Update app.py

app.py

@@ -1,12 +1,10 @@
 import numpy as np
 import pandas as pd
 import tensorflow as tf
-from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
+from sklearn.preprocessing import LabelEncoder, MinMaxScaler
 from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import MinMaxScaler
 from keras.models import Sequential
-from keras.layers import LSTM, Dense, Dropout
-from keras.optimizers import Adam
+from keras.layers import Dense, Dropout
 
 data = pd.read_csv('cars_raw.csv')
 
@@ -16,57 +14,54 @@ data['Model'] = le.fit_transform(data['Model'])
 
 data = data[data['Price'] != 'Not Priced']
 
-scaler = MinMaxScaler()
 data["Price"] = data["Price"].str.replace("$", "")
 data["Price"] = data["Price"].str.replace(",", "").astype(float)
 
+scaler = MinMaxScaler()
 data['Price'] = scaler.fit_transform(data['Price'].values.reshape(-1, 1))
 
 data = data.dropna()
-le = LabelEncoder()
+
 for col in data.select_dtypes(include=['category', 'object']).columns:
     data[col] = le.fit_transform(data[col])
 
-scaler = MinMaxScaler()
 for col in data.select_dtypes(include=['number']).columns:
     data[col] = scaler.fit_transform(data[col].values.reshape(-1, 1))
 
+# Удаление ненужных колонок
 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']
-
 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], 1)))
+model.add(Dense(128, activation='relu', input_shape=(X_train.shape[1],)))
 model.add(Dropout(0.3))
-model.add(Dense(64,))
+model.add(Dense(64, activation='relu'))
 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))
 
+# Оценка модели
 test_loss = model.evaluate(X_test, y_test)
 print(f'Test loss (MSE): {test_loss}')
 
 import gradio as gr
 
 def predict_car_price(Year, Make, Model, FuelType, Engine):
-
-    car_data = []
-    car_data.append({
-      "Year": int(Year),
-      "Make": Make,
-      "Model": Model,
-      "FuelType": FuelType,
-      "Engine": Engine
-    })
-
-    car_data_df = pd.DataFrame(car_data)
+    car_data = {
+        "Year": int(Year),
+        "Make": Make,
+        "Model": Model,
+        "FuelType": FuelType,
+        "Engine": Engine
+    }
+
+    car_data_df = pd.DataFrame([car_data])
     result_data = pd.read_csv('cars_raw.csv')
     result_data_df = pd.DataFrame(result_data)
 
@@ -86,17 +81,22 @@ def predict_car_price(Year, Make, Model, FuelType, Engine):
     prediction = model.predict(result_df)
 
     inverted_prediction = scaler.inverse_transform(
-            prediction.reshape(-1, 1)
-        )
+        prediction.reshape(-1, 1)
+    )
     predicted_price = inverted_prediction[0][0]
 
     return predicted_price
 
-demo = gr.Interface(
+iface = gr.Interface(
     fn=predict_car_price,
     inputs=[
-        gr.inputs.Slider(label="Year", min=1990, max=2023, step=1),
-        gr.inputs.Dropdown(label="Make", choices=["Acura", "Alfa Romeo", "Aston Martin", "Audi", "Bentley", "BMW", "Buick", "Cadillac", "Chevrolet", "Chrysler", "Dodge", "Ferrari", "Fiat", "Ford", "Genesis", "GMC", "Honda", "Hyundai", "Infiniti", "Jaguar", "Jeep", "Kia", "Lamborghini", "Land Rover", "Lexus", "Lincoln", "Lotus", "Maserati", "Mazda", "McLaren", "Mercedes-Benz", "Mini", "Mitsubishi", "Nissan", "Oldsmobile", "Peugeot", "Plymouth", "Pontiac", "Porsche", "Ram", "Rolls-Royce", "Saab", "Saturn", "Scion", "smart", "Subaru", "Suzuki", "Tesla", "Toyota", "Volkswagen", "Volvo"]),
-        gr.inputs.Textbox(label="Model"),
-        gr.i])
-iface.launch(share=True)  
+        gr.Slider(label="Year", minimum=1990, maximum=2023, step=1),
+        gr.Dropdown(label="Make", choices=["Acura", "Alfa Romeo", "Aston Martin", "Audi", "Bentley", "BMW", "Buick", "Cadillac", "Chevrolet", "Chrysler", "Dodge", "Ferrari", "Fiat", "Ford", "Genesis", "GMC", "Honda", "Hyundai", "Infiniti", "Jaguar", "Jeep", "Kia", "Lamborghini", "Land Rover", "Lexus", "Lincoln", "Lotus", "Maserati", "Mazda", "McLaren", "Mercedes-Benz", "Mini", "Mitsubishi", "Nissan", "Oldsmobile", "Peugeot", "Plymouth", "Pontiac", "Porsche", "Ram", "Rolls-Royce", "Saab", "Saturn", "Scion", "smart", "Subaru", "Suzuki", "Tesla", "Toyota", "Volkswagen", "Volvo"]),
+        gr.Textbox(label="Model"),
+        gr.Dropdown(label="Fuel Type", choices=["Gasoline", "Diesel", "Electric", "Hybrid"]),
+        gr.Textbox(label="Engine")
+    ],
+    outputs="number"
+)
+
+iface.launch(share=True)