Delete app-2.py

app-2.py

@@ -1,64 +0,0 @@
-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")
-
-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_)
-    
-#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("added_component")
-def added_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("added_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)
-