app.py

import gradio as gr
import joblib

# 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]
    prediction = model.predict(features)
    prediction = 1
    return "Income >50K" if prediction == 1 else "Income <=50K"

# 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()