Fixed syntax errors in app.py

app.py

@@ -1,136 +1,51 @@
 import gradio as gr
-import pandas as pd
-import numpy as np
-from tensorflow import keras
 import pickle
 
-# Configuration section START
+# Model URL for each
+lr_model_url = './logistic regression/logistic_regression_model.pkl'
 
-with open('model_config.pkl', 'rb') as f:
-    tf = pickle.load(f)
+def bool_value(val):
+    if val:
+        return 1
+    else:
+        return 0
 
-model = keras.models.load_model('model.keras')
 
-all_selected_features = ['Alcohol', 'Arrest Type', 'Belts', 'Contributed To Accident', 'Disobedience', 'Driver City',
-                         'Gender',
-                         'Invalid Documentation', 'Make', 'Mobile Phone', 'Negligent Driving', 'Number Of Offences',
-                         'Personal Injury',
-                         'Property Damage', 'Race', 'Road Signs And Markings', 'Search Outcome', 'Speeding',
-                         'Stop Hour', 'Stop Year',
-                         'SubAgency', 'Vehicle Safety And Standards', 'VehicleType', 'Year']
-very_high_cardinality_features = ['Driver City']
-high_cardinality_features = ['Make', 'VehicleType']
-bools = ['Alcohol', 'Belts', 'Contributed To Accident', 'Disobedience', 'Invalid Documentation', 'Mobile Phone',
-         'Negligent Driving',
-         'Personal Injury', 'Property Damage', 'Road Signs And Markings', 'Speeding', 'Vehicle Safety And Standards']
-num_cols = ['Driver City', 'Make', 'Number Of Offences', 'Stop Hour', 'Stop Year', 'VehicleType', 'Year']
+# Make Prediction Model but would also like to add Gender and Race
+def make_prediction(personal_injury, property_damage, fatal, commercial_vehicle, alcohol, rsam, disobedience, invalid_docu, phone, speeding, 
+                    negligent, vss, num_offences):
 
+    # load model
+    with open(lr_model_url, 'rb') as file:  
+        lr_model = pickle.load(file)
 
-# Configuration section END
+    x_input_feature = [[bool_value(personal_injury), bool_value(property_damage), bool_value(fatal), bool_value(commercial_vehicle), bool_value(alcohol),
+                        bool_value(rsam), bool_value(disobedience), bool_value(invalid_docu), bool_value(phone), bool_value(speeding), bool_value(negligent),
+                        bool_value(vss), num_offences]]
+    prd = lr_model.predict(x_input_feature)
 
+    if prd == 0:
+        return 'SERO'
+    elif prd == 1:
+        return 'Warning'
+    else:
+        return 'Citation'
 
-make_prediction(alcohol, arrest_type, belts, contributed_to_accident, disobedience, driver_city, gender,
-                    invalid_documentation, make, mobile_phone, negligent_driving, number_of_offences, personal_injury,
-                    property_damage, race, road_signs_and_markings, search_outcome, speeding, stop_hour, stop_year,
-                    subagency, vehicle_safety_and_standards, vehicletype, year):
-    """
-    Function to predict the 'Violation Type' of an individual sample of traffic stop:
-    :param alcohol: boolean
-    :param arrest_type: String
-    :param belts: Boolean
-    :param contributed_to_accident: Boolean
-    :param disobedience: Boolean
-    :param driver_city: String
-    :param gender: 'M', 'F' or 'N'
-    :param invalid_documentation: Boolean
-    :param make: String
-    :param mobile_phone: Boolean
-    :param negligent_driving: Boolean
-    :param number_of_offences: Integer
-    :param personal_injury: Boolean
-    :param property_damage: Boolean
-    :param race: One of 'HISPANIC', 'BLACK', 'WHITE', 'OTHER', 'ASIAN', or 'NATIVE AMERICAN'
-    :param road_signs_and_markings: Boolean
-    :param search_outcome: String
-    :param speeding: Boolean
-    :param stop_hour: Integer
-    :param stop_year: Integer
-    :param subagency: String
-    :param vehicle_safety_and_standards: Boolean
-    :param vehicletype: String
-    :param year: Integer
-    :return:
-    """
-    # Create a dataframe with the feature values
-    X = pd.DataFrame([[alcohol, arrest_type, belts, contributed_to_accident, disobedience, driver_city, gender,
-                       invalid_documentation, make,
-                       mobile_phone, negligent_driving, number_of_offences, personal_injury, property_damage, race,
-                       road_signs_and_markings,
-                       search_outcome, speeding, stop_hour, stop_year, subagency, vehicle_safety_and_standards,
-                       vehicletype, year]],
-                     columns=all_selected_features)
-
-    # Transform the features
-    # Encode very high cardinality features (Driver City) with ordinal encoding, by fitting on the whole dataset
-    oev = tf["OrdinalEncoder_VeryHighCardinality"].set_params(handle_unknown='use_encoded_value').set_params(
-        unknown_value=6714)
-    X[very_high_cardinality_features] = oev.transform(X[very_high_cardinality_features])
-
-    # Encode high cardinality features with ordinal encoding by fitting only on the training set
-    X[high_cardinality_features] = tf["OrdinalEncoder_HighCardinality"].transform(X[high_cardinality_features])
-
-    # Scale all the numerical features, including those that were ordinal encoded
-    X[num_cols] = tf["StandardScaler"].transform(X[num_cols])
-
-    # Convert booleans to numbers
-    X[bools] = X[bools].astype('int8')
-
-    # One-hot encode the low cardinality features
-    X = tf['OneHotEncoder'].transform(X)
-
-    # Make the prediction using the model
-    prediction = model.predict(X, verbose=0)
-
-    prediction = tf["LabelEncoder"].inverse_transform(np.argmax(prediction, axis=1))
-
-    # Return the prediction
-    return prediction[0]
-
-arrest_types = ['A - Marked Patrol', 'G - Marked Moving Radar (Stationary)',
-       'Q - Marked Laser', 'L - Motorcycle',
-       'H - Unmarked Moving Radar (Stationary)', 'O - Foot Patrol',
-       'E - Marked Stationary Radar', 'B - Unmarked Patrol',
-       'S - License Plate Recognition', 'R - Unmarked Laser',
-       'J - Unmarked Moving Radar (Moving)', 'M - Marked (Off-Duty)',
-       'I - Marked Moving Radar (Moving)',
-       'F - Unmarked Stationary Radar', 'D - Unmarked VASCAR',
-       'K - Aircraft Assist', 'C - Marked VASCAR', 'P - Mounted Patrol',
-       'N - Unmarked (Off-Duty)']
 
