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Create app.py

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  1. app.py +242 -0
app.py ADDED
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+ import gradio as gr
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+ import torch, numpy as np, pandas as pd
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+ import skimage
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+ import pickle
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+
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+ defaultColumns = ['MSSubClass',
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+ 'MSZoning',
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+ 'LotFrontage',
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+ 'LotArea',
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+ 'Street',
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+ 'Alley',
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+ 'LotShape',
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+ 'LandContour',
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+ 'Utilities',
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+ 'LotConfig',
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+ 'LandSlope',
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+ 'Neighborhood',
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+ 'Condition1',
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+ 'Condition2',
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+ 'BldgType',
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+ 'HouseStyle',
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+ 'OverallQual',
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+ 'OverallCond',
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+ 'YearBuilt',
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+ 'YearRemodAdd',
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+ 'RoofStyle',
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+ 'RoofMatl',
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+ 'Exterior1st',
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+ 'Exterior2nd',
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+ 'MasVnrType',
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+ 'MasVnrArea',
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+ 'ExterQual',
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+ 'ExterCond',
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+ 'Foundation',
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+ 'BsmtQual',
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+ 'BsmtCond',
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+ 'BsmtExposure',
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+ 'BsmtFinType1',
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+ 'BsmtFinSF1',
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+ 'BsmtFinType2',
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+ 'BsmtFinSF2',
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+ 'BsmtUnfSF',
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+ 'TotalBsmtSF',
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+ 'Heating',
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+ 'HeatingQC',
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+ 'CentralAir',
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+ 'Electrical',
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+ '1stFlrSF',
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+ '2ndFlrSF',
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+ 'LowQualFinSF',
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+ 'GrLivArea',
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+ 'BsmtFullBath',
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+ 'BsmtHalfBath',
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+ 'FullBath',
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+ 'HalfBath',
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+ 'BedroomAbvGr',
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+ 'KitchenAbvGr',
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+ 'KitchenQual',
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+ 'TotRmsAbvGrd',
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+ 'Functional',
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+ 'Fireplaces',
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+ 'FireplaceQu',
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+ 'GarageType',
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+ 'GarageYrBlt',
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+ 'GarageFinish',
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+ 'GarageCars',
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+ 'GarageArea',
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+ 'GarageQual',
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+ 'GarageCond',
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+ 'PavedDrive',
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+ 'WoodDeckSF',
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+ 'OpenPorchSF',
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+ 'EnclosedPorch',
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+ '3SsnPorch',
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+ 'ScreenPorch',
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+ 'PoolArea',
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+ 'PoolQC',
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+ 'Fence',
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+ 'MiscFeature',
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+ 'MiscVal',
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+ 'MoSold',
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+ 'YrSold',
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+ 'SaleType',
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+ 'SaleCondition']
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+
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+ categorical_columns = ['MSZoning',
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+ 'Street',
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+ 'Alley',
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+ 'LotShape',
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+ 'LandContour',
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+ 'Utilities',
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+ 'LotConfig',
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+ 'LandSlope',
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+ 'Neighborhood',
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+ 'Condition1',
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+ 'Condition2',
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+ 'BldgType',
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+ 'HouseStyle',
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+ 'RoofStyle',
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+ 'RoofMatl',
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+ 'Exterior1st',
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+ 'Exterior2nd',
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+ 'MasVnrType',
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+ 'ExterQual',
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+ 'ExterCond',
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+ 'Foundation',
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+ 'BsmtQual',
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+ 'BsmtCond',
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+ 'BsmtExposure',
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+ 'BsmtFinType1',
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+ 'BsmtFinType2',
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+ 'Heating',
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+ 'HeatingQC',
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+ 'CentralAir',
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+ 'Electrical',
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+ 'KitchenQual',
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+ 'Functional',
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+ 'FireplaceQu',
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+ 'GarageType',
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+ 'GarageFinish',
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+ 'GarageQual',
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+ 'GarageCond',
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+ 'PavedDrive',
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+ 'PoolQC',
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+ 'Fence',
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+ 'MiscFeature',
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+ 'SaleType',
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+ 'SaleCondition']
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+
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+ with open("model.pkl", "rb") as f:
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+ model = pickle.load(f)
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+
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+ def house_price(LotArea, LotFrontage, YearBuilt, GrLivArea, GarageArea):
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+
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+ lot_area = float(LotArea)
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+ lot_frontage = float(LotFrontage)
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+ year_built = float(YearBuilt)
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+ gr_liv_area = float(GrLivArea)
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+ garage_area = float(GarageArea)
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+
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+ default = [20,
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+ 'RL',
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+ lot_frontage,
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+ lot_area,
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+ 'Pave',
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+ 'Grvl',
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+ 'Reg',
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+ 'Lvl',
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+ 'AllPub',
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+ 'Inside',
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+ 'Gtl',
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+ 'NAmes',
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+ 'Norm',
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+ 'Norm',
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+ '1Fam',
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+ '1Story',
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+ 5,
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+ 5,
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+ year_built,
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+ 1950,
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+ 'Gable',
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+ 'CompShg',
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+ 'VinylSd',
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+ 'VinylSd',
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+ 'None',
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+ 0.0,
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+ 'TA',
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+ 'TA',
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+ 'PConc',
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+ 'TA',
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+ 'TA',
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+ 'No',
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+ 'Unf',
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+ 0.0,
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+ 'Unf',
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+ 0.0,
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+ 0.0,
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+ 0.0,
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+ 'GasA',
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+ 'Ex',
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+ 'Y',
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+ 'SBrkr',
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+ 864,
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+ 0,
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+ 0,
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+ gr_liv_area,
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+ 0.0,
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+ 0.0,
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+ 2,
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+ 0,
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+ 3,
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+ 1,
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+ 'TA',
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+ 6,
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+ 'Typ',
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+ 0,
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+ 'Gd',
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+ 'Attchd',
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+ 2005.0,
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+ 'Unf',
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+ 2.0,
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+ garage_area,
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+ 'TA',
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+ 'TA',
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+ 'Y',
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+ 0,
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+ 0,
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+ 0,
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+ 0,
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+ 0,
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+ 0,
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+ 'Gd',
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+ 'MnPrv',
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+ 'Shed',
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+ 0,
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+ 6,
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+ 2007,
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+ 'WD',
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+ 'Normal']
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+
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+ df=pd.DataFrame([default], columns = defaultColumns)
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+ df[categorical_columns] = df[categorical_columns].astype("category")
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+ df[categorical_columns] = df[categorical_columns].apply(lambda x: x.cat.codes)
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+ df = (df - df.mean()) / df.std()
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+
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+ predictions = model.predict(test)
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+
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+ return predictions[0]
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+
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+ iface = gr.Interface(
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+ fn=car_purchase,
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+ title="House Prices",
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+ allow_flagging="never",
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+ inputs=[
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+ gr.inputs.Number(default=9600, label="LotArea"),
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+ gr.inputs.Number(default=60.0, label="LotFrontage"),
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+ gr.inputs.Number(default=2005, label="YearBuilt"),
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+ gr.inputs.Number(default=864, label="GrLivArea"),
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+ gr.inputs.Number(default=730, label="GarageArea"),
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+ ],
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+ outputs="text")
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+ iface.launch()