--- a
+++ b/train_classifier.py
@@ -0,0 +1,60 @@
+
+
+# train_classifier.py
+
+import os
+import numpy as np
+import cv2
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.utils import to_categorical
+
+IMAGE_SIZE = 128
+DATASET_PATH = "dataset"
+
+def load_data():
+    images = []
+    labels = []
+    class_names = os.listdir(DATASET_PATH)
+    class_map = {name: i for i, name in enumerate(class_names)}
+
+    for class_name in class_names:
+        img_folder = os.path.join(DATASET_PATH, class_name, "images")
+        for img_name in os.listdir(img_folder):
+            img_path = os.path.join(img_folder, img_name)
+            img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
+
+            if img is not None:
+                img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE)) / 255.0
+                images.append(img)
+                labels.append(class_map[class_name])
+
+    X = np.array(images).reshape(-1, IMAGE_SIZE, IMAGE_SIZE, 1)
+    y = to_categorical(np.array(labels))
+    return X, y, class_map
+
+def build_classifier(num_classes):
+    model = Sequential()
+    model.add(Conv2D(32, 3, activation='relu', input_shape=(IMAGE_SIZE, IMAGE_SIZE, 1)))
+    model.add(MaxPooling2D(2))
+    model.add(Conv2D(64, 3, activation='relu'))
+    model.add(MaxPooling2D(2))
+    model.add(Flatten())
+    model.add(Dense(128, activation='relu'))
+    model.add(Dropout(0.3))
+    model.add(Dense(num_classes, activation='softmax'))
+
+    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+    return model
+
+# Load & train
+X, y, class_map = load_data()
+X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)
+
+model = build_classifier(num_classes=len(class_map))
+model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, batch_size=16)
+
+model.save("app/model/classifier_model.h5")
+print("Classifier model saved to app/model/classifier_model.h5")
+print(f" Class Map: {class_map}")