 iface = gr.Interface(fn=make_prediction,
-                     inputs=[gr.components.Checkbox(label='Was the driver under the influence of alcohol?'),
-                             gr.components.Dropdown(label='Choose the arrest type', choices=arrest_types, value='A - Marked Patrol')
-                             gr.components.Checkbox(label='Were seatbelts used appropriately?'),
-                             gr.components.Checkbox(label='Did the driver actions contribute to an accident?'),
+                     inputs=[gr.components.Checkbox(label='Did the violation involve any personal injury?'),
+                             gr.components.Checkbox(label='Did the violation involve any property damage?'),
+                             gr.components.Checkbox(label='Did the violation involve any fatalities?'),
+                             gr.components.Checkbox(label='Is the vehicle committing the traffic violation a commercial vehicle?'),
+                             gr.components.Checkbox(label='Was the driver under the influence of alcohol?'),
+                             gr.components.Checkbox(label='Did the driver fail to obey signs and markings (such as traffic control device instructions, stop lights, red signal and stop sign lines)?'),
                              gr.components.Checkbox(label='Was the driver disobedient? (such as failing to display documentation upon request)?'),
-                             gr.components.Dropdown(label='Choose the arrest type', choices=tf['OrdinalEncoder_VeryHighCardinality'].categories_, value='SILVER SPRING')
-                             gr.components.Dropdown(label='Driver Gender', choices=['M', 'F', 'N'], value='M')
                              gr.components.Checkbox(label='Was the driver driving with Invalid Documentation (such as suspended registration, suspended license, expired registration plates and validation tabs or expired license plate)?'),
-                             gr.components.Dropdown(label='Vehicle Make', choices=tf['OrdinalEncoder_HighCardinality'].categories_[0], value='TOYOTA')
                              gr.components.Checkbox(label='Was the driver using a mobile phone while driving?'),
+                             gr.components.Checkbox(label='Was the driver caught speeding?'),
                              gr.components.Checkbox(label='Was the driver caught driving with negligence (example switching lanes in an unsafe manner)?'),
+                             gr.components.Checkbox(label='Was the vehicle up to standards (lights properly switched, registration plates attached etc.)?'),
                              gr.components.Slider(minimum=1, step=1, label='Number of offences committed')],
-                             gr.components.Checkbox(label='Did the violation involve any personal injury?'),
-                             gr.components.Checkbox(label='Did the violation involve any property damage?'),
-                             gr.components.Dropdown(label='Choose the race of the driver', choices=tf['OneHotEncoder'].transformers_[0][1].categories_[2], value='WHITE')
-                             gr.components.Checkbox(label='Did the driver fail to obey signs and markings (such as traffic control device instructions, stop lights, red signal and stop sign lines)?'),
-                             gr.components.Dropdown(label='Choose the race of the driver', choices=tf['OneHotEncoder'].transformers_[0][1].categories_[3], value='NO SEARCH CONDUCTED')
-                             gr.components.Checkbox(label='Was the driver caught speeding?'),
-                             gr.components.Slider(maximum=23, step=1, label='Time HOUR when stop occurred in 24-hour format')],
-                             gr.components.Slider(minimum=2012, maximum=2024, step=1, label='Year when stop occurred')],
-                             gr.components.Dropdown(label='What is the name of the subagency that conducted the traffic stop?', choices=tf['OneHotEncoder'].transformers_[0][1].categories_[1], value='4th District, Wheaton')
-                             gr.components.Checkbox(label='Was the vehicle safe and up to standards (lights properly switched, registration plates attached etc.)?'),
-                             gr.components.Slider(minimum=1970, maximum=2023, step=1, label='Year of manufacture of the vehicle:)],
                      outputs=["text"])
 
 iface.launch(debug=True)