import2

def preprocess_image(image):
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    resized_image = cv2.resize(gray_image, (32, 32))
    normalized_image = resized_image / 255.0
    return normalized_image

import tensorflow as tf

def build_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 1)),
        tf.keras.layers.MaxPooling2D((2, 2)),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(10, activation='softmax')
    ])
    return model

def train_model(model, train_data, train_labels, val_data, val_labels):
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    model.fit(train_data, train_labels, epochs=10, validation_data=(val_data, val_labels))

def evaluate_model(model, test_data, test_labels):
    test_loss, test_accuracy = model.evaluate(test_data, test_labels)
    print('Test loss:', test_loss)
    print('Test accuracy:', test_accuracy)

def augment_data(x_train, y_train):
    dat = ImageDataGenerator(
 rotation_range=20,
        width_shift_range=0.2,
 height_shift_range=02,
        shear_range=02,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest    
    datagen.fit(x_train)
    
    augmented_data = []
    augmented_labels = []
    
    for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=len(x_train)):
        augmented_data.append(x_batch)
        augmented_labels.append(y_batch)
        break
    
    return augmented_data, augmented_labels

from keras.applications import VGG16
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D

def build_transfer_model_classes):
    base_model = VGG16(weights='imagenet', include_top=False)
    
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    x = Dense(1024, activation='relu')(x)
    predictions = Dense(num_classes, activation='softmax')(x)
    
    model Model(inputs=base_model.input, outputs=predictions)
    
    for layer in base_model.layers:
        layer.trainable = False
    
    return model

from sklearn.model_selection import GridCV
from sklearn.svm import SVC

def tune_hyperparameters(x_train, y_train):
    param_grid = {'C': [1, 10, 100], 'gamma': [0.1, 0.01 0.001]}
    grid_search = GridSearchCV(SVC(), param_grid, cv=5)
    grid_search.fit(x_train, y_train)
    
    best_params = grid_search.best_params_
    best_model = grid_search.best_estimator_
    
    return best_params, best_model

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    image =_image(data['image'])
    prediction = model.predict(image)
    
    return jsonify({'prediction': prediction})

if __name__ == '__main__':
    app.run()