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app.py

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+import gradio as gr
+import tensorflow as tf
+
+def split_char(text):
+  return " ".join(list(text))
+  
+from spacy.lang.en import English
+def make_predictions(Your_Abstract):
+  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(Your_Abstract)
+
+  # 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")
+
+  # 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="Abstract Here..."),outputs="text")
+demo.launch(debug=True, inline=True)