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1fadb8f ab9e52b 1fadb8f 68535f8 1fadb8f 68535f8 1fadb8f | 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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | 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() |