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# import all necessary libraries
import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import tensorflow as tf
# load the models
model1 = load_model('Model_From_VGG16.h5')
model2 = load_model('Model_basic_From_Scratch.h5')
# define a function to predict using model 1
def predict_model1(image):
# preprocess the image
img = np.array(image.convert('RGB').resize((64, 64))) / 255.0
img = np.expand_dims(img, axis=0)
# make a prediction using model 1
prediction = model1.predict(img)
return prediction
# define a function to predict using model 2
def predict_model2(image):
# preprocess the image
img = np.array(image.convert('RGB').resize((128, 128))) / 255.0
img = np.expand_dims(img, axis=0)
# make a prediction using model 2
prediction = model2.predict(img)
return prediction
# create the Streamlit app
def app():
st.title('Malaria Parasite Presence Detector Using Not One But Two Models')
st.write('Upload an image and see the predictions of two CNN models!')
# create file uploader
uploaded_file = st.file_uploader("Upload Magnified Blood Samples Only!", type=["jpg", "jpeg", "png"])
# check if file has been uploaded
if uploaded_file is not None:
# load the image
image = Image.open(uploaded_file)
# display the image
st.image(image, caption='Uploaded Image', use_column_width=True)
# make a prediction using model 1
prediction1 = predict_model1(image)
# make a prediction using model 2
prediction2 = predict_model2(image)
# display the predictions
st.subheader('Model 1 Prediction - Transfer Learning_VGG16')
if np.argmax(prediction1) == 0:
st.write("Malaria Parasite Not Present in the Blood sample")
else:
st.write("Malaria Parasite Present in the Blood sample")
st.subheader('Model 2 Prediction - Built from Scratch')
if np.round(prediction2) == 1:
st.write("Malaria Parasite Not Present in the Blood sample")
else:
st.write("Malaria Parasite Present in the Blood sample")
# run the app
if __name__ == '__main__':
app() |