Create app.py

app.py

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+import pandas as pd
+from scipy import stats
+from sklearn.preprocessing import MinMaxScaler, StandardScaler, PolynomialFeatures
+from sklearn.linear_model import Ridge, ElasticNet, LinearRegression, Lasso
+from sklearn.model_selection import train_test_split
+import sweetviz as sv
+import dtale
+import gradio as gr
+
+# Load the dataset
+df = pd.read_csv('ebw_data.csv')
+
+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)
+
+# Создайте экземпляр модели линейной регрессии.
+model = LinearRegression()
+
+# Фитим
+model.fit(X_train, y_train)
+
+# Предиктим
+y_pred = model.predict(X_test)
+
+# 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)
+    arr_reshaped = np.reshape(y_predd, (2, 1))
+    arr1, arr2 = np.split(arr_reshaped, 2)
+    value1 = arr1[0]
+    value2 = arr2[0]
+    return value1, value2
+
+inputs = [gr.Slider(43, 49), gr.Slider(131, 150), gr.Slider(4.5, 10), gr.Slider(50, 125)]
+outputs = [gr.Number(label="Width"), gr.Number(label="Depth")]
+
+demo = gr.Interface(
+    fn=greet,
+    inputs=inputs,
+    outputs=outputs,
+    title="Predict Depth and Width"
+)
+demo.launch()