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import gradio as gr
import joblib
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder
from sklearn.impute import KNNImputer
from sklearn.decomposition import PCA
import pickle
# Load your saved model
# model = joblib.load("ann_model.joblib")
# # Define the prediction function
def predict(age, workclass, education, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country):
features = [age, workclass, education, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country]
columns = {
"age": [age], "workclass":[workclass], "educational-num":[education], "marital-status":[marital_status], "occupation":[occupation],
"relationship":[relationship], "race":[race], "gender":[gender], "capital-gain":[capital_gain], "capital-loss":[capital_loss],
"hours-per-week":[hours_per_week], "native-country":[native_country]}
df = pd.DataFrame(data=columns)
fixed_features = cleaning_features(df)
# prediction = model.predict(features)
# prediction = 1
# return "Income >50K" if prediction == 1 else "Income <=50K"
return print(fixed_features)
def cleaning_features(data):
# with open('race_onehot_encoder.pkl', 'rb') as enc_file:
# encoder = pickle.load(enc_file)
with open('label_encoder_work.pkl', 'rb') as le_file:
le_work = pickle.load(le_file)
with open('label_encoder_occ.pkl', 'rb') as le_file:
le_occ = pickle.load(le_file)
with open('scaler.pkl', 'rb') as scaler_file:
scaler = pickle.load(scaler_file)
education_num_mapping = {
"Preschool": 1,
"1st-4th": 2,
"5th-6th": 3,
"7th-8th": 4,
"9th": 5,
"10th": 6,
"11th": 7,
"12th": 8,
"HS-grad": 9,
"Some-college": 10,
"Assoc-voc": 11,
"Assoc-acdm": 12,
"Bachelors": 13,
"Masters": 14,
"Doctorate": 15,
"Prof-school": 16
}
gender_mapping = {"Male":1,"Female":0}
country_mapping = {"United-States":1,"Other":0}
numeric_cols = ['age', 'educational-num', 'hours-per-week']
columns_to_encode = ['race','marital-status','relationship']
data['workclass'] = le_work.transform(data['workclass'])
data['occupation'] = le_occ.transform(data['occupation'])
data['gender'] = data['gender'].map(gender_mapping)
data['native-country'] = data['native-country'].map(country_mapping)
data['educational-num'] = data['educational-num'].map(education_num_mapping)
data[numeric_cols] = scaler.transform(data[numeric_cols])
#data = pca(data)
return data
# def pca(data):
# encoder = OneHotEncoder(sparse_output=False)
# one_hot_encoded = encoder.fit_transform(data[['workclass', 'occupation']])
# encoded_columns_df = pd.DataFrame(one_hot_encoded, columns=encoder.get_feature_names_out())
# pca_net = PCA(n_components=10)
# pca_result_net = pca_net.fit_transform(encoded_columns_df)
# pca_columns = [f'pca_component_{i+1}' for i in range(10)]
# pca_df = pd.DataFrame(pca_result_net, columns=pca_columns)
# data = data.drop(columns=['workclass', 'occupation'], axis=1) #remove the original columns
# data = pd.concat([data, pca_df], axis=1)
# return data
def pca(data):
encoder_pkl = 'onehot_encoder.pkl'
pca_model_pkl = 'pca.pkl'
with open(pca_model_pkl, 'rb') as file:
pca_model = pickle.load(file)
with open(encoder_pkl, 'rb') as file:
encoder = pickle.load(file)
one_hot_encoded = encoder.transform(data[['workclass', 'occupation']])
encoded_columns_df = pd.DataFrame(one_hot_encoded, columns=encoder.get_feature_names_out())
pca_result_net = pca_model.transform(encoded_columns_df)
pca_columns = [f'pca_component_{i+1}' for i in range(pca_model.n_components_)]
pca_df = pd.DataFrame(pca_result_net, columns=pca_columns)
data = data.drop(columns=['workclass', 'occupation'], axis=1)
data = pd.concat([data, pca_df], axis=1)
return data
def hbdscan_tranform(df_transformed):
df_transformed['capital-gain'] = np.log1p(df_transformed['capital-gain'])
df_transformed['capital-loss'] = np.log1p(df_transformed['capital-loss'])
# Apply RobustScaler to all numerical features
numerical_features = ['age', 'capital-gain', 'capital-loss', 'hours-per-week']
scaler = RobustScaler()
df_transformed[numerical_features] = scaler.fit_transform(df_transformed[numerical_features])
return df_transformed
# Create the Gradio interface
interface = gr.Interface(
fn=predict,
inputs=[
gr.Slider(18, 90, step=1, label="Age"),
gr.Dropdown(
["Private", "Self-emp-not-inc", "Self-emp-inc", "Federal-gov",
"Local-gov", "State-gov", "Without-pay", "Never-worked"],
label="Workclass"
),
gr.Dropdown(
["Bachelors", "Some-college", "11th", "HS-grad", "Prof-school",
"Assoc-acdm", "Assoc-voc", "9th", "7th-8th", "12th", "Masters",
"1st-4th", "10th", "Doctorate", "5th-6th", "Preschool"],
label="Education"
),
gr.Dropdown(
["Married-civ-spouse", "Divorced", "Never-married", "Separated",
"Widowed", "Married-spouse-absent", "Married-AF-spouse"],
label="Marital Status"
),
gr.Dropdown(
["Tech-support", "Craft-repair", "Other-service", "Sales",
"Exec-managerial", "Prof-specialty", "Handlers-cleaners",
"Machine-op-inspct", "Adm-clerical", "Farming-fishing",
"Transport-moving", "Priv-house-serv", "Protective-serv",
"Armed-Forces"],
label="Occupation"
),
gr.Dropdown(
["Wife", "Husband", "Own-child", "Unmarried", "Other-relative", "Not-in-family"],
label="Relationship"
),
gr.Dropdown(
["White", "Black", "Asian-Pac-Islander", "Amer-Indian-Eskimo", "Other"],
label="Race"
),
gr.Dropdown(
["Male", "Female"],
label="Gender"
),
gr.Slider(1, 90, step=1, label="Hours Per Week"),
gr.Slider(0, 100000, step=100, label="Capital Gain"),
gr.Slider(0, 5000, step=50, label="Capital Loss"),
gr.Dropdown(
["United-States", "Other"],
label="Native Country"
)
],
outputs="text",
title="Adult Income Predictor"
)
# Launch the app
interface.launch()