Additional renames

app.py

@@ -10,7 +10,7 @@ from settings import (
     CHILDREN_MIN_MAX,
     INCOME_MIN_MAX,
     AGE_MIN_MAX,
-    SALARIED_MIN_MAX,
+    EMPLOYED_MIN_MAX,
     FAMILY_MIN_MAX,
     INCOME_TYPES,
     OCCUPATION_TYPES,
@@ -94,8 +94,8 @@ with demo:
 
         with gr.Column():
             gr.Markdown("### Third party ")
-            salaried = gr.Radio(["Yes", "No"], label="Is the person salaried ?", value="Yes")
-            years_salaried = gr.Slider(**SALARIED_MIN_MAX, step=1, label="Years of employment", info="How long have this person been salaried (in years) ?")
+            employed = gr.Radio(["Yes", "No"], label="Is the person employed ?", value="Yes")
+            years_employed = gr.Slider(**EMPLOYED_MIN_MAX, step=1, label="Years of employment", info="How long have this person been employed (in years) ?")
 
 
     gr.Markdown("## Step 3: Encrypt the inputs using FHE and send them to the server.")
@@ -201,7 +201,7 @@ with demo:
     # client side to the server    
     encrypt_button_third_party.click(
         pre_process_encrypt_send_third_party,
-        inputs=[client_id, salaried, years_salaried],
+        inputs=[client_id, employed, years_employed],
         outputs=[encrypted_input_third_party],
     )
 

backend.py

@@ -306,13 +306,13 @@ def pre_process_encrypt_send_third_party(client_id, *inputs):
         (int, bytes): Integer ID representing the current client and a byte short representation of 
             the encrypted input to send. 
     """
-    salaried, years_salaried = inputs
+    employed, years_employed = inputs
 
-    is_salaried = salaried == "Yes"
+    is_employed = employed == "Yes"
 
     third_party_inputs = pandas.DataFrame({
-        "Salaried": [is_salaried],
-        "Years_employed": [years_salaried],
+        "Employed": [is_employed],
+        "Years_employed": [years_employed],
     })
 
     third_party_inputs = third_party_inputs.reindex(THIRD_PARTY_COLUMNS, axis=1)

deployment_files/client.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b698501c432ade8d406a3d83fcff9a86ed59a4c693116d14b72815602b04b1d9
-size 75886
+oid sha256:3a407200f51839a85032c3f69ca3413e9cdb99d2e03722f03450599d2f8d318d
+size 76138

deployment_files/pre_processor_third_party.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:999ae2b4f9420d6b2e2987035cde0f43e93b2edb85ac32b3c877584b45871ca8
+oid sha256:223d27a2af60a06845da0191cf725b7bab7f31ebb0a84bc530cbd32001ac4f47
 size 1588

deployment_files/pre_processor_user.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8454f6f929f89b5dec427d9f6e522a33cde0a49c8dc6f06a650bb0bf90b59913
-size 6221
+oid sha256:60f9d3d6e98f3eadc7a30340dae414df3f42fabd73da01fe0a6ece91186964b5
+size 6222

deployment_files/server.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:73e247b96ac6325fde795f2f6e6c8cd43946b5e5979421845283871bb8e0475e
-size 3356
+oid sha256:98a6a55f0be2a5fc9b2174b9a9686550fe2a89c3c2ae937aeb31e4057f645a03
+size 3419

settings.py

@@ -51,7 +51,7 @@ USER_COLUMNS = [
     'Occupation_type',
 ]
 BANK_COLUMNS = ["Account_age"]
-THIRD_PARTY_COLUMNS = ["Years_employed", "Salaried"]
+THIRD_PARTY_COLUMNS = ["Years_employed", "Employed"]
 
 _data = pandas.read_csv(DATA_PATH, encoding="utf-8")
 
@@ -67,7 +67,7 @@ ACCOUNT_MIN_MAX = get_min_max(_data, "Account_age")
 CHILDREN_MIN_MAX = get_min_max(_data, "Num_children")
 INCOME_MIN_MAX = get_min_max(_data, "Total_income")
 AGE_MIN_MAX = get_min_max(_data, "Age")
-SALARIED_MIN_MAX = get_min_max(_data, "Years_employed")
+EMPLOYED_MIN_MAX = get_min_max(_data, "Years_employed")
 FAMILY_MIN_MAX = get_min_max(_data, "Household_size")
 
 # App data choices