Send textbox value from ASP.NET webform to Python script and return output to labal in ASP.NET webform

protected void Button1_Click(object sender, EventArgs e)
       {
           float mpg = 15.2f;
           float cylinders=8f;
           float cubicinches=400f;
           float hp=165f;
           float weightlbs=4525f;
           float timeto60=12f;
           float year=1998f;

           CallPython python = new CallPython(@"C:\python38\python.exe");
           python.Run_Script(@"C:\SQL Dataset with Decision tree Prediction.py", mpg, cylinders, cubicinches, hp, weightlbs, timeto60, year);

       }

public class CallPython
   {
       string pythonLocationString;
       public CallPython(string pythonLocation)
       {
           pythonLocationString = pythonLocation;
       }

       public void Run_Script(string scriptPathName, float mpg, float cylinders, float cubicinches, float hp, float weightlbs, float timeto60, float year)
       {
           int ExitCode;
           ProcessStartInfo processStartInfo = new ProcessStartInfo();
           processStartInfo.FileName = pythonLocationString;
           processStartInfo.Arguments = scriptPathName;
           processStartInfo.UseShellExecute = false;
           processStartInfo.RedirectStandardOutput = true;
           processStartInfo.WindowStyle = ProcessWindowStyle.Hidden;
           processStartInfo.CreateNoWindow = true;
           Process updateProcess = Process.Start(processStartInfo);
           updateProcess.WaitForExit();
           ExitCode = updateProcess.ExitCode;
           updateProcess.Close();

           updateProcess.ErrorDataReceived += Process_OutputDataReceived;
           process.OutputDataReceived += Process_OutputDataReceived;

           process.Start();
           process.BeginErrorReadLine();
           process.BeginOutputReadLine();
           process.WaitForExit();
           Console.Read();
       }
   }

# Load libraries
import pandas as pd
from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier
from sklearn.model_selection import train_test_split # Import train_test_split function
from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation
from sqlalchemy import create_engine

mpg = 15.2;
cylinders=8;
cubicinches=400;
hp=165;
weightlbs=4525;
timeto60=12;
year=1998;

SERVER = 'DESKTOP-75BEPIE\\NEO'
DATABASE = 'FFAS'
DRIVER = 'SQL Server Native Client 11.0'
USERNAME = 'sa'
PASSWORD = 'sa'
DATABASE_CONNECTION = f'mssql://{USERNAME}:{PASSWORD}@{SERVER}/{DATABASE}?driver={DRIVER}'

engine = create_engine(DATABASE_CONNECTION)
connection = engine.connect()

data = pd.read_sql_query("select * from Dataset", connection)
data.head()

#split dataset in features and target variable
feature_cols = ['mpg', 'cylinders', 'cubicinches', 'hp','weightlbs','timeto60','year']
X = data[feature_cols] # Features
y = data.Decision # Target variable

ft = pd.get_dummies(data[ ['mpg', 'cylinders', 'cubicinches', 'hp', 'weightlbs', 'timeto60', 'year'] ])
ft

# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test

# Create Decision Tree classifer object
clf = DecisionTreeClassifier()

# Train Decision Tree Classifer
clf = clf.fit(X_train,y_train)

#Predict the response for test dataset
y_pred = clf.predict(X_test)
y_pred

# Model Accuracy, how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

# Using random forest to make sure my model doesn't overfit

from sklearn.ensemble import RandomForestClassifier

clf = RandomForestClassifier(n_estimators = 20) #n_esitmators value can be changed according to need
clf = clf.fit(ft,data['Decision'])

#input parameters to Predict
clf.predict([[15.0,8,400,165,4525,12,1998]]))