app.py

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
from tensorflow.keras.models import load_model



# # Define the prediction function
def predict(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]}
    columns = {
    "age": [age], "workclass":[workclass], "educational-num":[education], "occupation":[occupation],
    "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)
    # with open('ann_model.pkl', 'rb') as ann_model_file:
    #     ann_model = pickle.load(ann_model_file)
    scaler = StandardScaler()
    X = scaler.fit_transform(fixed_features)
    ann_model = load_model('ann_model.h5')
    prediction = ann_model.predict(fixed_features)
    # prediction = 1
    return "Income >50K" if prediction == 1 else "Income <=50K"

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']
    columns_to_encode = ['race']
    
    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])

    for N in columns_to_encode:
        race_encoded = encoder.transform(data[[N]])
        race_encoded_cols = encoder.get_feature_names_out([N])
        race_encoded_df = pd.DataFrame(race_encoded, columns=race_encoded_cols, index=data.index)
        # Combine the encoded data with original dataframe
        data = pd.concat([data.drop(N, axis=1), race_encoded_df], axis=1)

    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, 60, 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()