app.py

import streamlit as st
import numpy as np
from skimage.io import imread
from skimage.transform import resize
import pickle 
from PIL import Image
st.set_option('deprecation.showfileUploaderEncoding', False)
st.title('Image Classifier using Machine Learning')
st.text('Upload the Image from the listed category.\n[Kerang Bulu, Kerang Darah]')

model = pickle.load(open('img_model.p','rb'))

uploaded_file = st.file_uploader("Choose an image...", type="jpg")
if uploaded_file is not None:
  img = Image.open(uploaded_file)
  st.image(img,caption='Uploaded Image')

  if st.button('PREDICT'):
    Categories = ['kerang bulu', 'kerang darah']    
    st.write('Result...')
    flat_data=[]
    img = np.array(img)
    img_resized = resize(img,(150,150,3))
    flat_data.append(img_resized.flatten())
    flat_data = np.array(flat_data)
    y_out = model.predict(flat_data)
    y_out = Categories[y_out[0]]
    st.title(f' PREDICTED OUTPUT: {y_out.upper()}')
    q = model.predict_proba(flat_data)
    for index, item in enumerate(Categories):
      st.write(f'{item} : {q[0][index]*100}%')

st.text("")
st.text('')