Create app.py

app.py

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