Update app.py

app.py

@@ -25,9 +25,11 @@ from config import domainColumnName
 
 
 if notionToken is None:
-    raise Exception("Notion token not found. Please check the environment variables.")
+    notionFound = "Notion token not found."
+    raise Exception(notionFound)    
 else:
-    print("Notion token found successfully!")
+    notionFound = "Notion token found successfully!"
+    print(notionFound)
     if useNotionData:
         notion = client_notion(auth=notionToken)
         lu_mapperDict, subdomain_mapperDict = getDataFromNotion(
@@ -60,9 +62,11 @@ from config import useSpeckleData
 from imports_utils import getDataFromSpeckle
 
 if speckleToken is None:
-    raise Exception("Speckle token not found")
+    speckleFound = "Speckle token not found."
+    raise Exception(speckleFound)
 else:
-    print("Speckle token found successfully!")
+    speckleFound = "Speckle token found successfully!"
+    print(speckleFound)
     if useSpeckleData == True:
         CLIENT = SpeckleClient(host="https://speckle.xyz/")
         account = get_default_account()
@@ -171,7 +175,47 @@ def test(input_json):
     
     df_lu_dict = dfLanduses.to_dict('index')
     dm_dictionary = dfMatrix.to_dict('index')
-   
+
+    # generate report string
+
+
+    nan_count = dfMatrix.isna().sum().sum()
+    nan_count_after_inf_replacement = dfMatrix.replace([np.inf, -np.inf], np.nan).isna().sum().sum()
+    inf_count = nan_count_after_inf_replacement - nan_count
+    
+    landuseSum = ', '.join([f"{column} = {dfLanduses[column].sum()}" for column in dfLanduses.columns])
+    weightsSum = ', '.join([f"{column} = {LivabilitySubdomainsInputs[column].sum()}" for column in LivabilitySubdomainsInputs.columns])
+
+
+    valid_indexes = [idx for idx in mask_connected if idx in dfLanduses_gh.index]
+    missing_indexes = set(mask_connected) - set(valid_indexes)
+    if missing_indexes:
+        indexError = f"Error: The following indexes were not found in the DataFrame: {missing_indexes}, length: {len(missing_indexes)}"
+
+
+    accessibilityStats = [
+    f"{column} = {df[column].min()}, {df[column].max()}, {df[column].mean()}" for column in subdomainsAccessibility.columns
+    ]
+
+    livabilityStats = [
+    f"{column} = {df[column].min()}, {df[column].max()}, {df[column].mean()}" for column in livability.columns
+    ]
+
+    outputReport = [
+    notionFound,
+    speckleFound,
+    f"Distance matrix length: {len(dfMatrix)}",  
+    f"Number Infinity values: {inf_count}, number NaN values: {nan_count}",
+    f"Activity nodes with landuses number: {len(dfLanduses)}",  
+    f"Total area per landuse: {landuseSum}",
+    f"Total area (jobs) per livability category: {weightsSum}",
+    indexError,
+    f"Accessibility parameters used: threshold: {threshold}, alpha: {alpha}",
+    f"Accessibility stats: {accessibilityStats}",
+    f"Livability stats: {livabilityStats}"
+    ]
+
+
     # Prepare the output
     output = {
         "subdomainsAccessibility_dictionary": subdomainsAccessibility_dictionary,
@@ -182,7 +226,8 @@ def test(input_json):
         "mask_connected": mask_connected,
         "dm_an": dm_dictionary,
         "landuses":df_lu_dict,
-        "constants": [alpha, threshold]
+        "constants": [alpha, threshold],
+        "report": outputReport
     }