import streamlit as st from spacy import displacy from Model.NER.VLSP2021.Predict_Ner import ViTagger import re from thunghiemxuly import save_uploaded_image,convert_text_to_txt,add_string_to_txt import os from transformers import AutoTokenizer, BertConfig from Model.MultimodelNER.VLSP2021.train_umt_2021 import load_model,predict from Model.MultimodelNER.Ner_processing import format_predictions,process_predictions,combine_entities,remove_B_prefix,combine_i_tags from Model.MultimodelNER.predict import get_test_examples_predict from Model.MultimodelNER import resnet as resnet from Model.MultimodelNER.resnet_utils import myResnet import torch import numpy as np from Model.MultimodelNER.VLSP2021.dataset_roberta import MNERProcessor_2021 CONFIG_NAME = 'bert_config.json' WEIGHTS_NAME = 'pytorch_model.bin' device = torch.device("cuda" if torch.cuda.is_available() else "cpu") net = getattr(resnet, 'resnet152')() net.load_state_dict(torch.load(os.path.join('Model/Resnet/', 'resnet152.pth'))) encoder = myResnet(net, True, device) def process_text(text): # Loại bỏ dấu cách thừa và dấu cách ở đầu và cuối văn bản processed_text = re.sub(r'\s+', ' ', text.strip()) return processed_text def show_mner_2021(): multimodal_text = st.text_area("Enter your text for MNER:", height=300) multimodal_text = process_text(multimodal_text) # Xử lý văn bản image = st.file_uploader("Upload an image (only jpg):", type=["jpg"]) if st.button("Process Multimodal NER"): save_image = 'Model/MultimodelNER/VLSP2021/Image' save_txt = 'Model/MultimodelNER/VLSP2021/Filetxt/test.txt' image_name = image.name save_uploaded_image(image, save_image) convert_text_to_txt(multimodal_text, save_txt) add_string_to_txt(image_name, save_txt) st.image(image, caption="Uploaded Image", use_column_width=True) bert_model = 'vinai/phobert-base-v2' output_dir = 'Model/MultimodelNER/VLSP2021/best_model' output_model_file = os.path.join(output_dir, WEIGHTS_NAME) output_encoder_file = os.path.join(output_dir, "pytorch_encoder.bin") processor = MNERProcessor_2021() label_list = processor.get_labels() auxlabel_list = processor.get_auxlabels() num_labels = len(label_list) + 1 auxnum_labels = len(auxlabel_list) + 1 trans_matrix = np.zeros((auxnum_labels, num_labels), dtype=float) trans_matrix[0, 0] = 1 # pad to pad trans_matrix[1, 1] = 1 # O to O trans_matrix[2, 2] = 0.25 # B to B-MISC trans_matrix[2, 4] = 0.25 # B to B-PER trans_matrix[2, 6] = 0.25 # B to B-ORG trans_matrix[2, 8] = 0.25 # B to B-LOC trans_matrix[3, 3] = 0.25 # I to I-MISC trans_matrix[3, 5] = 0.25 # I to I-PER trans_matrix[3, 7] = 0.25 # I to I-ORG trans_matrix[3, 9] = 0.25 # I to I-LOC trans_matrix[4, 10] = 1 # X to X trans_matrix[5, 11] = 1 # [CLS] to [CLS] trans_matrix[6, 12] = 1 tokenizer = AutoTokenizer.from_pretrained(bert_model, do_lower_case=False) model_umt, encoder_umt = load_model(output_model_file, output_encoder_file, encoder, num_labels, auxnum_labels) eval_examples = get_test_examples_predict( 'Model/MultimodelNER/VLSP2021/Filetxt/') y_pred, a = predict(model_umt, encoder_umt, eval_examples, tokenizer, device, save_image, trans_matrix) formatted_output = format_predictions(a, y_pred[0]) final = process_predictions(formatted_output) final2 = combine_entities(final) final3 = remove_B_prefix(final2) final4 = combine_i_tags(final3) words_and_labels = final4 # Tạo danh sách từ words = [word for word, _ in words_and_labels] # Tạo danh sách thực thể và nhãn cho mỗi từ, loại bỏ nhãn 'O' entities = [{'start': sum(len(word) + 1 for word, _ in words_and_labels[:i]), 'end': sum(len(word) + 1 for word, _ in words_and_labels[:i + 1]), 'label': label} for i, (word, label) in enumerate(words_and_labels) if label != 'O'] # print(entities) # Render the visualization without color for 'O' labels html = displacy.render( {"text": " ".join(words), "ents": entities, "title": None}, style="ent", manual=True, options={"colors": {"DATETIME-DATERANGE": "#66c2ff", "LOCATION-GPE": "#ffcc99", "O": None, # Màu cho nhãn 'O' "QUANTITY-NUM": "#ffdf80", "EVENT-CUL": "#bfbfbf", "DATETIME": "#80ff80", "PERSONTYPE": "#ff80ff", "PERSON": "#bf80ff", "QUANTITY-PER": "#80cccc", "ORGANIZATION": "#ff6666", "LOCATION-GEO": "#66cc66", "LOCATION-STRUC": "#cccc66", "PRODUCT-COM": "#ffff66", "DATETIME-DATE": "#66cccc", "QUANTITY-DIM": "#6666ff", "PRODUCT": "#cc6666", "QUANTITY": "#6666cc", "DATETIME-DURATION": "#9966ff", "QUANTITY-CUR": "#ff9966", "DATETIME-TIME": "#cdbf93", "QUANTITY-TEM": "#cc9966", "DATETIME-TIMERANGE": "#cc8566", "EVENT-GAMESHOW": "#8c8c5a", "QUANTITY-AGE": "#70db70", "QUANTITY-ORD": "#e699ff", "PRODUCT-LEGAL": "#806699", "LOCATION": "#993366", "ORGANIZATION-MED": "#339933", "URL": "#ff4d4d", "PHONENUMBER": "#99cc99", "ORGANIZATION-SPORTS": "#6666ff", "EVENT-SPORT": "#ffff80", "SKILL": "#b38f66", "EVENT-NATURAL": "#ff9966", "ADDRESS": "#cc9966", "IP": "#b38f66", "EMAIL": "#cc8566", "ORGANIZATION-STOCK": "#666633", "DATETIME-SET": "#70db70", "PRODUCT-AWARD": "#e699ff", "MISCELLANEOUS": "#806699", "LOCATION-GPE-GEO": "#99ffff"}} ) # print(html) st.markdown(html, unsafe_allow_html=True) # Sử dụng widget st.html để hiển thị HTML # Hiển thị văn bản đã nhập # st.write("Văn bản đã nhập:", text) ###Ví dụ 1 : Một trận hỗn chiến đã xảy ra tại trận đấu khúc côn cầu giữa Penguins và Islanders ở Mỹ (image:penguin)