Delete car_predictions.py

car_predictions.py

@@ -1,77 +0,0 @@
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-from sklearn.preprocessing import LabelEncoder, MinMaxScaler
-from keras.models import Sequential
-from keras.layers import Dense, Dropout
-from flask import Flask, request, jsonify
-
-app = Flask(__name__)
-
-# Загрузка и подготовка данных
-data = pd.read_csv('cars_raw.csv')
-
-# Предобработка данных
-le = LabelEncoder()
-data['Make'] = le.fit_transform(data['Make'])
-data['Model'] = le.fit_transform(data['Model'])
-data = data[data['Price'] != 'Not Priced']
-data["Price"] = data["Price"].str.replace("$", "").str.replace(",", "").astype(float)
-
-scaler = MinMaxScaler()
-data['Price'] = scaler.fit_transform(data['Price'].values.reshape(-1, 1))
-
-data = data.dropna()
-for col in data.select_dtypes(include=['category', 'object']).columns:
-    data[col] = le.fit_transform(data[col])
-
-for col in data.select_dtypes(include=['number']).columns:
-    data[col] = scaler.fit_transform(data[col].values.reshape(-1, 1))
-
-data = data.drop(columns=["Mileage", "SellerType", "VIN", "Stock#", "Drivetrain", 
-                          "SellerName", "ConsumerReviews", "ExteriorStylingRating", 
-                          "State", "Zipcode", "DealType"])
-data_df = pd.DataFrame(data)
-
-X = data.drop('Price', axis=1)
-y = data['Price']
-
-# Создание и обучение модели
-model = Sequential()
-model.add(Dense(128, input_shape=(X.shape[1],)))
-model.add(Dropout(0.3))
-model.add(Dense(64))
-model.add(Dropout(0.3))
-model.add(Dense(1))
-
-model.compile(optimizer='adam', loss='mean_squared_error')
-model.fit(X, y, epochs=100, batch_size=32)
-
-# Добавление маршрута для главной страницы
-@app.route('/')
-def home():
-    return "Добро пожаловать на сайт по предсказанию цен на поддержанные автомобили"
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    car_data = request.json
-    car_data_df = pd.DataFrame([car_data])
-
-    # Предобработка входных данных
-    for col in car_data_df.select_dtypes(include=['category', 'object']).columns:
-        car_data_df[col] = le.transform(car_data_df[col])
-    
-    for col in car_data_df.select_dtypes(include=['number']).columns:
-        car_data_df[col] = scaler.transform(car_data_df[col].values.reshape(-1, 1))
-
-    # Получаем предсказание
-    prediction = model.predict(car_data_df)
-    inverted_prediction = scaler.inverse_transform(prediction.reshape(-1, 1))
-    predicted_price = inverted_prediction[0][0]
-
-    return jsonify({'predicted_price': predicted_price})
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5000)
-
-model.save("test_model.keras")