Update app.py

app.py

@@ -32,19 +32,14 @@ if notionToken is None:
     raise Exception("Notion token not found. Please check the environment variables.")
 else:
     print("Notion token found successfully!")
-    """
-    landuse_attributes  = fetch_all_database_pages(notion, landuseDatabaseId)
-    livability_attributes  = fetch_all_database_pages(notion, subdomainAttributesDatabaseId)
-    landuseMapperDict = fetchDomainMapper (landuse_attributes)
-    livabilityMapperDict = fetchSubdomainMapper (livability_attributes)
-    """
-if useNotionData:
-    landuseMapperDict, livabilityMapperDict = getDataFromNotion(
-        notion=notion,
-		notionToken=notionToken,
-		landuseDatabaseID=landuseDatabaseId,
-		subdomainDatabaseID=subdomainAttributesDatabaseId
-    )
+    if useNotionData == True:
+        notion = client_notion(auth=notionToken)
+        landuseMapperDict, livabilityMapperDict = getDataFromNotion(
+            notion=notion,
+    		notionToken=notionToken,
+    		landuseDatabaseID=landuseDatabaseId,
+    		subdomainDatabaseID=subdomainAttributesDatabaseId
+        )
 
 
 
@@ -74,54 +69,29 @@ if speckleToken is None:
     raise Exception("Speckle token not found")
 else:
     print("Speckle token found successfully!")
-
-if useSpeckleData == True:
-    
-    CLIENT = SpeckleClient(host="https://speckle.xyz/")
-    account = get_default_account()
-    CLIENT.authenticate_with_token(token=speckleToken)
-    
-    landuses, matrices = getDataFromSpeckle(speckleClient = CLIENT, streamID=streamId,matrixBranchName=dmBranchName, landuseBranchName=luBranchName)
-         
-    #streamDistanceMatrices = speckle_utils.getSpeckleStream(streamId,dmBranchName,CLIENT, dmCommitId)
-    #matrices = fetchDistanceMatrices (streamDistanceMatrices)
-    #streamLanduses = speckle_utils.getSpeckleStream(streamId,luBranchName,CLIENT, luCommitId)
-    #streamData = streamLanduses["@Data"]["@{0}"]
-    
-    #df_speckle_lu = speckle_utils.get_dataframe(streamData, return_original_df=False)
-    #df_lu = df_speckle_lu.copy()
-    #df_lu = df_lu.astype(str)
-    #df_lu =  df_lu.set_index("uuid", drop=False)  # variable, uuid as default
-
-    df_dm = matrices[distanceMatrixActivityNodes]
-    df_dm_dict = df_dm.to_dict('index')
-     
-    # Replace infinity with 10000 and NaN values with 0, then convert to integers
-    df_dm = df_dm.replace([np.inf, -np.inf], 10000).fillna(0)
-    df_dm = df_dm.apply(pd.to_numeric, errors='coerce')
-    df_dm = df_dm.round(0).astype(int)
+    if useSpeckleData == True:
+        CLIENT = SpeckleClient(host="https://speckle.xyz/")
+        account = get_default_account()
+        CLIENT.authenticate_with_token(token=speckleToken)
         
-    mask_connected = df_dm.index.tolist()
-
-    """
-    lu_columns = []   # provided by user? or prefix
-    for name in df_lu.columns:
-      if name.startswith("lu+"):
-        lu_columns.append(name)
-
-    """
-    df_lu = landuses.loc[mask_connected]
+        landuses, matrices = getDataFromSpeckle(speckleClient = CLIENT, streamID=streamId,matrixBranchName=dmBranchName, landuseBranchName=luBranchName)
+             
+        df_dm = matrices[distanceMatrixActivityNodes]
+        df_dm_dict = df_dm.to_dict('index')
+        df_dm = df_dm.replace([np.inf, -np.inf], 10000).fillna(0)
+        df_dm = df_dm.apply(pd.to_numeric, errors='coerce')
+        df_dm = df_dm.round(0).astype(int)
+        mask_connected = df_dm.index.tolist()
+        df_lu = landuses.loc[mask_connected]
+        
+        mergeAssetNonAssetLanduse = True
+        if mergeAssetNonAssetLanduse:
+            df_lu.columns = [col.replace('ASSETS+', '') for col in df_lu.columns]
     
-    #df_lu_filtered.columns = [col.replace('lu+', '') for col in df_lu_filtered.columns]
-
-    mergeAssetNonAssetLanduse = True
-    if mergeAssetNonAssetLanduse:
-        df_lu.columns = [col.replace('ASSETS+', '') for col in df_lu.columns]
-
-    df_lu = df_lu.replace([np.inf, -np.inf], 10000).fillna(0)
-    df_lu = df_lu.apply(pd.to_numeric, errors='coerce')
-    df_lu = df_lu.astype(int)
-    df_lu = df_lu.T.groupby(level=0).sum().T
+        df_lu = df_lu.replace([np.inf, -np.inf], 10000).fillna(0)
+        df_lu = df_lu.apply(pd.to_numeric, errors='coerce')
+        df_lu = df_lu.astype(int)
+        df_lu = df_lu.T.groupby(level=0).sum().T
     
     
 
@@ -232,8 +202,8 @@ def test(input_json):
         #"subdomainsAccessibility_dictionary": subdomainsAccessibility_dictionary,
         #"livability_dictionary": livability_dictionary,
         #"subdomainsWeights_dictionary": LivabilitySubdomainsInputs_dictionary,
-        #"luDomainMapper": landuseMapperDict,
-        #"attributeMapper": livabilityMapperDict,
+        "luDomainMapper": landuseMapperDict,
+        "attributeMapper": livabilityMapperDict,
         "mask_connected": mask_connected,
         "dm_an": df_dm_dict,
         "landuses":df_lu_filtered_dict,