Delete app.py

app.py

@@ -1,84 +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 sklearn.model_selection import train_test_split
-import gradio as gr
-
-data = pd.read_csv('cars_raw.csv')
-
-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"])
-X = data.drop('Price', axis=1)
-y = data['Price']
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
-
-model = Sequential()
-model.add(Dense(128, input_shape=(X_train.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_train, y_train, epochs=100, batch_size=32, validation_data=(X_test, y_test))
-
-def predict_price(year, make, model_name, fuel_type, engine):
-    car_data = {
-        "Year": [year],
-        "Make": [make],
-        "Model": [model_name],
-        "FuelType": [fuel_type],
-        "Engine": [engine]
-    }
-
-    car_data_df = pd.DataFrame(car_data)
-    
-    needs_df = pd.concat([X, car_data_df], ignore_index=True)
-
-    for col in needs_df.select_dtypes(include=['category', 'object']).columns:
-        needs_df[col] = le.fit_transform(needs_df[col])
-
-    for col in needs_df.select_dtypes(include=['number']).columns:
-        needs_df[col] = scaler.fit_transform(needs_df[col].values.reshape(-1, 1))
-
-    result_df = needs_df.loc[[0]]
-    prediction = model.predict(result_df)
-
-    inverted_prediction = scaler.inverse_transform(prediction.reshape(-1, 1))
-    predicted_price = inverted_prediction[0][0]
-
-    return f'Предсказанная цена: ${predicted_price:.2f}'
-
-iface = gr.Interface(
-    fn=predict_price,
-    inputs=[
-        gr.inputs.Number(label="Год издания машины"),
-        gr.inputs.Textbox(label="Бренд машины"),
-        gr.inputs.Textbox(label="Модель машины"),
-        gr.inputs.Textbox(label="Тип топлива"),
-        gr.inputs.Textbox(label="Характеристики двигателя")
-    ],
-    outputs="text",
-    title="Предсказание цены автомобиля",
-    description="Введите характеристики автомобиля для предсказания его цены."
-)
-
-iface.launch(share=True)