from sklearn.linear_model import LogisticRegression
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import joblib

# Load the MNIST dataset
mnist = fetch_openml('mnist_784', version=1)

# Normalize the data
X = mnist.data / 255.0
y = mnist.target.astype(int)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

# Train the logistic regression model
model = LogisticRegression(solver='lbfgs', max_iter=1000, multi_class='multinomial')
model.fit(X_train, y_train)

# Evaluate the model
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy * 100:.2f}%')

# Save the model
joblib.dump(model, 'logistic_regression_model.pkl')

import tkinter as tk
from tkinter import *
import numpy as np
from PIL import Image, ImageDraw, ImageOps
import joblib

# Load the trained model (make sure you have a trained model saved as 'logistic_regression_model.pkl')
model = joblib.load('logistic_regression_model.pkl')

class App(tk.Tk):
    def __init__(self):
        super().__init__()
        self.title('Handwritten Digit Recognition')
        self.canvas = tk.Canvas(self, width=200, height=200, bg='white')
        self.canvas.pack()

        self.button_predict = tk.Button(self, text='Predict', command=self.predict)
        self.button_predict.pack()

        self.button_clear = tk.Button(self, text='Clear', command=self.clear)
        self.button_clear.pack()

        self.label_result = tk.Label(self, text='Draw a digit and click Predict')
        self.label_result.pack()

        self.canvas.bind('<B1-Motion>', self.paint)

        self.image = Image.new('L', (200, 200), color=255)
        self.draw = ImageDraw.Draw(self.image)

    def paint(self, event):
        x1, y1 = (event.x - 2), (event.y - 2)  # Adjust the size to make the line thinner
        x2, y2 = (event.x + 2), (event.y + 2)  # Adjust the size to make the line thinner
        self.canvas.create_oval(x1, y1, x2, y2, fill='black', width=2)  # Adjust the width to make the line thinner
        self.draw.ellipse([x1, y1, x2, y2], fill='black')

    def clear(self):
        self.canvas.delete('all')
        self.image = Image.new('L', (200, 200), color=255)
        self.draw = ImageDraw.Draw(self.image)
        self.label_result.config(text='Draw a digit and click Predict')

    def predict(self):
        # Resize and invert the image
        image = self.image.resize((28, 28))
        image = ImageOps.invert(image)
        image = np.array(image)

        # Normalize the image data to [0, 1] range
        image = image.astype('float32') / 255

        # Flatten the image
        image = image.flatten()

        # Predict the digit
        prediction = model.predict([image])
        digit = prediction[0]

        # Display the prediction
        self.label_result.config(text=f'Predicted Digit: {digit}')

# Create and run the application
if __name__ == '__main__':
    app = App()
    app.mainloop()