How to classify an image from Azure Storage in Django using a Tensorflow model - django

Question

I am developing a django application where the user chooses a machine learning model from a drop down list and uploads an image for classification. This image was initially saved in the project directory (bad, I know) so that I can use it in the classification.
Now I save these images in Azure Storage, but at the moment I can't find a way to access them without having to save them locally to classify them, so I think I'll have to temporarily save them in the project directory and once I use them in the ml model, then I remove the images.
I would like to deploy this application to Azure web service, so I consider it is a bad idea to save and delete images in the project directory.
You can see app's form here
models.py
image is saved in Azure Storage, the other fields in Azure Database for PostgreSQL.
class UploadedImage(models.Model):
image = models.ImageField(upload_to='%Y/%m/%d/')
uploaded = models.DateTimeField(auto_now_add=False, auto_now=True)
title = models.CharField(max_length=50)
prediction = models.FloatField(null=True, blank=True)
def __str__(self):
return self.title
forms.py
class UploadImageForm(forms.ModelForm):
EXTRA_CHOICES = [
('MOB', 'MobileNetV2'),
('VGG', 'VGG-19'),
('CNN', 'CNN 3BI'),
]
predicted_with = forms.ChoiceField(label="Modelo Predictivo",
choices=EXTRA_CHOICES, required=True,
widget=forms.Select(attrs={'class': 'form-control'})
)
class Meta:
model = UploadedImage
fields = [
'image',
]
widgets = {
'image': forms.FileInput(attrs={'class':'custom-file-input'}),
}
views.py
def make_prediction(image_to_predict, model='MOB'):
tf.keras.backend.reset_uids()
folders = {'VGG': 'vgg', 'MOB': 'mobilenet', 'CNN': 'cnn3', 'MSG': 'mobile_sin_gpu'}
model_as_json = 'upload_images/model/%s/modelo.json' % (folders[model])
weights = 'upload_images/model/%s/modelo.h5' % (folders[model])
json_file = open(model_as_json, 'r')
loaded_json_model = json_file.read()
json_file.close()
model = tf.keras.models.model_from_json(loaded_json_model)
model.load_weights(weights)
image = [image_to_predict]
data = img_preprocessing.create_data_batches(image)
return model.predict(data)
def upload_image_view(request):
if request.method == 'POST':
form = forms.UploadImageForm(request.POST, request.FILES)
if form.is_valid():
m = form.save(commit=False)
try:
pred = make_prediction(m.image.path, form.cleaned_data['predicted_with'])[0][0]
if pred > 0.5:
# Code continue...
if status == 200:
m.prediction = pred
m.title = m.image.path
m.save()
# Code continue...
The above snippet worked when I initially saved the images in the project directory but when I started saving the images in Azure Storage I started getting this error:
This backend doesn't support absolute paths.
So I changed the following line: pred = make_prediction(m.image.name, form.cleaned_data['predicted_with'])[0][0]
However now I have this error: NewRandomAccessFile failed to Create/Open: image-100.png : The system cannot find the file specified. ; No such file or directory [[{{node ReadFile}}]] [[IteratorGetNext]] [Op:__inference_predict_function_845] Function call stack: predict_function
For this reason I think my solution would be to temporarily save the image in the project directory, use it in the model and then delete it, however, I do not think it is ideal.
What approach is appropriate to follow in this case?