Update app.py

app.py

@@ -7,14 +7,14 @@ import pandas as pd
 import requests
 
 available_datasets = [
+    "Brand Propensity Technology",
+    "Brand Propensity Quick Service Restaurant (QSR)",
+    "Category Propensity Personal Care & Lifestyle",
+    "Customer Segmentation Doordash",
+    "Customer Segmentation Uber",
+    "Customer Segmentation Walgreens",
+    "Customer Segmentation Walmart",
     "Demographics",
-    "DoorDash Customer Segmentation",
-    "Personal Care and Lifestyle Category Propensity",
-    "Quick Service Restaurant (QSR) Brand Propensity",
-    "Technology Brand Propensity",
-    "Uber Customer Segmentation",
-    "Walgreens Customer Segmentation",
-    "Walmart Customer Segmentation",
 ]
 
 default_dataset = "Demographics"
@@ -560,7 +560,7 @@ def load_dataset(dataset):
                 ],
             }
         )
-    elif formated_dataset_name == "doordash_customer_segmentation":
+    elif formated_dataset_name == "customer_segmentation_doordash":
         return pd.DataFrame(
             {
                 "monetary_doordash": [
@@ -628,7 +628,7 @@ def load_dataset(dataset):
                 ],
             }
         )
-    elif formated_dataset_name == "personal_care_and_lifestyle_category_propensity":
+    elif formated_dataset_name == "category_propensity_personal_care_&_lifestyle":
         return pd.DataFrame(
             {
                 "category_propensity_beauty_products": [
@@ -717,7 +717,7 @@ def load_dataset(dataset):
                 ],
             }
         )
-    elif formated_dataset_name == "quick_service_restaurant_(qsr)_brand_propensity":
+    elif formated_dataset_name == "brand_propensity_quick_service_restaurant_(qsr)":
         return pd.DataFrame(
             {
                 "brand_propensity_burger_king": [
@@ -807,7 +807,7 @@ def load_dataset(dataset):
             }
         )
 
-    elif formated_dataset_name == "technology_brand_propensity":
+    elif formated_dataset_name == "brand_propensity_technology":
         return pd.DataFrame(
             {
                 "brand_propensity_apple": [
@@ -896,7 +896,7 @@ def load_dataset(dataset):
                 ],
             }
         )
-    elif formated_dataset_name == "uber_customer_segmentation":
+    elif formated_dataset_name == "customer_segmentation_uber":
         return pd.DataFrame(
             {
                 "monetary_uber": [
@@ -964,7 +964,7 @@ def load_dataset(dataset):
                 ],
             }
         )
-    elif formated_dataset_name == "walgreens_customer_segmentation":
+    elif formated_dataset_name == "customer_segmentation_walgreens":
         return pd.DataFrame(
             {
                 "monetary_walgreens": [
@@ -1032,7 +1032,7 @@ def load_dataset(dataset):
                 ],
             }
         )
-    elif formated_dataset_name == "walmart_customer_segmentation":
+    elif formated_dataset_name == "customer_segmentation_walmart":
         return pd.DataFrame(
             {
                 "monetary_walmart": [