import random
import gradio as gr
from joblib import load

# Model URL for each
rf_model_url = 'random_forest_model.joblib'


# Load Model method
def load_model(url):
    return load(rf_model_url)


def bool_value(val):
    if not val:
        return 0
    else:
        return 1


def gender(val):
    if val == 'Male':
        return 1
    elif val == 'Female':
        return 0
    else:
        return 2


def race(val):
    if val == 'Hispanic':
        return 2
    elif val == 'Black':
        return 1
    elif val == 'White':
        return 5
    elif val == 'Other':
        return 4
    elif val == 'Asian':
        return 0
    else:
        return 3


def search_outcome(val, end_range):
    if not val:
        return 2  # Which mean no search was conducted
    else:
        return random.randrange(0, end_range)


def search_reason(val, end_range):
    if not val:
        return 726  # Which mean no search was conducted
    else:
        return random.randrange(0, end_range)


# Make Prediction Model but would also like to add Gender and Race
def make_predication(year_stopped, offences, search, doc, car_year, alcohol, safety, genders, speeding,
                     races, accident, actual_accident, damage, road_signs, injury, belt, disobedience, bad_driving,
                     phone):
    input_features = {
        'encoded_Search Outcome': search_outcome(search, 6),
        'encoded_Search Reason For Stop': search_reason(search, 727),
        'Number Of Offences': offences,
        'Search Conducted': bool_value(search),
        'Year Stopped': year_stopped,
        'encoded_SubAgency': random.randrange(0, 7),
        'encoded_Arrest Type': random.randrange(0, 18),
        'Invalid Documentation': bool_value(doc),
        'Year': car_year,  # int
        'encoded_Driver City': random.randrange(0, 8114),  # int
        'encoded_Make': random.randrange(0, 57),  # int
        'Alcohol': bool_value(alcohol),  # bool
        'encoded_Color': random.randrange(0, 25),  # int
        'Vehicle Safety And Standards': bool_value(safety),  # bool
        'Speeding': bool_value(speeding),
        'encoded_Race': race(races),
        'Contributed To Accident': bool_value(accident),
        'Accident': bool_value(actual_accident),
        'encoded_VehicleType': random.randrange(0, 31),
        'Property Damage': bool_value(damage),
        'Road Signs And Markings': bool_value(road_signs),
        'Personal Injury': bool_value(injury),
        'encoded_Gender': gender(genders),
        'Belts': bool_value(belt),
        'Disobedience': bool_value(disobedience),
        'encoded_DL State': random.randrange(0, 70),
        'encoded_State': random.randrange(0, 68),
        'Negligent Driving': bool_value(bad_driving),
        'encoded_Driver State': random.randrange(0, 67),
        'Mobile Phone': bool_value(phone)
    }

    x_input_feature = [[input_features[feature] for feature in sorted(input_features)]]
    rfc_model = load_model(rf_model_url)
    prd = rfc_model.predict(x_input_feature)

    if prd == 0:
        return 'Citation'
    elif prd == 1:
        return 'SERO'
    else:
        return 'Warning'


iface = gr.Interface(fn=make_predication,
                     inputs=[gr.components.Slider(minimum=2010, maximum=2023, step=1, label='Citation Year'),
                             gr.components.Slider(minimum=1, step=1, label='Number of offences found for stop'),
                             gr.components.Checkbox(label='Was a search conducted'),
                             gr.components.Checkbox(label='Any invalid documents'),
                             gr.components.Slider(minimum=1990, maximum=2023, step=1, label='Make Year'),
                             gr.components.Checkbox(label='Was alcohol involved'),
                             gr.components.Checkbox(label='Safety Standard issues in Vehicle'),
                             gr.components.Dropdown(label='Gender', choices=['Female',
                                                                             'Male',
                                                                             'Unknown']),
                             gr.components.Checkbox(label='Speeding'),
                             gr.components.Dropdown(label='Race', choices=['Asian',
                                                                           'Black',
                                                                           'Hispanic',
                                                                           'Native American',
                                                                           'Other',
                                                                           'White']),
                             gr.components.Checkbox(label='Irregularities which could contribute to accident'),
                             gr.components.Checkbox(label='Was the stop actual for an accident'),
                             gr.components.Checkbox(label='Any Property damage'),
                             gr.components.Checkbox(label='Road sign violation involvement'),
                             gr.components.Checkbox(label='Any injuries'),
                             gr.components.Checkbox(label='Seat Belts irregulation'),
                             gr.components.Checkbox(label='Any disobedience'),
                             gr.components.Checkbox(label='Bad driving'),
                             gr.components.Checkbox(label='Mobile phone')],
                     outputs=["text"])

iface.launch(debug=True)