app.py

import streamlit as st
from PIL import Image,ImageOps
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_hub as hub
import numpy as np
from tensorflow.keras import preprocessing
from tensorflow.keras.models import load_model
from tensorflow.keras.activations import sigmoid
import os
import h5py

st.header('Malaria Cell Detector')

def main():
  file_uploaded = st.file_uploader('Choose the file', type = ['jpg','png','jpeg'])
  if file_uploaded is not None:
    image = Image.open(file_uploaded)
    figure = plt.figure()
    plt.imshow(image)
    plt.axis('off')
    result = predict_class(image)
    st.write(result)
    st.pyplot(figure)

def predict_class(image):
  classifier_model = tf.keras.models.load_model(r'/content/my_model.hdf5')
  shape = ((64,64,3))
  model  = tf.keras.Sequential([hub.KerasLayer(classifier_model, input_shape = shape)])
  test_image = image.resize((64,64))
  test_image = preprocessing.image.img_to_array(test_image)
  test_image = test_image / 255.0
  test_image = np.expand_dims(test_image, axis = 0)
  class_names = ['Parasitized','Uninfected']
  predictions = model.predict(test_image)
  scores = tf.nn.sigmoid(predictions[0])
  scores = scores.numpy()
  image_class = class_names[np.argmax(scores)]
  result = "The image uploaded is: {}".format(image_class)
  return result

if __name__ == "__main__":
  main()