streamlit.errors.StreamlitAPIException: `seek()` is not a valid Streamlit command - python-3.x

Question

I am trying to pass the image to my function loadImage:
def loadImage(image):
np_image = Image.open(image)
np_image = np.array(np_image).astype('float32') / 255
np_image = transform.resize(np_image, (224, 224, 3))
# np_image = np.expand_dims(np_image, axis=0)
return np_image
When I try to pass the Image read from the Streamlit Image then it gives me the following error:
raise StreamlitAPIException(message)
streamlit.errors.StreamlitAPIException: `seek()` is not a valid Streamlit command.
My Main Function command:
list = predictions(model=model, breed_list=breed_list, image=st.image(img))
Prediction Function:
def predictions(model, breed_list, image):
probabilities = model.predict(helper.loadImage(image))
return probabilities
Loading image
img_file = st.sidebar.file_uploader(label='Upload a file', type=['png', 'jpg'])
if img_file:
img = Image.open(img_file)
Full Error

Answer 1

Image.open() is expecting a filename, pathlib.Path, or IO-like object, but you're passing it the output of st.image(img), which is going to be a Streamlit DeltaGenerator object (essentially a Streamlit element). As the output of st.image(img) is not a filename (i.e. string) and is not a pathlib.Path object, Image.open() is assuming it to be an IO-like object, hence why it's trying to call the .seek() method (which DeltaGenerator objects don't provide, which then caused the error).
Judging by your code, I assume img represents the filename of some image you want to display. If this is this case, you might want to change your code so as to load the image into a numpy array first, then render the loaded image in Streamlit, then make your predictions from it:
def loadImage(image):
np_image = Image.open(image)
np_image = np.array(np_image).astype('float32') / 255
np_image = transform.resize(np_image, (224, 224, 3))
# np_image = np.expand_dims(np_image, axis=0)
return np_image
def predictions(model, breed_list, image):
probabilities = model.predict(loadImage(image))
return probabilities
loaded_img = loadImage(img)
st.image(img)
list = predictions(model=model, breed_list=breed_list, image=loaded_img)
Additionally, and as an aside: to reduce confusion, I would strongly recommend renaming list in list = predictions(...) to something that doesn't conflict with the Python built-in list, perhaps to something like predicted.