Update app.py

app.py

@@ -1,7 +1,28 @@
 import streamlit as st
 import numpy as np
 
-# Load your trained model (or you can directly use the model if it's in the same script)
+# Model
+
+import seaborn as sns
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+
+# Load dataset
+df = sns.load_dataset('mpg')
+df.dropna(inplace=True)  # Dropping missing values
+
+# Selecting relevant features for simplicity
+features = df[['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model_year']]
+target = df['mpg']
+
+# Splitting the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
+
+# Create and train the model
+model = LinearRegression()
+model.fit(X_train, y_train)
 
 # Write a function to take user inputs
 def user_input_features():