load_keras_django_naive.py

load_keras_django_naive.py

import json
import os

import numpy as np
import pandas as pd
import tensorflow as tf
from keras import backend as K
from keras.models import load_model

from rest_framework.decorators import api_view
from rest_framework.response import Response

@api_view(['POST'])
def score_segment(request):
    graph, model = _load_model_from_path('path_to_keras_model')
    df_segment = pd.DataFrame(request.data)
    try:
        predictions = _score_segment_by_model(df_segment, graph, model)
    except Exception as error:
        return Response({'message':str(error)}, status=status.HTTP_400_BAD_REQUEST)
    return Response({
        'predictions': predictions
    })

def _load_model_from_path(path):
    graph = tf.get_default_graph()
    model = load_model(path)  # keras function
    return graph, model
    
def _score_segment_by_model(df_segment, graph, model):
    X = preprocess_df_segment()  # This is the preprocessing function, depending on your business need. I don't provide it here
    try:
        with graph.as_default():
            predictions = model.predict(X)
            return predictions
    except Exception as err:
        raise(err)
    return None