Build Desktop-Style Apps
In Your Browser

import pandas as pd
import matplotlib.pyplot as plt

def Form_Load():
    # Load sales data
    df = pd.read_csv("sales.csv")
    DataGrid1.DataFrame = df
    
    # Create chart
    Chart1.plot(df["Month"], df["Revenue"])
    Chart1.title = "Monthly Revenue"
    
def btnFilter_Click():
    filtered = df[df["Region"] == ComboBox1.Text]
    DataGrid1.DataFrame = filtered

from PIL import Image, ImageFilter

def btnOpen_Click():
    img = Image.open(FileDialog1.FileName)
    Canvas1.draw_image(img, 0, 0)
    
def btnBlur_Click():
    img = current_image.filter(ImageFilter.BLUR)
    Canvas1.draw_image(img, 0, 0)
    
def btnGrayscale_Click():
    img = current_image.convert("L")
    Canvas1.draw_image(img, 0, 0)

Private Sub Form_Load()
    Database1.ConnectionString = "sqlite:inventory.db"
    Database1.Execute "SELECT * FROM products"
    DataGrid1.DataSource = Database1
End Sub

Private Sub btnAddStock_Click()
    Dim qty As Integer
    qty = Val(txtQuantity.Text)
    Database1.Execute "UPDATE products SET stock = stock + " & qty & _
        " WHERE id = " & DataGrid1.SelectedRow("id")
    RefreshGrid
End Sub

import numpy as np
import pandas as pd

def btnAnalyze_Click():
    df = pd.read_csv(txtFile.Text)
    
    # Calculate statistics
    lblMean.Caption = f"Mean: {df['score'].mean():.2f}"
    lblStd.Caption = f"Std Dev: {df['score'].std():.2f}"
    lblMedian.Caption = f"Median: {df['score'].median()}"
    
    # Create histogram
    Chart1.histogram(df['score'], bins=10)
    Chart1.xlabel = "Score"
    Chart1.ylabel = "Frequency"

Private Sub Form_Load()
    Inet1.URL = "https://api.example.com/customers"
    Inet1.Method = "GET"
    Inet1.Execute
End Sub

Private Sub Inet1_Complete()
    DataGrid1.DataSource = Inet1.ResponseJSON
End Sub

Private Sub btnSearch_Click()
    Dim results
    results = Filter(customers, txtSearch.Text)
    DataGrid1.DataSource = results
End Sub

from sklearn.linear_model import LinearRegression
import numpy as np

model = LinearRegression()

def btnTrain_Click():
    X = np.array(DataGrid1.GetColumn("feature")).reshape(-1, 1)
    y = np.array(DataGrid1.GetColumn("target"))
    model.fit(X, y)
    lblStatus.Caption = "Model trained!"
    
def btnPredict_Click():
    x_new = float(txtInput.Text)
    prediction = model.predict([[x_new]])
    lblResult.Caption = f"Prediction: {prediction[0]:.2f}"