Update app.py

app.py

@@ -166,10 +166,10 @@ def test(input_json):
         
         df_LivabilitySubdomainsWorkplaces = pd.DataFrame(0, index=DistanceMatrix.index, columns=['jobs'])
     
-        for domain in UniqueSubdomainsList:
+        for subdomain in UniqueSubdomainsList:
           for key, value_list in SubdomainAttributeDict.items():
-            sqm_per_empl = float(SubdomainAttributeDict[domain]['sqmPerEmpl'][0])
-            if key in destinationWeights.columns and key == domain:
+            sqm_per_empl = float(SubdomainAttributeDict[subdomain]['sqmPerEmpl'][0])
+            if key in destinationWeights.columns and key == subdomain:
               if sqm_per_empl > 0:
                 df_LivabilitySubdomainsWorkplaces['jobs'] += (round(destinationWeights[key] / sqm_per_empl,2)).fillna(0)
               else:
@@ -217,8 +217,6 @@ def test(input_json):
     def accessibilityToLivability (DistanceMatrix,subdomainsAccessibility, SubdomainAttributeDict,UniqueDomainsList):
     
         livability = pd.DataFrame(index=DistanceMatrix.index, columns=subdomainsAccessibility.columns)
-        #livability.drop(columns='jobs', inplace=True)
-        #livability["Workplaces"] = 0
                              
         for domain in UniqueDomainsList:
             livability[domain] = 0
@@ -240,7 +238,7 @@ def test(input_json):
                 livability.loc[subdomainsAccessibility['jobs'] >= threshold, 'Workplaces'] = max_livability
                 livability.loc[subdomainsAccessibility['jobs'] < threshold, 'Workplaces'] = livability_score
 
-            if key in subdomainsAccessibility.columns:
+            elif key in subdomainsAccessibility.columns and key != 'commercial':
                 livability_score = remap(subdomainsAccessibility[key], 0, threshold, 0, max_livability)
                 livability.loc[subdomainsAccessibility[key] >= threshold, key] = max_livability
                 livability.loc[subdomainsAccessibility[key] < threshold, key] = livability_score