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import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression
import gradio as gr
longitude = gr.Textbox(label="Longitude")
latitude = gr.Textbox(label="Latitude")
housing_median_age = gr.Textbox(label="Housing median age")
total_rooms = gr.Textbox(label="Total rooms")
total_bedrooms = gr.Textbox(label="Total bedrooms")
population = gr.Textbox(label="Population")
households = gr.Textbox(label="Households")
median_income = gr.Textbox(label="Median income")
output_house_value = gr.Textbox(label="Predicted house value")
def process_function(longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income):
housing = pd.read_csv('housing.csv')
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=10)
train_set_clean = train_set.dropna(subset=["total_bedrooms"])
train_labels = train_set_clean["median_house_value"].copy()
train_features = train_set_clean[["longitude", "latitude", "housing_median_age", "total_rooms","total_bedrooms","population", "households", "median_income"]]
scaler = MinMaxScaler()
scaler.fit(train_features)
train_features_normalized = scaler.transform(train_features)
lin_reg = LinearRegression()
lin_reg.fit(train_features_normalized, train_labels)
new_features = np.array([[float(longitude), float(latitude), float(housing_median_age), float(total_rooms), float(total_bedrooms), float(population), float(households), float(median_income)]])
new_features_normalized = scaler.transform(new_features)
output_house_value = lin_reg.predict(new_features_normalized)
return str(output_house_value[0])
myexamples = [["-116.52", "33.82", "21.0", "10227.0", "2315.0", "3623.0", "1734.0", "2.5212"]]
iface = gr.Interface(
fn=process_function,
inputs=[longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income],
outputs=output_house_value,
examples=myexamples,
)
iface.launch(debug=True)
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