Create app.py

app.py

@@ -0,0 +1,152 @@
+import gradio as gr
+import spacy
+from spacy.pipeline import EntityRuler
+from spacy.language import Language
+from spacy.matcher import PhraseMatcher
+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)
+
+#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_))
+
+#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)
+  
+#patterns = list(nlp.pipe(pattern_list))
+#print("patterns:", patterns)
+
+#Instantiate PhraseMatcher
+#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)
+
+# 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)
+
+
+#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])
+
+#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)
+
+iface = gr.Interface(
+    process_text,
+    [gr.inputs.Textbox(lines=10, default="The coronavirus disease 2019 (COVID-19) pandemic is the result of widespread infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).", label="Text to Run through Entity Recognition")],
+    
+    entities,
+    [gr.inputs.Textbox(lines=3, default= ("Coronavirus, coronavirus, COVID-19, SARS-CoV-2, SARS‐CoV‐2"), label="Enter entity references")],
+    
+    run,
+    [gr.inputs.Textbox(lines=1, default= ("COVID"), label="Enter entity label")],
+    gr.outputs.HighlightedText(),
+)
+
+     test,
+    [gr.inputs.Textbox(lines=1, default= ("The tissue distribution of the virus-targeted receptor protein, angiotensin converting enzyme II (ACE2), determines which organs will be attacked by SARS‐CoV‐2."), label="Test: Enter new sentence containing named entity")],
+    gr.outputs.HighlightedText(),
+)
+iface.launch()