separate officer race and gender code

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 
-from block import officerRaceDemo, officerGenderDemo
+from block_officer_race import officerRaceDemo
+from block_officer_gender import officerGenderDemo
 
 demo = gr.TabbedInterface([officerRaceDemo, officerGenderDemo],
                           ["Predict OfficerRace", "Predict Officer Gender"],

block.py → block_officer_race.py

@@ -144,14 +144,4 @@ with gr.Blocks() as officerRaceDemo:
                         impacted_race_dropdown,
                         incident_precinct_dropdown]
     btn = gr.Button("Predict")
-    btn.click(fn=infer, inputs=input_components, outputs=out)
-
-with gr.Blocks() as officerGenderDemo:
-    def greet(name):
-        return "Hello " + name + "!"
-
-
-    name = gr.Textbox(label="Name")
-    output = gr.Textbox(label="Output Box")
-    greet_btn = gr.Button("Greet")
-    greet_btn.click(fn=greet, inputs=name, outputs=output, api_name="greet")
+    btn.click(fn=infer, inputs=input_components, outputs=out)

inference.py

@@ -2,7 +2,7 @@ import numpy as np
 
 import config
 from model import load_models
-from preprocessor import preprocess
+from preprocessor import process_officer_race
 
 
 def predict_officer_race(model_name, X):
@@ -21,10 +21,24 @@ def infer_officer_race(model_name, current_rank, incident_rank, previous_complai
                        days_on_force, officer_gender, fado_type, allegation, ccrb_disposition,
                        penalty_rec, penalty_cat, location_type, contact_outcome, impacted_gender, impacted_race,
                        incident_precinct):
-    input_array = preprocess(current_rank, incident_rank, previous_complaints, complaint_duration_days,
-                             days_on_force, officer_gender, fado_type, allegation, ccrb_disposition,
-                             penalty_rec, penalty_cat, location_type, contact_outcome, impacted_gender, impacted_race,
-                             incident_precinct)
+    input_array = process_officer_race(current_rank, incident_rank, previous_complaints, complaint_duration_days,
+                                       days_on_force, officer_gender, fado_type, allegation, ccrb_disposition,
+                                       penalty_rec, penalty_cat, location_type, contact_outcome, impacted_gender, impacted_race,
+                                       incident_precinct)
     prediction = predict_officer_race(model_name, input_array)
     prediction = f"The officer is predicted to be {prediction}"
     return prediction
+
+
+def infer_officer_gender(model_name, current_rank, incident_rank, previous_complaints, complaint_duration_days,
+                       days_on_force, officer_gender, fado_type, allegation, ccrb_disposition,
+                       penalty_rec, penalty_cat, location_type, contact_outcome, impacted_gender, impacted_race,
+                       incident_precinct):
+    input_array = process_officer_race(current_rank, incident_rank, previous_complaints, complaint_duration_days,
+                                       days_on_force, officer_gender, fado_type, allegation, ccrb_disposition,
+                                       penalty_rec, penalty_cat, location_type, contact_outcome, impacted_gender, impacted_race,
+                                       incident_precinct)
+    prediction = predict_officer_race(model_name, input_array)
+    prediction = f"The officer is predicted to be {prediction}"
+    return prediction
+

model.py

@@ -2,6 +2,7 @@ import os
 import joblib
 from pathlib import Path
 
+
 def load_models(model_name, target_column):
     """Load models from disk"""
     model_root = Path("./models")

preprocessor.py

@@ -77,11 +77,52 @@ def transform_to_ohe(column_name, value):
     return one_hot
 
 
-def preprocess(current_rank, incident_rank, previous_complaints, complaint_duration_days,
-               days_on_force, officer_gender, fado_type, allegation, ccrb_disposition,
-               penalty_rec, penalty_cat, location_type, contact_outcome,
-               impacted_gender, impacted_race,
-               incident_precinct):
+def process_officer_race(current_rank, incident_rank, previous_complaints, complaint_duration_days,
+                         days_on_force, officer_gender, fado_type, allegation, ccrb_disposition,
+                         penalty_rec, penalty_cat, location_type, contact_outcome,
+                         impacted_gender, impacted_race,
+                         incident_precinct):
+    current_rank = transform_rank(current_rank)
+    incident_rank = transform_rank(incident_rank)
+    previous_complaints = transform_previous_complaints(previous_complaints)
+    complaint_duration_days = transform_complaint_duration_days(complaint_duration_days)
+    days_on_force = transform_days_on_force(days_on_force)
+    officer_gender = transform_to_ohe('OfficerGender', officer_gender)
+    fado_type = transform_to_ohe('FADOType', fado_type)
+    allegation = transform_to_ohe('Allegation', allegation)
+    ccrb_disposition = transform_to_ohe('CCRBDisposition', ccrb_disposition)
+    penalty_rec = transform_to_ohe('PenaltyRec', penalty_rec)
+    penalty_cat = transform_to_ohe('PenaltyCat', penalty_cat)
+    location_type = transform_to_ohe('LocationType', location_type)
+    contact_outcome = transform_to_ohe('ContactOutcome', contact_outcome)
+    impacted_gender = transform_to_ohe("ImpactedGender", impacted_gender)
+    impacted_race = transform_to_ohe("ImpactedRace", impacted_race)
+    incident_precinct = transform_to_ohe("IncidentPrecinct", incident_precinct)
+
+    input_array = np.concatenate((current_rank,
+                                  incident_rank,
+                                  days_on_force,
+                                  previous_complaints,
+                                  complaint_duration_days,
+                                  officer_gender,
+                                  fado_type,
+                                  allegation,
+                                  ccrb_disposition,
+                                  penalty_rec,
+                                  penalty_cat,
+                                  location_type,
+                                  contact_outcome,
+                                  impacted_gender,
+                                  impacted_race,
+                                  incident_precinct), dtype=np.float32)
+    input_array = input_array.reshape(1, -1)
+    return input_array
+
+def process_officer_gender(current_rank, incident_rank, previous_complaints, complaint_duration_days,
+                         days_on_force, officer_gender, fado_type, allegation, ccrb_disposition,
+                         penalty_rec, penalty_cat, location_type, contact_outcome,
+                         impacted_gender, impacted_race,
+                         incident_precinct):
     current_rank = transform_rank(current_rank)
     incident_rank = transform_rank(incident_rank)
     previous_complaints = transform_previous_complaints(previous_complaints)