Update app.py

app.py

@@ -1,5 +1,6 @@
 import pandas as pd
 import numpy as np
+import pickle
 #from scipy import stats
 from sklearn.preprocessing import MinMaxScaler, StandardScaler, PolynomialFeatures
 from sklearn.linear_model import Ridge, ElasticNet, LinearRegression, Lasso
@@ -8,27 +9,32 @@ from sklearn.model_selection import train_test_split
 #import dtale
 import gradio as gr
 
-# Load the dataset
-df = pd.read_csv('ebw_data.csv')
+# # Load the dataset
+# df = pd.read_csv('ebw_data.csv')
 
-X = df.drop(['Width', 'Depth'], axis=1)
-y = df[['Width', 'Depth']]
+# X = df.drop(['Width', 'Depth'], axis=1)
+# y = df[['Width', 'Depth']]
 
-# Разделим данные на трэйн и тест
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
+# # Разделим данные на трэйн и тест
+# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
 
-# Создайте экземпляр модели линейной регрессии.
-model = LinearRegression()
+# # Создайте экземпляр модели линейной регрессии.
+# model = LinearRegression()
 
-# Фитим
-model.fit(X_train, y_train)
+# # Фитим
+# model.fit(X_train, y_train)
 
-# Предиктим
-y_pred = model.predict(X_test)
+# # Предиктим
+# y_pred = model.predict(X_test)
+
+# # Оценка производительности модели
+# score = model.score(X_test, y_test)
+# #print('Accuracy:', score)
+filename = 'finalized_model.sav'
+model = pickle.load(open(filename, 'rb'))
+#result = loaded_model.score(X_test, Y_test)
+# print(result)
 
-# Оценка производительности модели
-score = model.score(X_test, y_test)
-#print('Accuracy:', score)
 def greet(IW, IF, VW, FP):
     X_new = pd.DataFrame({'IW': [IW], 'IF': [IF], 'VW': [VW], 'FP': [FP]})
     y_predd = model.predict(X_new)