Commit
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08fd9fc
1
Parent(s):
991a56b
Create app.py
Browse files
app.py
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import json
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from scipy.stats import multivariate_normal as mvn
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from scipy.stats import norm
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import numpy as np
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import gradio as gr
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from functions import *
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def main(file):
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# Read input file
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with open(file.name) as json_file:
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data = json.load(json_file)
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# Validate input
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team_stats = [person['attributes'] for person in data['team']]
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applicant_stats = [person['attributes'] for person in data['applicants']]
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if len(team_stats) < 2:
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print('Input file must have at least 2 team members to score applicants')
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return None
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if len(team_stats[0]) == 0:
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print('Team members must have at least 1 attribute')
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return None
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if not all_equal([tuple(person.keys()) for person in team_stats + applicant_stats]):
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print('Input file has incomplete data. Please input all attributes for all people')
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return None
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# Build model
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# Model each attribute as a normal distribution, with each attribute independent from eachother
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attributes = tuple(team_stats[0].keys())
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dist = [[person[attribute] for person in team_stats] for attribute in attributes]
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multivariate_normal_model = {
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"attributes": attributes,
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"distribution": dist,
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"means":[np.mean(feature) for feature in dist],
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"covariance_matrix": make_independent(np.cov(dist))
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}
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model = mvn(mean=multivariate_normal_model['means'],
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cov=multivariate_normal_model['covariance_matrix'], allow_singular=True)
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# Calculate scores
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scores = [{'name': applicant['name'],
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'score': compatibility(model, multivariate_normal_model['attributes'], applicant['attributes'])}
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for applicant in data['applicants']]
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# Write results to scores.json
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json_object = json.dumps({"scoredApplicants": scores}, indent=4)
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with open("scores.json", "w") as outfile:
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outfile.write(json_object)
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return "scores.json"
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demo = gr.Interface(main, "file", "file")
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demo.launch()
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