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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.inputs.Textbox(label = "Longitude") | |
latitude = gr.inputs.Textbox(label = "Latitude") | |
housing_median_age = gr.inputs.Textbox(label = "Housing median age") | |
total_rooms = gr.inputs.Textbox(label = "total rooms") | |
total_bedrooms = gr.inputs.Textbox(label = "total bedrooms") | |
population = gr.inputs.Textbox(label = "population") | |
households = gr.inputs.Textbox(label = "housholds") | |
median_income = gr.inputs.Textbox(label = "median income") | |
output_house_value = gr.inputs.Textbox(label = "predicted house value") | |
def process_function(longitude,latitude,housing_medain_age,total_rooms,total_bedrooms,population,households,median_income): | |
housing=pd.read_csv('/content/drive/MyDrive/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.drop("median_house_value", axis=1) | |
scaler = MinMaxScaler() | |
scaler.fit(train_features) | |
train_features_normalized = scaler.transform(train_features) | |
lin_reg=LinearRegression() | |
lin_reg.fir(train_features_normalized,train_labels) | |
new_features=np.array([[longitude,latitude,housing_medain_age,total_rooms,population,households,median_income]]) | |
new_features_normalized=scaler.transform(new_features) | |
output_house_value=lin_reg.predict(new_features_normalized) | |
return output_house_value | |
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(share=True, debug=True) |