70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
|
from flask import Flask, render_template, request
|
||
|
|
import pandas as pd
|
||
|
|
from sklearn.model_selection import train_test_split
|
||
|
|
from sklearn.neural_network import MLPClassifier
|
||
|
|
from sklearn.preprocessing import OneHotEncoder
|
||
|
|
import joblib
|
||
|
|
|
||
|
|
app = Flask(__name__)
|
||
|
|
|
||
|
|
# Загрузка данных
|
||
|
|
data = pd.read_csv('top_240_restaurants_recommended_in_los_angeles_2.csv')
|
||
|
|
|
||
|
|
# Выбор нужных столбцов
|
||
|
|
selected_columns = ['Rank', 'StarRating', 'NumberOfReviews', 'Style']
|
||
|
|
data = data[selected_columns]
|
||
|
|
|
||
|
|
# Кодирование столбца Style
|
||
|
|
encoder = OneHotEncoder(sparse=False)
|
||
|
|
encoded_styles = encoder.fit_transform(data[['Style']])
|
||
|
|
encoded_styles_df = pd.DataFrame(encoded_styles, columns=encoder.get_feature_names_out(['Style']))
|
||
|
|
data = pd.concat([data, encoded_styles_df], axis=1).drop('Style', axis=1)
|
||
|
|
|
||
|
|
# Разделение данных
|
||
|
|
X = data.drop('Rank', axis=1)
|
||
|
|
y = data['Rank']
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
|
||
|
|
|
||
|
|
# Обучение модели
|
||
|
|
mlp_model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=1000)
|
||
|
|
mlp_model.fit(X_train, y_train)
|
||
|
|
|
||
|
|
# Сохранение модели
|
||
|
|
joblib.dump(mlp_model, 'mlp_model.joblib')
|
||
|
|
|
||
|
|
# Загрузка модели
|
||
|
|
mlp_model = joblib.load('mlp_model.joblib')
|
||
|
|
|
||
|
|
|
||
|
|
@app.route('/')
|
||
|
|
def index():
|
||
|
|
return render_template('index.html')
|
||
|
|
|
||
|
|
|
||
|
|
@app.route('/predict', methods=['POST'])
|
||
|
|
def predict():
|
||
|
|
if request.method == 'POST':
|
||
|
|
# Получение данных из формы
|
||
|
|
input_data = {
|
||
|
|
'StarRating': float(request.form['StarRating']),
|
||
|
|
'NumberOfReviews': int(request.form['NumberOfReviews']),
|
||
|
|
'Style': request.form['Style']
|
||
|
|
}
|
||
|
|
|
||
|
|
# Кодирование стиля
|
||
|
|
input_style_encoded = encoder.transform([[input_data['Style']]])
|
||
|
|
input_data.pop('Style')
|
||
|
|
input_data.update(dict(zip(encoded_styles_df.columns, input_style_encoded[0])))
|
||
|
|
|
||
|
|
# Преобразование данных в DataFrame
|
||
|
|
input_df = pd.DataFrame([input_data])
|
||
|
|
|
||
|
|
# Предсказание
|
||
|
|
prediction = mlp_model.predict(input_df)[0]
|
||
|
|
|
||
|
|
return render_template('index.html', prediction=prediction)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
app.run(debug=True)
|