web.py

import streamlit as st
import numpy as np
from PIL import Image
from tensorflow.keras.models import load_model
import tensorflow as tf

from tempfile import NamedTemporaryFile
from tensorflow.keras.preprocessing import image

#st.set_option('deprecation.showfileUploaderEncoding', False)


@st.cache(allow_output_mutation=True)
def loading_model():
    fp = "./model/model.h5"
    model_loader = load_model(fp)
    return model_loader


cnn = loading_model()
st.write("""
# Cloud Based Web Application Tuberculosis Detection Using CNN App

""")


temp = st.file_uploader("Please Provide the Snap Shot of your X-Ray Image")
#temp = temp.decode()

buffer = temp
temp_file = NamedTemporaryFile(delete=False)
if buffer:
    temp_file.write(buffer.getvalue())
    st.write(image.load_img(temp_file.name))


if buffer is None:
    st.write("This model is built for public use ")

else:

    img = image.load_img(temp_file.name, target_size=(
        500, 500), color_mode='grayscale')

    # Preprocessing the image
    pp_img = image.img_to_array(img)
    pp_img = pp_img/255
    pp_img = np.expand_dims(pp_img, axis=0)

    # predict
    preds = cnn.predict(pp_img)
    if preds >= 0.5:
        out = ('I am {:.2%} percent confirmed that this is a Tuberculosis case You may Need to consult a Doctor'.format(
            preds[0][0]))

    else:
        out = ('I am {:.2%} percent confirmed that this is a Normal case'.format(
            1-preds[0][0]))

    st.success(out)

    image = Image.open(temp)
    st.image(image, use_column_width=True)