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import subprocess
import random
from typing import Any
import gradio as gr
import joblib
import numpy as np
import pandas as pd
OUTPUT_DATA_PATH = "data/processed/app_dataset.csv"
PREDICTIONS_PATH = "models/predictions/app_predictions.csv"
UNIQUE_VALUES_PATH = "models/other/unique_column_values.pkl"
MODEL_PATH = "models/final_model.pkl"
def predict(*args: tuple) -> Any:
app_df = pd.DataFrame(data=[args], columns=columns, index=[0])
app_df.to_csv(OUTPUT_DATA_PATH, index=False)
model = joblib.load(MODEL_PATH)
predictions = model.predict_proba(app_df)
print(predictions)
if predictions[0][0] < 0.99:
message = "Client is considered bad. Issuance of credit is not recommended."
else:
message = "Client is considered good. Issuance of credit is allowed."
return round(predictions[0][0], 3), message
columns = (
"YEARS_BIRTH",
"CODE_GENDER",
"AMT_INCOME_TOTAL",
"NAME_INCOME_TYPE",
"YEARS_EMPLOYED",
"OCCUPATION_TYPE",
"NAME_EDUCATION_TYPE",
"CNT_FAM_MEMBERS",
"CNT_CHILDREN",
"NAME_FAMILY_STATUS",
"FLAG_OWN_CAR",
"FLAG_OWN_REALTY",
"NAME_HOUSING_TYPE",
"FLAG_PHONE",
"FLAG_WORK_PHONE",
"FLAG_EMAIL",
)
unique_values = joblib.load(UNIQUE_VALUES_PATH)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
age = gr.Slider(label="Age", minimum=18, maximum=90, step=1, randomize=True)
sex = gr.Dropdown(
label="Sex",
choices=unique_values["CODE_GENDER"],
value=lambda: random.choice(unique_values["CODE_GENDER"]),
)
annual_income = gr.Slider(
label="Annual income",
minimum=0,
maximum=1000000,
step=10000,
randomize=True,
)
income_type = gr.Dropdown(
label="Income type",
choices=unique_values["NAME_INCOME_TYPE"],
value=lambda: random.choice(unique_values["NAME_INCOME_TYPE"]),
)
work_experience = gr.Slider(
label="Work experience at current position",
minimum=0,
maximum=75,
step=1,
randomize=True,
)
occupation_type = gr.Dropdown(
label="Occupation type",
choices=unique_values["OCCUPATION_TYPE"],
value=lambda: random.choice(unique_values["OCCUPATION_TYPE"]),
)
education_type = gr.Dropdown(
label="Education type",
choices=unique_values["NAME_EDUCATION_TYPE"],
value=lambda: random.choice(unique_values["NAME_EDUCATION_TYPE"]),
)
amount_of_family_members = gr.Slider(
label="Amount of family members",
minimum=0,
maximum=12,
step=1,
randomize=True,
)
amount_of_children = gr.Slider(
label="Amount of children",
minimum=0,
maximum=10,
step=1,
randomize=True,
)
with gr.Column():
family_status = gr.Dropdown(
label="Family status",
choices=unique_values["NAME_FAMILY_STATUS"],
value=lambda: random.choice(unique_values["NAME_FAMILY_STATUS"]),
)
flag_own_car = gr.Dropdown(
label="Having a car",
choices=unique_values["FLAG_OWN_REALTY"],
value=lambda: random.choice(unique_values["FLAG_OWN_REALTY"]),
)
flag_own_realty = gr.Dropdown(
label="Having a realty",
choices=unique_values["FLAG_OWN_REALTY"],
value=lambda: random.choice(unique_values["FLAG_OWN_REALTY"]),
)
housing_type = gr.Dropdown(
label="Housing type",
choices=unique_values["NAME_HOUSING_TYPE"],
value=lambda: random.choice(unique_values["NAME_HOUSING_TYPE"]),
)
flag_phone = gr.Dropdown(
label="Having a phone",
choices=unique_values["FLAG_PHONE"],
value=lambda: random.choice(unique_values["FLAG_PHONE"]),
)
flag_work_phone = gr.Dropdown(
label="Having a work phone",
choices=unique_values["FLAG_WORK_PHONE"],
value=lambda: random.choice(unique_values["FLAG_WORK_PHONE"]),
)
flag_email = gr.Dropdown(
label="Having an email",
choices=unique_values["FLAG_EMAIL"],
value=lambda: random.choice(unique_values["FLAG_EMAIL"]),
)
with gr.Column():
label_1 = gr.Label(label="Client rating")
label_2 = gr.Textbox(label="Client verdict (client is considered bad if client rating < 0.99)")
with gr.Row():
predict_btn = gr.Button(value="Predict")
predict_btn.click(
predict,
inputs=[
age,
sex,
annual_income,
income_type,
work_experience,
occupation_type,
education_type,
amount_of_family_members,
amount_of_children,
family_status,
flag_own_car,
flag_own_realty,
housing_type,
flag_phone,
flag_work_phone,
flag_email,
],
outputs=[label_1, label_2],
)
demo.launch()
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