| from spacy.lang.en import English | |
| import tensorflow as tf | |
| import streamlit as st | |
| st.title("Research Abstract Classifier") | |
| st.markdown(""" | |
| This app classifies study abstracts into different sections such as BACKGROUND, OBJECTIVE, METHODS, RESULTS, and CONCLUSIONS. | |
| Enter your study abstract in the text area below. | |
| """) | |
| st.link_button("Reserch Paper National Library of Medicine", "https://pubmed.ncbi.nlm.nih.gov/") | |
| abstext = st.text_area("Enter the study") | |
| def split_chars(text): | |
| return " ".join(list(text)) | |
| loaded_model = tf.keras.models.load_model( | |
| r'models/nmodelmain') | |
| if abstext: | |
| nlp = English() | |
| with st.spinner("Vectorizing..."): | |
| sentencizer = nlp.add_pipe("sentencizer") | |
| doc = nlp(abstext) | |
| abstract_lines = [str(sent) for sent in list(doc.sents)] | |
| total_lines_in_sample = len(abstract_lines) | |
| sample_lines = [] | |
| for i, line in enumerate(abstract_lines): | |
| sample_dict = {} | |
| sample_dict["text"] = str(line) | |
| sample_dict["line_number"] = i | |
| sample_dict["total_lines"] = total_lines_in_sample - 1 | |
| sample_lines.append(sample_dict) | |
| test_abstract_line_numbers = [line["line_number"] for line in sample_lines] | |
| test_abstract_line_numbers_one_hot = tf.one_hot( | |
| test_abstract_line_numbers, depth=15) | |
| test_abstract_total_lines = [line["total_lines"] for line in sample_lines] | |
| test_abstract_total_lines_one_hot = tf.one_hot( | |
| test_abstract_total_lines, depth=20) | |
| abstract_chars = [split_chars(sentence) for sentence in abstract_lines] | |
| with st.spinner("predicting......"): | |
| test_abstract_pred_probs = loaded_model.predict(x=(test_abstract_line_numbers_one_hot, | |
| test_abstract_total_lines_one_hot, | |
| tf.constant(abstract_lines), | |
| tf.constant(abstract_chars))) | |
| test_abstract_preds = tf.argmax(test_abstract_pred_probs, axis=1) | |
| with st.spinner("Catogerizing...."): | |
| numlab = [] | |
| for i in (test_abstract_preds): | |
| numlab.append(int(i)) | |
| classnames = ['BACKGROUND', 'CONCLUSIONS', 'METHODS', 'OBJECTIVE', 'RESULTS'] | |
| dictionary = {} | |
| for i in range(len(numlab)): | |
| class_name = classnames[numlab[i]] | |
| abstract_line = abstract_lines[i] | |
| if class_name in dictionary: | |
| dictionary[class_name].append(abstract_line) | |
| else: | |
| dictionary[class_name] = [abstract_line] | |
| ordered_dictionary = {} | |
| ordered_classnames = ['BACKGROUND', 'OBJECTIVE', | |
| 'METHODS', 'RESULTS', 'CONCLUSIONS'] | |
| for class_name in ordered_classnames: | |
| if class_name in dictionary: | |
| ordered_dictionary[class_name] = dictionary[class_name] | |
| lis = [] | |
| for class_name, lines in ordered_dictionary.items(): | |
| lis.append(class_name) | |
| st.subheader(class_name) | |
| for line in lines: | |
| lis.append(line) | |
| st.write(line) | |