Can you switch the multiple files upload to be switched on (True) and off (False) between different outputs

Dash Python
1

Appreciate some help with the
“multiple files to be uploaded
multiple=True” property in dash

For this, am I able to have it “True” for 1 output/callback and then turn it to “False” for the next output/callback within the same dashboard script?

Because my first callback has commands that require ‘multiple’ since I parse the file and display the image, filename and date.

My next callback takes the image filename and preprocesses the image and runs it through a model and displays an output

It runs into issues because of that multiple property

My code:

model = keras.models.load_model(filename)
no_of_pixels = 255

app = dash.Dash(__name__)

app.layout = html.Div([
    dcc.Upload(
        id='upload-image',
        children=html.Div([
            'Drag and Drop or ',
            html.A('Select Files')
        ]),
        style={
            'width': '100%',
            'height': '60px',
            'lineHeight': '60px',
            'borderWidth': '1px',
            'borderStyle': 'dashed',
            'borderRadius': '5px',
            'textAlign': 'center',
            'margin': '10px'
        },
        multiple=True
    ),
    html.Div(id='output-image-upload'),
    html.Div(id='output-prediction'),
])

#For the image display
def parse_contents(contents, filename, date):
    return html.Div([
        html.H5(filename),
        html.H6(datetime.datetime.fromtimestamp(date)),
        html.Img(src=contents),
        html.Hr(),
        html.Div('Raw Content'),
        html.Pre(contents[0:200] + '...', style={
            'whiteSpace': 'pre-wrap',
            'wordBreak': 'break-all'
        })
    ])

#for the model output
def load_and_preprocess(image):
   image1 = Image.open(image)
   rgb =  Image.new('RGB', image1.size)
   rgb.paste(image1)
   image = rgb
   test_image = image.resize((256,256))
   return test_image

#for the model output
def np_array_normalise(test_image):
   np_image = np.array(test_image)
   np_image = np_image / no_of_pixels
   final_image = np.expand_dims(np_image, 0)
   return final_image

#For the image display
@app.callback(Output('output-image-upload', 'children'),
              Input('upload-image', 'contents'),
              State('upload-image', 'filename'),
              State('upload-image', 'last_modified'),

#For the image display
def update_output(list_of_contents, list_of_names, list_of_dates):
    if list_of_contents is not None:
        children = [
            parse_contents(c, n, d) for c, n, d in
            zip(list_of_contents, list_of_names, list_of_dates)]
        return children

#for the model output
@app.callback(Output('output-prediction', 'children'),
              Input('upload-image', 'filename'))

#for the model output
def prediction(image):
    final_img = load_and_preprocess(image)
    final_img = np_array_normalise(final_img)
    Y = model.predict(final_img)
    return Y

if __name__ == '__main__':
    app.run_server(debug=True)