# Kullanım:
# python main.py dosya.png
#
# NOT:
# - İlk çalıştırmada MNIST veri seti indirilecek ve model eğitilecek.
# - Bu yüzden internet bağlantısı gereklidir.
# - Gerekli kütüphaneler:
#   python -m venv venv
#   source venv/bin/activate  # Linux/macOS
#   venv\Scripts\activate     # Windows
#   pip install numpy pillow scikit-learn matplotlib pandas
# Dataset: https://www.tensorflow.org/datasets/catalog/mnist

import sys
from sklearn.datasets import fetch_openml
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
from PIL import Image, ImageEnhance, ImageOps
import matplotlib.pyplot as plt

def predict_handwritten_image(image_path, clf, show=False):
    img = Image.open(image_path).convert('L')
    img = img.resize((28, 28))
    img = ImageOps.autocontrast(img)
    enhancer = ImageEnhance.Contrast(img)
    img = enhancer.enhance(2.0)
    img_array = np.array(img) / 255.0
    
    if show:
        plt.imshow(img_array, cmap='gray')
        plt.title(image_path)
        plt.axis('off')
        plt.show()
    
    img_flatten = img_array.flatten().reshape(1, -1)
    # Convert to DataFrame to ensure feature names match training data
    feature_names = [f'pixel{i+1}' for i in range(784)]
    img_df = pd.DataFrame(img_flatten, columns=feature_names)
    prediction = clf.predict(img_df)
    return prediction[0]

def main():
    if len(sys.argv) != 2:
        print("Kullanım: python main.py dosya.png")
        sys.exit(1)

    image_path = sys.argv[1]

    print("MNIST verisi indiriliyor (ilk seferde internet gerekiyor)...")
    mnist = fetch_openml('mnist_784', version=1)
    X = mnist.data / 255.0
    y = mnist.target.astype(int)

    # Veriyi eğitim ve test olarak ayır
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    print("Model eğitiliyor...")
    clf = LogisticRegression(max_iter=1000)
    clf.fit(X_train, y_train)

    # Test setindeki doğruluk oranı
    y_pred = clf.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred) * 100
    print(f"Test setindeki doğruluk oranı: %{accuracy:.2f}")

    # Kullanıcının verdiği dosyayı tahmin et
    pred = predict_handwritten_image(image_path, clf, show=True)
    print(f"{image_path} için tahmin edilen rakam: {pred}")

if __name__ == "__main__":
    main()