File size: 1,635 Bytes
76a416c a140c46 732bb5e 76a416c 2f8142e 76a416c dd8b9c6 76a416c bcc09bd 76a416c a140c46 76a416c a140c46 76a416c 732bb5e | 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 47 48 49 50 | import gradio as gr
import pandas as pd
import numpy as np
import pickle
with open("linear_regression_model.pkl", "rb") as file:
lin_reg = pickle.load(file)
# define the input module
inputs = [
gr.components.Number(label="Longitude"),
gr.components.Number(label="Latitude"),
gr.components.Number(label="Housing Median Age"),
gr.components.Number(label="Total Rooms"),
gr.components.Number(label="Total Bedrooms"),
gr.components.Number(label="Population"),
gr.components.Number(label="Households"),
gr.components.Number(label="Median Income")
]
# define the output module
outputs = gr.components.Textbox(label = 'Output')
# define the backend function
from sklearn.preprocessing import MinMaxScaler
def predict_price(longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income):
features = pd.DataFrame({
'longitude': [longitude],
'latitude': [latitude],
'housing_median_age': [housing_median_age],
'total_rooms': [total_rooms],
'total_bedrooms': [total_bedrooms],
'population': [population],
'households': [households],
'median_income': [median_income]
})
scaler = MinMaxScaler() ## define the transformer
# load your scaler from the training dataset
features=scaler.fit_transform(features)
linear_regression_prediction = lin_reg.predict(features)[0]
#decision_tree_prediction = tree_reg.predict(features)[0]
return linear_regression_prediction
interface = gr.Interface(fn=predict_price, inputs=inputs, outputs=outputs).launch(debug=True)
|