create app.py

app.py

@@ -0,0 +1,67 @@
+import spacy
+import gradio as gr
+from spacy.pipeline import EntityRuler
+from spacy import displacy
+import jsonlines
+nlp = spacy.load('en_core_web_sm')
+
+# Create list with entity labels from jsonl file
+with jsonlines.open("skill_patterns.jsonl") as f:
+    created_entities = [line['label'].upper() for line in f.iter()]
+    
+def extract_text_from_word(txt):
+    '''Opens en reads in a .doc or .docx file from path'''  
+    return txt.replace('\n', ' ').replace('\t', ' ').lower()
+
+def add_newruler_to_pipeline(skill_pattern_path):
+    '''Reads in all created patterns from a JSONL file and adds it to the pipeline after PARSER and before NER'''
+    
+#     new_ruler = EntityRuler(nlp).from_disk(skill_pattern_path)
+    ruler=nlp.add_pipe("entity_ruler",after='parser')
+    ruler.from_disk(skill_pattern_path)  # loads patterns only
+    
+def create_skill_set(doc):
+    '''Create a set of the extracted skill entities of a doc'''
+    
+    return set([ent.label_.upper()[6:] for ent in doc.ents if 'skill' in ent.label_.lower()])
+
+def create_skillset_dict(resume_names, resume_texts):
+    '''Create a dictionary containing a set of the extracted skills. Name is key, matching skillset is value'''
+    skillsets = [create_skill_set(resume_text) for resume_text in resume_texts]
+    return dict(zip(resume_names, skillsets))
+    
+def match_skills(vacature_set, cv_set, resume_name):
+    '''Get intersection of resume skills and job offer skills and return match percentage'''
+    
+    if len(vacature_set) < 1:
+        print('could not extract skills from job offer text')   
+    else:
+        pct_match = round(len(vacature_set.intersection(cv_set[resume_name])) / len(vacature_set) * 100, 0)
+        print(resume_name + " has a {}% skill match on this job offer".format(pct_match))
+        print('Required skills: {} '.format(vacature_set))
+        print('Matched skills: {} \n'.format(vacature_set.intersection(skillset_dict[resume_name])))
+        
+        return (resume_name, pct_match)
+    
+add_newruler_to_pipeline("skill_patterns.jsonl")   
+ 
+def match(CV,JD):
+    resume_texts=[]
+    resume_texts.append(nlp(CV))
+    resume_names=['ABHI']
+    skillset_dict = create_skillset_dict(resume_names, resume_texts)
+    jd_skillset = create_skill_set(nlp(JD))
+    match_pairs = [match_skills(jd_skillset, skillset_dict, name) for name in skillset_dict.keys()]
+    return match_pairs
+
+exp=["Who is steve jobs?","What is coldplay?","What is a turing test?","What is the most interesting thing about our universe?","What are the most beautiful places on earth?"]
+
+desc="This is a semantic search engine powered by SentenceTransformers (Nils_Reimers) with a retrieval and reranking system on Wikipedia corous. This will return the top 5 results. So Quest on with Transformers."
+
+inp1=gr.inputs.Textbox(lines=10, placeholder=None, default="", label="CV")
+inp2=gr.inputs.Textbox(lines=10, placeholder=None, default="", label="JD")
+
+out=gr.outputs.Textbox(type="auto",label="search results")
+
+iface = gr.Interface(fn=match, inputs=[inp1,inp2], outputs=[out],examples=[exp],article=desc,title="Neural Search Engine",theme="huggingface",layout='vertical')
+iface.launch()