def predict_model_data(request):
all_info = request.GET
context = {}
model_id = all_info['model_id']
input_test_data = all_info['input_test_data']
input_test_data = [float(x) for x in input_test_data.split(',')]
model_async_id = MLModel.objects.get(id=model_id).model_async_id
loaded_model = AsyncResult(model_async_id)
model_state = loaded_model.state
if model_state == 'SUCCESS':
loaded_model = loaded_model.result
predicted_result = str(loaded_model.predict([input_test_data])[0])
context['predicted_result'] = predicted_result
return render(request, 'prediction_result_partial.html', context)
elif model_state == 'PENDING':
MLModel.objects.get(id=model_id).delete()
models = MLModel.objects.filter(
user__exact=request.user).select_related('user')
t = loader.get_template('trained_models_partial.html')
context['t'] = t.render({'models': models})
context[
'error'] = 'selected model was expired and removed from database'
return JsonResponse(context, status=400)
else:
MLModel.objects.get(id=model_id).delete()
models = MLModel.objects.filter(
user__exact=request.user).select_related('user')
t = loader.get_template('trained_models_partial.html')
context['t'] = t.render({'models': models})
return JsonResponse(context, status=400)
