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| import gradio as gr | |
| import tensorflow as tf | |
| def split_char(text): | |
| return " ".join(list(text)) | |
| # Unzipping model | |
| import zipfile | |
| zip_ref = zipfile.ZipFile("Universal_sentence_encoder_Tribrid_embedding_model-20220920T213455Z-001.zip", "r") | |
| zip_ref.extractall() | |
| zip_ref.close() | |
| from spacy.lang.en import English | |
| def make_predictions(Input): | |
| class_names=['BACKGROUND','CONCLUSIONS','METHODS','OBJECTIVE','RESULTS'] | |
| # setup English sentence parser | |
| nlp = English() | |
| # create & add sentence splitting pipeline object to sentence parser | |
| nlp.add_pipe('sentencizer') | |
| # create "doc" of parsed sequences | |
| doc=nlp(Input) | |
| # Create a list | |
| sents_list = [] | |
| for sent in doc.sents: | |
| sents_list.append(sent.text) | |
| abstract_sentences=sents_list | |
| # Creating a loop to go through each line in abstract and create a list of dictionaries containing festure for each lines | |
| sample_line=[] | |
| for i,line in enumerate(abstract_sentences): | |
| sample_dict={} | |
| sample_dict["text"]=str(line) | |
| sample_dict["line_number"]=i | |
| sample_dict["total_lines"]=len(abstract_sentences)-1 | |
| sample_line.append(sample_dict) | |
| # Get all the line_number values from sample abstract | |
| abstract_line_number=[line["line_number"] for line in sample_line] | |
| # one-hot encoding line_number values | |
| abstract_line_number_one_hot=tf.one_hot(abstract_line_number,depth=15) | |
| # Getting all total_number values from sample abstract | |
| abstract_total_lines=[line["total_lines"] for line in sample_line] | |
| # One-hot encoding total_number lines | |
| abstract_total_lines_one_hot=tf.one_hot(abstract_total_lines,depth=20) | |
| # Splitting abstract lines into character | |
| abstract_char=[split_char(sentence) for sentence in abstract_sentences] | |
| #abstract_char=[" ".join(list((sentence) for sentence in abstract_sentences))] | |
| # Loading in model and getting a summary of loaded model | |
| #skimlit_universal_sentence_encoder_model=tf.keras.models.load_model("/content/drive/MyDrive/skimlit_models/Universal_sentence_encoder_Tribrid_embedding_model") | |
| skimlit_universal_sentence_encoder_model=tf.keras.models.load_model("Universal_sentence_encoder_Tribrid_embedding_model-20220920T213455Z-001") | |
| # Making prediction with loaded model on sample abstract | |
| abstract_pred_probs=skimlit_universal_sentence_encoder_model.predict(x=(abstract_line_number_one_hot, | |
| abstract_total_lines_one_hot, | |
| tf.constant(abstract_sentences), | |
| tf.constant(abstract_char))) | |
| # Turning model's prediction into labels | |
| abstract_preds=tf.argmax(abstract_pred_probs,axis=1) | |
| # Turn predicted labels into string class names | |
| predicted_classes=[class_names[i] for i in abstract_preds] | |
| # Visualizing abstract lines and predicted labels | |
| summary="" | |
| for i,line in enumerate(abstract_sentences): | |
| summary=summary+f"{predicted_classes[i]}: {line}\n" | |
| #summary=f"{predicted_classes[i]}: {line}" | |
| return summary | |
| demo = gr.Interface(fn=make_predictions,inputs=gr.Textbox(lines=2, placeholder="Enter Abstract Here..."),outputs="text") | |
| demo.launch(debug=True, inline=True) |