classify.py

'''
NAME : OMONIYI TEMIDAYO ANDREW
EMAIL: omoniyiandrewai@gmail.com, kiddojazz@gmail.com
NO:    +2348166220117
TOPIC : MALARIA CLASSIFICATION USING AI
TEAM : TEAM WAKANDA
'''

import tensorflow as tf
import numpy as np
import io
#import cv2
from PIL import Image
import base64
from tensorflow.keras import backend as k
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import load_model # used for loading model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing.image import img_to_array, load_img
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array



#Load the model
print('Loading...')
model = load_model('malaria_model_summit.h5')
print("Model Loaded***")

def converter(value):
    if value == 0:
        return "infected"
    else:
        return "uninfected"

def predict(image1): 
    image = load_img(image1, target_size=(224, 224))
    # convert the image pixels to a numpy array
    image = img_to_array(image)
    # reshape data for the model
    image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
    # prepare the image for the VGG model
    #image = preprocess_input(image)
    # predict the probability across all output classes
    yhat = model.predict(image)
    # convert the probabilities to class labels

    print('Input', image)
    print('Output_binary', yhat*100,'%');
    print('Output', converter(np.argmax(yhat)));

          
    return converter(np.argmax(yhat))