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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}')
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