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import time
import streamlit as st
import numpy as np
from PIL import Image
import urllib.request
from utils import *
labels = ["cardboard", "glass", "metal", "paper", "plastic", "trash"]
html_temp = '''
<div style="padding-bottom: 20px; padding-top: 20px; padding-left: 5px; padding-right: 5px">
<center><h1>Garbage Segregation</h1></center>
</div>
'''
st.markdown(html_temp, unsafe_allow_html=True)
html_temp = '''
<div>
<h2></h2>
<center><h3>Please upload Waste Image to find its Category</h3></center>
</div>
'''
st.markdown(html_temp, unsafe_allow_html=True)
opt = st.selectbox("How do you want to upload the image for classification?\n", ('Please Select', 'Upload image via link', 'Upload image from device'))
image = None # Initialize image variable
if opt == 'Upload image from device':
file = st.file_uploader('Select', type=['jpg', 'png', 'jpeg'])
if file is not None:
image = Image.open(file).resize((256, 256), Image.LANCZOS)
elif opt == 'Upload image via link':
try:
img = st.text_input('Enter the Image Address')
image = Image.open(urllib.request.urlopen(img)).resize((256, 256), Image.LANCZOS)
except ValueError:
if st.button('Submit'):
show = st.error("Please Enter a valid Image Address!")
time.sleep(4)
show.empty()
try:
if image is not None:
st.image(image, width = 300, caption = 'Uploaded Image')
if st.button('Predict'):
img = preprocess(image)
model = model_arc()
#model.load_weights("classify_model.h5")
prediction = model.predict(img[np.newaxis, ...])
st.info('Hey! The uploaded image has been classified as " {} waste " '.format(labels[np.argmax(prediction[0], axis=-1)]))
except Exception as e:
st.info(e)
pass |