File size: 1,464 Bytes
4cf5a45 381f844 4cf5a45 381f844 4cf5a45 381f844 |
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 |
import gradio as gr
from joblib import load
# Load your saved model and scaler
scaler = load('scaler.joblib')
best_knn_model = load('best_knn_model.joblib')
# Define the prediction function
def predict_house_price(longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income):
inputs = [[longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income]]
scaled_inputs = scaler.transform(inputs)
prediction = best_knn_model.predict(scaled_inputs)[0]
return f"${prediction:,.2f}"
input_longitude = gr.Slider(label="Longitude")
input_latitude = gr.Slider(label="Latitude")
input_housing_med_age = gr.Slider(label="Housing Median Age")
input_totla_rooms = gr.Slider(label="Total Rooms")
input_total_bedRooms = gr.Slider(label="Total Bedrooms")
input_popu = gr.Slider(label="Population")
input_households = gr.Slider(label="Households")
input_med_income = gr.Slider(label="Median Income")
# output modules
output_predicted_value = gr.Textbox(label="Predicted Median House Value")
gr.Interface(
fn=predict_house_price,
inputs=[
input_longitude,
input_latitude,
input_housing_med_age,
input_totla_rooms,
input_total_bedRooms,
input_popu,
input_households,
input_med_income
],
outputs=gr.Textbox(label="Predicted Median House Value"),
).launch() |