tresh flooder leakan yang katanya no leak leak

LINTAR BROWSER
LEAK BY @jo1el trash browser dont buy it
flood source on @jo1el

Don't buy from @Lintar21 later he will regret it like my friend

```
import tensorflow as tf
from tensorflow.keras import layers, models
import cv2
import os
import numpy as np
from sklearn.preprocessing import LabelEncoder

def load_data(directory):
images = []
labels = []

images, labels = load_data("dataset")

encoder = LabelEncoder()
labels_encoded = encoder.fit_transform(labels)
labels_encoded = tf.keras.utils.to_categorical(labels_encoded)

model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 1)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(len(set(labels)), activation='softmax')
])

model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])

model.fit(images, labels_encoded, epochs=10)

model.save("text_detection_model.jawa")
```

folder for your dataset image
/dataset/

tensorflow training for detection text on image
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CONNECT (v1.2) x Ratelimit Detect
@jo1el
@mscjs

I'm looking for 1 person who is good at c++ to make this team team will make a c++ browser method pm @mscjs