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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 | |
| # 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] | |
| fixed_features = cleaning_features(features) | |
| prediction = model.predict(features) | |
| prediction = 1 | |
| return "Income >50K" if prediction == 1 else "Income <=50K" | |
| def cleaning_features(data): | |
| le = LabelEncoder() | |
| scaler = StandardScaler() | |
| encoder = OneHotEncoder(sparse_output=False) | |
| numeric_cols = ['age', 'educational-num', 'hours-per-week'] | |
| columns_to_encode = ['race','marital-status','relationship'] | |
| data.replace({'?': np.nan, 99999: np.nan}, inplace=True) | |
| # 1. Scale numerical features | |
| data[numeric_cols] = scaler.fit_transform(data[numeric_cols]) | |
| # 2. Label encode gender and income | |
| data['gender'] = le.fit_transform(data['gender']) | |
| # 3. One-hot encode race | |
| for N in columns_to_encode: | |
| race_encoded = encoder.fit_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) | |
| # Binarize native country | |
| data['native-country'] = data['native-country'].apply(lambda x: x == 'United-States') | |
| data['native-country'] = data['native-country'].astype(int) | |
| print(data.head(10)) | |
| return data, encoder, scaler | |
| # 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() | |