Initial commit

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

__init__.py

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+# Importing flask module in the project is mandatory 
+# An object of Flask class is our WSGI application. 
+from flask import Flask, request
+  
+# Flask constructor takes the name of  
+# current module (__name__) as argument. 
+app = Flask(__name__) 
+  
+# The route() function of the Flask class is a decorator,  
+# which tells the application which URL should call  
+# the associated function. 
+@app.route('/') 
+# ‘/’ URL is bound with hello_world() function. 
+def hello_world(): 
+    return 'Hello World'
+
+
+@app.route('/resume/score', methods=['GET'])
+def get_resume_score():
+    print(request.data)
+    return request.data
+  
+# main driver function 
+if __name__ == '__main__': 
+  
+    # run() method of Flask class runs the application  
+    # on the local development server. 
+    app.run() 
+
+

main.py

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+import pdfreader
+import textdistance
+from pdfreader import PDFDocument, SimplePDFViewer
+
+# Preparing resume parsing
+file_name = "example_resume.pdf"
+fd = open(file_name, "rb")
+doc = SimplePDFViewer(fd)
+doc.render()
+
+# Getting the string content from the file
+resume_content_dump = " ".join(doc.canvas.strings)
+sucky_resume_content = " ".join(doc.canvas.strings[:5])
+# print(resume_content_dump)
+
+# Example inputs from the company
+previous_roles = ["technical product manager", "product manager"]
+previous_skills = ["react", "sql"]
+previous_roles.extend(previous_skills)
+client_interests = previous_roles
+print(client_interests)
+
+# Test of text distance algo
+print(textdistance.levenshtein.normalized_similarity('ass', 'a s s'))
+
+# Finding total final score for words
+list_norm_scores = [textdistance.jaro_winkler(resume_content_dump, word) for word in client_interests]
+avg_normalized_score = sum(list_norm_scores) / len(list_norm_scores)
+
+# sucky resume
+sucky_list_norm_scores = [textdistance.jaro_winkler(sucky_resume_content, word) for word in client_interests]
+sucky_avg_normalized_score = sum(sucky_list_norm_scores) / len(sucky_list_norm_scores)
+
+print('stud resume: ', avg_normalized_score * 100)
+print('sucky: ', sucky_avg_normalized_score * 100)

readme.md

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+# resumeeval
+