputs = model(**{k: v.to(device) for k,v in encoding.items() if k != "labels"}) # process output predictions = outputs["logits"].argmax(-1).squeeze().tolist() if outputs["logits"].shape[0] > 1: for i, label in enumerate(labels): if i>0: labels[i] = labels[i][256:] predictions[i] = predictions[i][256:] predictions = [j for i in predictions for j in i] labels = [j for i in labels for j in i] true_predictions = [id2label[pred] for pred, label in zip(predictions, labels) if label != -100] for i, pred in enumerate(true_predictions): if pred in key_list: result[pred].append(words[i]) return str(result) def norm(result: str) -> str: result = ast.literal_eval(result) result["person_name"] = " ".join([parse_string(i).capitalize() for i in " ".join(result["person_name"]).split()]) result["email_value"] = parse_email(result["email_value"]) result["phonenumber_value"] = "".join([i for i in "".join(result["phonenumber_value"]) if i.isdigit()]) result["address_value"] = parse_address(result["address_value"]) result["designation_value"] = parse_designation(result["designation_value"]) result["experience_time"] = parse_time(result["experience_time"]) result["gender_value"] = parse_gender(result["gender_value"]) result["skill_value"] = parse_skill(result["skill_value"]) result["education_name"] = parse_designation(result["education_name"]) result["experience_name"] = parse_designation(result["experience_name"]) for k, v in result.items(): if isinstance(v, list): result[k] = ". ".join([i for i in result[k]]) if isinstance(v, int) or isinstance(v, float): result[k] = str(result[k]) return "Tên: "+result["person_name"]+"\n"+"Ngày sinh: "+result["dob_value"]+"\n"+"Giới tính: "+result["gender_value"]+"\n"+"Chức danh: "+result["designation_value"]+"\n"+"Số điện thoại: "+result["phonenumber_value"]+"\n"+"Email: "+result["email_value"]+"\n"+"Địa chỉ: "+result["address_value"]+"\n"+"Tên công ty/công việc: "+result["experience_name"]+"\n"+"Tên trường học: "+result["education_name"]+"\n"+"Kỹ năng: "+result["skill_value"]+"\n"+"Năm kinh nghiệm: "+result["experience_time"] with gr.Blocks() as demo: gr.Markdown("DEMO PROJECTS: REVIEW ANALYSIS AND EXTRACT INFOMATION FROM RESUME") with gr.Tab("Review analysis"): text_input = gr.Textbox(label="Input sentence (ex: Sếp tốt, bảo hiểm đóng full lương bảo hiểm cho nhân viên. Hàng năm tăng lương ổn OT không trả thêm tiền, chỉ cho ngày nghỉ và hỗ trợ ăn tối.):", placeholder="input here...") text_output = gr.Textbox(label="Result:") text_button = gr.Button("Predict") with gr.Tab("Extract infomation from resume"): with gr.Column(): file_input = gr.File(label="Upload pdf", file_types=[".pdf"]) with gr.Column(): cv_output = gr.Textbox(label="Information fields") resume_button = gr.Button("Extract") with gr.Column(): normalize_output = gr.Textbox(label="Normalize by rule-based:") normalize_button = gr.Button("Normailze") # with gr.Accordion("Open for More!"): # gr.Markdown("Look at me...") text_button.click(sentiment, inputs=text_input, outputs=text_output) resume_button.click(pred_resume, inputs=file_input, outputs=cv_output) normalize_button.click(norm, inputs=cv_output, outputs=normalize_output) demo.launch()