app.py

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
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)
filename = 'finalized_model.sav'
model = pickle.load(open(filename, 'rb'))
#result = loaded_model.score(X_test, Y_test)
# print(result)

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()