app.py

import gradio as gr
import tensorflow as tf
import numpy as np

# Load the pickled model
model = tf.keras.models.load_model("census.h5")

# Define the function for making predictions
def salarybracket(age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country):
    inputs = np.array([[age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country]])
    prediction = model.predict(inputs)
    prediction_value = prediction[0][0]  # Assuming the prediction is a scalar
    result = "Income_bracket lesser than or equal to 50K ⬇️" if prediction_value <= 0.5 else "Income_bracket greater than 50K ⬆️"
    return f"{result}"

    
# Create the Gradio interface
salarybracket_ga = gr.Interface(fn=salarybracket, 
                               inputs = [
                                            gr.Number(17, 90, label="Age [17 to 90]"),
                                            gr.Number(0, 8, label="Workclass [0 to 8]"),
                                            gr.Number(0, 15, label="Education [0 to 15]"),
                                            gr.Number(1, 16, label="Education Num [1 to 16]"),
                                            gr.Number(0, 6, label="Marital Status [0 to 6]"),
                                            gr.Number(0, 14, label="Occupation [0 to 14]"),
                                            gr.Number(0, 5, label="Relationship [0 to 5]"),
                                            gr.Number(0, 4, label="Race [0 to 4]"),
                                            gr.Number(0, 1, label="Gender [0 to 1]"),
                                            gr.Number(0, 99999, label="Capital Gain [0 to 99999]"),
                                            gr.Number(0, 4356, label="Capital Loss [0 to 4356]"),
                                            gr.Number(1, 99, label="Hours per Week [1 to 99]"),
                                            gr.Number(0, 40, label="Native Country [0 to 40]"),
                                        ],
                                outputs="text", title="Salary Bracket Prediction - Income <=50k or >50K ", 
                               examples = [
                                            [75,0,0,6,6,0,2,1,0,0,0,1,3,0,0],
                                            [25,4,11,9,2,13,2,4,0,0,0,48,38,1,1],
                                            [29,4,1,7,4,3,3,2,1,0,0,40,14,0,0],
                                            [51,5,12,14,2,4,0,4,1,15024,0,50,38,1,1],
                                            [66,0,15,10,2,0,0,4,1,0,1825,40,38,1,1],
                                        ],
                                description="Predicting Income_bracket Prediction Using TensorFlow",
                                theme='dark'
                               )

salarybracket_ga.launch(share=True,debug=True)