What does "ValueError: Exception encountered when calling Sequential.call()" mean and how can i fix it

Question

I am trying to train an AI on a custom database and i ran into this error

I tried removing and altering the numbers and activations etc. but either i get a syntax error or the same ValueError said in the title. The 2 codes are from different tutorials so i understand if they arent compatible but for the most part the code works except for the last part( starting at model = models.Sequential()

# Imports needed
import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models
from tensorflow.keras.preprocessing.image import ImageDataGenerator


ds_train = tf.keras.preprocessing.image_dataset_from_directory(
    "dataset",
    labels="inferred",
    label_mode="int",  # categorical, binary
    
    color_mode="grayscale",
    batch_size=2,
    image_size=(28,28),  # reshape if not in this size
    shuffle=True,
    seed=123,
    validation_split=0.1,
    subset="training",
)

ds_validation = tf.keras.preprocessing.image_dataset_from_directory(
    "dataset",
    labels="inferred",
    label_mode="int",  
    
    color_mode="grayscale",
    batch_size=2,
    image_size=(28, 28),  # reshape if not in this size
    shuffle=True,
    seed=123,
    validation_split=0.1,
    subset="validation",
)


def augment(x, y):
    image = tf.image.random_brightness(x, max_delta=0.05)
    return image, y


ds_train = ds_train.map(augment)

# Custom Loops
for epochs in range(10):
    for x, y in ds_train:
        # train here
        pass

model = models.Sequential()
model.add(layers.Conv2D(32,(3,3), activation = 'relu', input_shape = (32,32,3)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,(3,3), activation = 'relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,(3,3), activation = 'relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation = 'relu'))
model.add(layers.Dense(10, activation= 'softmax'))

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

model.fit(ds_train, epochs=10, verbose=2)

The full traceback is:

Found 236 files belonging to 1 classes.
Using 213 files for training.
Found 236 files belonging to 1 classes.
Using 23 files for validation.
C:\Users\alboz\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\layers\convolutional\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(
Epoch 1/10
Traceback (most recent call last):
  File "c:\Users\alboz\OneDrive\Desktop\Jugendforscht\medizin\egal.py", line 65, in <module>
    model.fit(ds_train, epochs=10, verbose=2)
  File "C:\Users\alboz\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\utils\traceback_utils.py", line 123, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "C:\Users\alboz\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\layers\input_spec.py", line 227, in assert_input_compatibility
    raise ValueError(
ValueError: Exception encountered when calling Sequential.call().

Input 0 of layer "conv2d" is incompatible with the layer: expected axis -1 of input shape to have value 3, but received input with shape (None, 28, 28, 1)  

Arguments received by Sequential.call():
  • inputs=tf.Tensor(shape=(None, 28, 28, 1), dtype=float32)
  • training=True
  • mask=None