File size: 3,112 Bytes
1fedaae 5e4ed5c abbb9cd 1fedaae 14120f0 1fedaae abbb9cd 1fedaae abbb9cd 1fedaae abbb9cd 1fedaae cae4728 1fedaae cae4728 1fedaae 111a05b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | 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"Income_bracket Prediction: {prediction_value} \n\nResult: {result}"
# Create the Gradio interface
salarybracket_ga = gr.Interface(fn=salarybracket,
inputs = [
gr.Number(13.0, 84.0, label="Age: [13 to 84]"),
gr.Number(1.0, 28.0, label="workclass: [1 to 28]"),
gr.Number(10.0, 32.0, label="education: [10 to 32]"),
gr.Number(0.0, 11.0, label="education_num: [0 to 11]"),
gr.Number(0.0, 1.0, label="marital_status: [0 or 1]"),
gr.Number(0.0, 37.0, label="occupation: [0 to 37]"),
gr.Number(0.0, 37.0, label="relationship: [0 to 37]"),
gr.Number(0.0, 1.0, label="race: [0 or 1]"),
gr.Number(0.0, 30.0, label="gender: [0 to 30]"),
gr.Number(0.0, 1.0, label="capital_gain: [0 or 1]"),
gr.Number(0.0, 19.0, label="capital_loss: [0.0 19.0]"),
gr.Number(0.0, 1.0, label="hours_per_week: [0 or 1]"),
gr.Number(0.0, 1.0, label="native_country: [0 or 1]"),
],
outputs="text", title="Salary Bracket Prediction",
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 Machine Learning",
theme='dark'
)
salarybracket_ga.launch(share=True,debug=True) |