Delete app.py

app.py

@@ -1,75 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Copy of Lab06.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1eKsEZ2OurE_fRyVw_cxMPq5cuYpUkSJM
-
-We will train an XGBoost model on the Adult's Income dataset and deploy it on Hugging Face spaces.
-"""
-
-!wget http://www.donlapark.cmustat.com/Income.csv
-
-import pandas as pd
-from sklearn.compose import ColumnTransformer
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import OneHotEncoder, StandardScaler
-
-from xgboost import XGBClassifier
-
-
-EDU_DICT = {'Preschool': 1,
-            '1st-4th': 2,
-            '5th-6th': 3,
-            '7th-8th': 4,
-            '9th': 5,
-            '10th': 6,
-            '11th': 7,
-            '12th': 8,
-            'HS-grad': 9, 
-            'Some-college': 10,
-            'Assoc-voc': 11,
-            'Assoc-acdm': 12,
-            'Bachelors': 13,
-            'Masters': 14,
-            'Prof-school': 15,
-            'Doctorate': 16
-            }
-
-
-X_train = pd.read_csv('Income.csv')
-
-X_train
-
-y_train = X_train.pop("income")
-y_train = (y_train == ">50K").astype(int)
-X_train['education'].replace(EDU_DICT, inplace=True)
-
-# Names of numerical features
-num_col = X_train.select_dtypes(include=['int64', 'float64']).columns
-# Names of categorical features
-cat_col = X_train.select_dtypes(include=['object', 'bool']).columns
-
-print(num_col)
-print(cat_col)
-
-# print num_col and cat_col
-
-preprocessor = ColumnTransformer([("scaler", StandardScaler(), num_col), 
-                                  ("onehot", OneHotEncoder(sparse=False), cat_col)])
-
-model = Pipeline(steps=[('preprocessor', preprocessor), 
-                        ('classifier', XGBClassifier())])  
-
-model.fit(X_train, y_train)
-
-"""### Saving the model"""
-
-import joblib
-
-joblib.dump(model, 'model.joblib')
-
-unique_values = {col:X_train[col].unique() for col in cat_col}
-
-joblib.dump(unique_values, 'unique_values.joblib')