| import pickle | |
| import numpy as np | |
| import pandas as pd | |
| import tensorflow as tf | |
| from tensorflow.keras.layers.experimental.preprocessing import TextVectorization | |
| import tensorflow_hub as hub | |
| from sklearn.preprocessing import LabelEncoder | |
| from spacy.lang.en import English | |
| import streamlit as st | |
| file7 = open('pub_text_vectorizer.pkl', 'rb') | |
| pre = pickle.load(file7) | |
| new_v = TextVectorization.from_config(pre['config']) | |
| new_v.set_weights(pre['weights']) | |
| file7.close() | |
| file8 = open('pub_label_encoder.pkl', 'rb') | |
| label_encoder = pickle.load(file8) | |
| file8.close() | |
| new_model = tf.keras.models.load_model('pubmed_model.h5', | |
| custom_objects={'KerasLayer': hub.KerasLayer}) | |
| st.title('Medical Abstract Reader') | |
| text = st.text_area('Classify medical abstract into various categories.', height=600, key='text') | |
| submit = st.button('Predict') | |
| def clear_text(): | |
| st.session_state["text"] = "" | |
| clear = st.button("Clear text input", on_click=clear_text) | |
| if submit: | |
| if text is not None: | |
| nlp = English() | |
| sentencizer = nlp.add_pipe("sentencizer") | |
| doc = nlp(text) | |
| abstract_lines = [str(sent) for sent in list(doc.sents)] | |
| sample_lines = [{ | |
| "text": str(line), | |
| "line_number": i, | |
| "total_lines": len(abstract_lines) - 1 | |
| } for i, line in enumerate(abstract_lines)] | |
| test = pd.DataFrame(sample_lines) | |
| testing_sentences = test['text'].tolist() | |
| new_v.adapt(testing_sentences) | |
| testing_dataset = tf.data.Dataset.from_tensor_slices((testing_sentences)) | |
| testing_dataset = testing_dataset.batch(32).prefetch(tf.data.AUTOTUNE) | |
| new_model_probs = new_model.predict(testing_dataset) | |
| new_model_preds = tf.argmax(new_model_probs, axis=1) | |
| test_pred_classes = [label_encoder.classes_[pred] for pred in new_model_preds] | |
| test["prediction"] = test_pred_classes | |
| test["pred_prob"] = tf.reduce_max(new_model_probs, axis=1).numpy() | |
| for i, line in enumerate(abstract_lines): | |
| st.write(f'{test_pred_classes[i]} : {line}') | |