Update app.py

app.py

@@ -7,131 +7,63 @@ from spacy.tokens import Span
 
 nlp = spacy.load("en_core_web_md")
 
-#Text 1
-def process_text(text1):
-  d = load(text1)
-  return [
-  (for ent in doc1.ents:
-    print(ent.text, ent.label_))
-  (for ent in doc1.ents:
-    print(ent.label_, spacy.explain(ent.label_)))
-    ]
-
-def load(text):
-  user_input = str(text.strip())
-  doc1 = nlp(user_input)
-
-#Text 2
-def entities(text2):
-  a = named_ents(text2)
-  return [print("patterns:", patterns)]
-  
-def named_ents(text):
-  pattern_list = []
-  for i in text.strip().split():
-    pattern_list.append(i)
-  
-  patterns = list(nlp.pipe(pattern_list))
-
-#Text 3  
-def run(text3):
-  b = pipe(text3)
-  return [
-  doc
-  print(nlp.pipe_names)]
-
-def pipe(text):
-  matcher = PhraseMatcher(nlp.vocab)
-  #Create label for pattern
-  user_named = str(text.strip()) #gradio text box here to enter pattern label
-  matcher.add(user_named, patterns)
-  # Define the custom component
-  @Language.component("covid_component")
-  def covid_component_function(doc):
-  # Apply the matcher to the doc
-    matches = matcher(doc)
-    # Create a Span for each match and assign the label "ANIMAL"
-    spans = [Span(doc, start, end, label=user_named) for match_id, start, end in matches]
-    # Overwrite the doc.ents with the matched spans
-    doc.ents = spans
-    return doc
-   # Add the component to the pipeline after the "ner" component
-  nlp.add_pipe((user_named + "component"), after="ner")
-  print(nlp.pipe_names)
-  
-#Text 4
-
-def test(text4):
-  c = new_sample(text4)
-  return [
-    print([(ent.text, ent.label_) for ent in apply_doc.ents])
-    Counter(labels)]
-    
 
-def new_sample(text):
-  user_doc = str(text).strip())
-  apply_doc = nlp(user_doc)
-  print([(ent.text, ent.label_) for ent in apply_doc.ents])
-  #Count total mentions of label COVID in the 3rd document
-  from collections import Counter
-  labels = [ent.label_ for ent in apply_doc.ents]
-  Counter(labels)
 
 
-#user_input = input(str("")) #gradio text box here to enter sample text
-#doc1 = nlp(user_input)
+user_input = input(str("")) 
+doc1 = nlp(user_input)
 
-#print list of entities captured by pertained model
-#for ent in doc1.ents:
-    #print(ent.text, ent.label_)
+print list of entities captured by pertained model
+for ent in doc1.ents:
+    print(ent.text, ent.label_)
     
-#inspect labels and their meaning
-#for ent in doc1.ents:
-    #print(ent.label_, spacy.explain(ent.label_))
+inspect labels and their meaning
+for ent in doc1.ents:
+    print(ent.label_, spacy.explain(ent.label_))
 
-#Use PhraseMatcher to find all references of interest
-#Define the different references to Covid
-#user_entries = input(str("")) #gradio text box here to enter sample terms
-#pattern_list = []
+Use PhraseMatcher to find all references of interest
+Define the different references to Covid
+user_entries = input(str("")) #gradio text box here to enter sample terms
+pattern_list = []
 
-#for i in user_entries.strip().split():
-#   pattern_list.append(i)
+for i in user_entries.strip().split():
+   pattern_list.append(i)
   
-#patterns = list(nlp.pipe(pattern_list))
-#print("patterns:", patterns)
+patterns = list(nlp.pipe(pattern_list))
+print("patterns:", patterns)
 
 #Instantiate PhraseMatcher
-#matcher = PhraseMatcher(nlp.vocab)
+matcher = PhraseMatcher(nlp.vocab)
 
 #Create label for pattern
-#user_named = input(str("").strip()) #gradio text box here to enter pattern label
-#matcher.add(user_named, patterns)
+user_named = input(str("").strip()) #gradio text box here to enter pattern label
+matcher.add(user_named, patterns)
 
 # Define the custom component
-#@Language.component("covid_component")
-#def covid_component_function(doc):
-    # Apply the matcher to the doc
-#    matches = matcher(doc)
-    # Create a Span for each match and assign the label "ANIMAL"
-#    spans = [Span(doc, start, end, label=user_named) for match_id, start, end in matches]
-    # Overwrite the doc.ents with the matched spans
-#    doc.ents = spans
-#    return doc
+@Language.component("covid_component")
+def covid_component_function(doc):
+  #Apply the matcher to the doc
+  matches = matcher(doc)
+  #Create a Span for each match and assign the label
+  spans = [Span(doc, start, end, label=user_named) for match_id, start, end in matches]
+  # Overwrite the doc.ents with the matched spans
+  doc.ents = spans
+  return doc
 
 # Add the component to the pipeline after the "ner" component
-#nlp.add_pipe((user_named + "component"), after="ner")
-#print(nlp.pipe_names)
+nlp.add_pipe((user_named + "component"), after="ner")
+print(nlp.pipe_names)
 
 
 #Verify that your model now detects all specified mentions of Covid on another text
-#user_doc = input(str("").strip())
-#apply_doc = nlp(user_doc)
-#print([(ent.text, ent.label_) for ent in apply_doc.ents])
+user_doc = input(str("").strip())
+apply_doc = nlp(user_doc)
+print([(ent.text, ent.label_) for ent in apply_doc.ents])
 
 #Count total mentions of label COVID in the 3rd document
-#from collections import Counter
-#labels = [ent.label_ for ent in apply_doc.ents]
-#Counter(labels)
+from collections import Counter
+labels = [ent.label_ for ent in apply_doc.ents]
+Counter(labels)
 
 iface = gr.Interface(
     process_text,