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import importlib | |
from tkinter import Label | |
import gradio as gd | |
import pandas as pd | |
import numpy as np | |
from joblib import load | |
def predict_price( | |
id,date,bedrooms,bathrooms,sqft_living, | |
sqft_lot,floors,waterfront,view,condition, | |
grade,sqft_above,sqft_basement,yr_built,yr_renovated, | |
zipcode,lat,long,sqft_living15,sqft_lot15 | |
): | |
model=load("housedata.jb") | |
# Create dict array from parameters | |
data={ | |
'id':[id], | |
'date':[date], | |
'bedrooms':[bedrooms], | |
'bathrooms':[bathrooms], | |
'sqft_living':[sqft_living], | |
'sqft_lot':[sqft_lot], | |
'floors':[floors], | |
'waterfront':[waterfront], | |
'view':[view], | |
'condition':[condition], | |
'grade':[grade], | |
'sqft_above':[sqft_above], | |
'sqft_basement':[sqft_basement], | |
'yr_built':[yr_built], | |
'yr_renovated':[yr_renovated], | |
'zipcode':[zipcode], | |
'lat':[lat], | |
'long':[long], | |
'sqft_living15':[sqft_living15], | |
'sqft_lot15':[sqft_lot15] | |
} | |
xin=pd.DataFrame(data) | |
Price=model.predict(xin) | |
return Price[0] | |
ui=gd.Interface( | |
fn=predict_price, | |
inputs=[ | |
gd.inputs.Textbox(type="text", placeholder="id",label="ID"), | |
gd.inputs.Textbox(type="text", placeholder="date",label="DATE"), | |
gd.inputs.Textbox(type="text", placeholder="bedrooms",numeric=True,label="BEDROOMS"), | |
gd.inputs.Textbox(type="text", placeholder="bathrooms",numeric=True,label="BATHROOMS"), | |
gd.inputs.Textbox(type="text", placeholder="sqft_living",numeric=True,label="SQFT_LIVING"), | |
gd.inputs.Textbox(type="text", placeholder="sqft_lot",numeric=True,label="SQFT_LOT"), | |
gd.Dropdown([1. , 2. , 1.5, 3. , 2.5, 3.5],label="FLOORS"), | |
gd.Dropdown([0,1],label="WATERFRONT"), | |
gd.Dropdown([0, 1, 2, 3, 4],label="VIEW"), | |
gd.Dropdown([1,2,3,4,5],label="CONDITION"), | |
gd.inputs.Textbox(type="text", placeholder="grade",numeric=True,label="GRADE"), | |
gd.inputs.Textbox(type="text", placeholder="sqft_above",numeric=True,label="SQFT_ABOVE"), | |
gd.inputs.Textbox(type="text", placeholder="sqft_basement",numeric=True,label="SQFT_BASEMENT"), | |
gd.inputs.Textbox(type="text", placeholder="yr_built",numeric=True,label="YR_BUILT"), | |
gd.inputs.Textbox(type="text", placeholder="yr_renovated",numeric=True,label="YR_RENOVATED"), | |
gd.inputs.Textbox(type="text", placeholder="zipcode",label="ZIPCODE"), | |
gd.inputs.Textbox(type="text", placeholder="lat",numeric=True,label="LATITUDE"), | |
gd.inputs.Textbox(type="text", placeholder="long",numeric=True,label="LONGITUDE"), | |
gd.inputs.Textbox(type="text", placeholder="sqft_living15",numeric=True,label="SQFT_LIVING15"), | |
gd.inputs.Textbox(type="text", placeholder="sqft_lot15",numeric=True,label="SQFT_LOT15"), | |
], | |
title="HOUSE PRICE PREDICTOR", | |
outputs="text", | |
examples=[["7129300520","20141013T000000",3,1,1180,5650,"1",0,0,3,7,1180,0,1955,0,"98178",47.5112,-122.257,1340,5650], | |
["9297300055","20150124T000000",4,3,2950,5000,"2",0,3,3,9,1980,970,1979,0,"98126",47.5714,-122.375,2140,4000], | |
["0065000400","20141022T000000",4,3,1490,6766,"1.5",0,1,5,7,1490,0,1915,0,"98136",47.5446,-122.382,1990,6526]] | |
) | |
if __name__=="__main__": | |
ui.launch() |