diff --git a/model_core.py b/model_core.py
new file mode 100644
index 0000000..acf6e16
--- /dev/null
+++ b/model_core.py
@@ -0,0 +1,66 @@
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, MaxPooling2D, Flatten, Dense, Dropout
+from tensorflow.keras.optimizers import Adam
+import numpy as np
+from PIL import Image
+
+
+class LeNet5Custom:
+    def __init__(self, dropout_rate):
+        self.dropout_rate = dropout_rate
+        self.model = self._create_model()
+
+
+    def _create_model(self):
+        model = Sequential([
+            Input(shape=(28, 28, 1)),  # 明确指定输入形状
+            Conv2D(32, (3, 3), activation='relu'),
+            BatchNormalization(),
+            MaxPooling2D((2, 2)),
+
+            Conv2D(64, (3, 3), activation='relu'),
+            BatchNormalization(),
+            MaxPooling2D((2, 2)),
+
+            Flatten(),
+
+            Dense(128, activation='relu'),
+            BatchNormalization(),
+            Dropout(self.dropout_rate),
+
+            Dense(10, activation='softmax')
+        ])
+        return model
+
+
+    def compile_model(self):
+        self.model.compile(optimizer=Adam(),
+                           loss='categorical_crossentropy',
+                           metrics=['accuracy'])
+        return self.model
+
+
+    def load_model(self, model_path):
+        self.model = tf.keras.models.load_model(model_path)
+        for layer in self.model.layers:
+            if isinstance(layer, Dropout):
+                layer.rate = self.dropout_rate
+        return self.model
+
+
+    def preprocess_image(self, image_path):
+        img = Image.open(image_path).convert('L')
+        img = img.resize((28, 28))
+        img_array = np.array(img)
+        img_array[img_array != 255] = 0  # 将像素值非255的全部设为0
+        img_array = img_array.astype("float32") / 255
+        img_array = np.expand_dims(img_array, axis=-1)
+        img_array = np.expand_dims(img_array, axis=0)
+        return img_array
+
+
+    def predict(self, image_path):
+        preprocessed_image = self.preprocess_image(image_path)
+        predictions = self.model.predict(preprocessed_image)
+        return predictions