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LICENSE

+    MIT License
+
+    Copyright (c) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE

README.md

+# Python Flask app on Azure Web App for Containers
+
+This is a minimal sample app that demonstrates how to run a Python Flask application on Azure App Service on Linux.
+
+For more information, please see the [Python on App Service quickstart](https://docs.microsoft.com/en-us/azure/app-service/containers/quickstart-python).
+
+# Contributing
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

application.py

+from flask import Flask, redirect, url_for, request 
+import json
+import numpy as np
+import random
+app = Flask(__name__) 
+
+
+def stable_marriage(prof_mat,allot_count):
+	a_b_mapping = {}
+	a_count = []
+
+	(n_a,n_b) = prof_mat.shape
+	# i - prof
+	# each - student
+	track_list = []
+	for i in range(n_b):
+		for j in range(int(allot_count[0][i])):
+			track_list.append(i)
+	random.shuffle(track_list)
+	# print(track_list)
+
+	track_len = len(track_list)
+	stud_count = n_a
+	while (track_len != 0 and stud_count>0):
+		i = track_list[0]
+		# for i in track_list:
+		stud_list = list(prof_mat[:,i])
+		# print(stud_list)
+		temp = False
+		while(True):
+			possible_stud = np.argwhere(stud_list == np.amax(stud_list)).flatten().tolist()
+			for each in possible_stud:
+				if each in a_b_mapping:
+					if prof_mat[each][a_b_mapping[each]]<prof_mat[each][i]:
+						# a_b_mapping.pop(each)
+						# track_list.append(each)
+						track_list.append(a_b_mapping[each])
+						a_b_mapping[each] = i
+						track_list.remove(i)
+						temp = True
+						break
+					else:
+						continue
+				else:
+					a_b_mapping[each] = i
+					track_list.remove(i)
+					temp = True
+					break
+			if temp:
+				# print(str(i) +" -- "+ str(each))
+				stud_count -= 1
+				break
+			else:
+				for j in possible_stud:
+					stud_list[j] = 0
+		track_len = len(track_list)
+
+	# print(a_b_mapping)
+	# print("yaay")
+
+	return a_b_mapping
+
+  
+@app.route('/alloc',methods = ['POST']) 
+def get_alloc_data(): 
+   data = request.get_data()
+   # print(data)
+   # print(json.loads(data))
+   data = json.loads(data)
+   id_A=[]
+   id_B=[]
+   col=0
+   row=0
+   rowB=0
+   for features in data['A']['features']:
+	  col=col+1
+   for entry in data['data_A']:
+	  row=row+1
+
+   for entry in data['data_B']:
+	  rowB=rowB+1
+   count=0
+   mat = np.zeros((row,col))
+   prof_mat = np.zeros((row,rowB))
+   mat.astype(float)
+   prof_mat.astype(float)
+   # print(data['A']['features'])
+
+
+#    print("counting...")
+   allot_count = np.ones((1,rowB))
+   allot_count.astype(int)
+   for i in range(rowB):
+      # print(data['data_B'][i]['fid'])
+      try:
+      	# print(data['final_count'][str(data['data_B'][i]['fid'])])
+      	allot_count[0][i] = data['final_count'][str(data['data_B'][i]['fid'])]
+      except:
+  	  	continue
+   # print(allot_count)
+
+
+   
+
+
+   for features in data['A']['features']:
+	  if(features['weight'] == 0 ):       #only one feature should have weight 0
+		 for entry in data['data_A']:
+			id_A.append(entry[features['name']])
+		 count = count + 1
+	  elif(features['condition'] in ["increasing", "decreasing"]):
+		 col1=[]
+		 maxi=0.0
+		 for entry in data['data_A']:
+			maxi=max(maxi,entry[features['name']])
+			col1.append(float(entry[features['name']]))
+		 for c in range(0,len(col1)):
+			# mat[c][count] = col1[c]/maxi
+			mat[c][count] = (col1[c]/maxi)*features['weight']
+		 count=count+1  #used to point to the column for that feature
+
+	  elif(features['condition'] in ("matching_without_values")):
+		 #consider no grades given
+		 # input is array of  key-value pair
+		 for i in range(row):
+			for j in range(rowB):
+			   list_A =  list(data['data_A'][i]['course_taken'].keys())    #update
+			   list_B = data['data_B'][j]['courses']
+			   matched = list(set(list_A) & set(list_B))
+			   prof_mat[i][j] += float(len(matched)*features['weight'])
+		 max_val = np.amax(prof_mat) 
+		 prof_mat = np.true_divide(prof_mat, max_val)
+		 count=count+1  #used to point to the column for that feature
+	  elif(features['condition'] in ("matching_with_values")):
+		 #consider grades are given
+		 # input is array of string  
+		 feature_name = features['name'] 
+		 mapped_feature_name = data['mapping'][feature_name]  
+		 # feature_name = 'course_taken' 
+		 # mapped_feature_name = 'courses'
+		 for i in range(row):
+			for j in range(rowB):
+			   list_A =  list(data['data_A'][i][feature_name].keys())
+			   list_B = data['data_B'][j][mapped_feature_name]
+			   matched = list(set(list_A) & set(list_B))
+			   sum_val_matched = sum([data['data_A'][i][feature_name][each] for each in matched])
+			   prof_mat[i][j] += float(sum_val_matched*features['weight'])
+		 max_val = np.amax(prof_mat) 
+		 prof_mat = np.true_divide(prof_mat, max_val)
+		 count=count+1  #used to point to the column for that feature
+
+   # print(mat)
+   # print(prof_mat)
+
+   score_students = np.sum(mat, axis=1)
+   # print(score_students)
+
+   for i in range(prof_mat.shape[0]):
+		prof_mat[i,:] = prof_mat[i,:] + score_students[i]
+   # print(prof_mat)
+
+   final_mapping = stable_marriage(prof_mat,allot_count)
+
+#    print(final_mapping)
+
+   final_mapping_list = []
+
+   for k,v in final_mapping.items():
+   		# print(data['data_A'][k])
+   		# print(data['data_B'][v])
+		final_mapping_list.append((data['data_A'][k],data['data_B'][v]))
+   		# print("----------------------")
+	print(json.dumps({'mapping':final_mapping_list}))
+   return json.dumps({'mapping':final_mapping_list})
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requirements.txt

+click==6.7
+Flask==1.0.2
+itsdangerous==0.24
+Jinja2==2.10
+MarkupSafe==1.0
+Werkzeug==0.14.1
+numpy

venv/bin/python

+python3
\ No newline at end of file

venv/bin/python3

+/usr/bin/python3
\ No newline at end of file

venv/lib64

+lib
\ No newline at end of file

venv/pyvenv.cfg

+home = /usr/bin
+include-system-site-packages = false
+version = 3.6.7