app.py

import time
import streamlit as st
import numpy as np
from PIL import Image
import urllib.request
import io
from utils import *
from IPython.display import display, Javascript, Image
from google.colab.output import eval_js
from base64 import b64decode, b64encode
import cv2
import numpy as np
import html
from google.colab.patches import cv2_imshow
from IPython.display import clear_output
import matplotlib.pyplot as plt


# Initialize labels and model
labels = gen_labels()
model = model_arc()  # Assuming this function initializes and returns a trained model


# Streamlit UI
st.markdown('''
  <div style="padding-bottom: 20px; padding-top: 20px; padding-left: 5px; padding-right: 5px">
  <center><h1>EcoIdentify (Test)</h1></center>
  </div>
''', unsafe_allow_html=True)

st.markdown('''
  <div>
  <center><h3>Please upload Waste Image to find its Category</h3></center>
  </div>
''', unsafe_allow_html=True)

# Function to take a photo using the webcam
def take_photo(filename='photo.jpg', quality=0.8):
    js = Javascript('''
        async function takePhoto(quality) {
            const div = document.createElement('div');
            const capture = document.createElement('button');
            capture.textContent = 'Capture';
            div.appendChild(capture);

            const video = document.createElement('video');
            video.style.display = 'block';
            const stream = await navigator.mediaDevices.getUserMedia({ video: true });

            document.body.appendChild(div);
            div.appendChild(video);
            video.srcObject = stream;
            await video.play();

            // Resize the output to fit the video element.
            google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);

            // Wait for Capture to be clicked.
            await new Promise((resolve) => capture.onclick = resolve);

            const canvas = document.createElement('canvas');
            canvas.width = video.videoWidth;
            canvas.height = video.videoHeight;
            canvas.getContext('2d').drawImage(video, 0, 0);
            stream.getVideoTracks()[0].stop();
            div.remove();
            return canvas.toDataURL('image/jpeg', quality);
        }
    ''')
    display(js)
    data = eval_js('takePhoto({})'.format(quality))
    binary = b64decode(data.split(',')[1])
    with open(filename, 'wb') as f:
        f.write(binary)
    return filename
    

opt = st.selectbox("How do you want to upload the image for classification?",
                    ('Please Select', 'Upload image via link', 'Upload image from device', 'Capture a picture'))

# Image processing based on user selection
image = None
if opt == 'Upload image from device':
    file = st.file_uploader('Select', type=['jpg', 'png', 'jpeg'])
    if file:
        try:
            image = Image.open(io.BytesIO(file.read())).resize((256, 256), Image.LANCZOS)
        except Exception as e:
            st.error(f"Error reading the file: {e}")

elif opt == 'Upload image via link':
    img_url = st.text_input('Enter the Image Address')
    if st.button('Submit'):
        try:
            response = urllib.request.urlopen(img_url)
            image = Image.open(response).resize((256, 256), Image.LANCZOS)
        except ValueError:
            st.error("Please Enter a valid Image Address!")

elif opt == 'Capture a picture':
    take_photo()

try:
    if image is not None:
        st.image(image, width=256, caption='Uploaded Image')
        if st.button('Predict'):
            img = preprocess(image)
            #model = model_arc()
            # model.load_weights("classify_model.h5")

            print("---------------img-array---------------------")
            print(img[np.newaxis, ...])
            prediction = model.predict(img[np.newaxis, ...])

            print("------------summary------------------------")
            print(model.summary())
            print("------------------------------------")
            print(prediction)

            st.info('Hey! The uploaded image has been classified as " {} waste " '.format(labels[np.argmax(prediction[0], axis=-1)]))

            def message(img):
                if img == 'paper' or 'cardboard' or 'metal' or 'glass':
                    return (
                        " therefore your item is recyclable. Please refer to https://www.wm.com/us/en/drop-off-locations to find a drop-off location near you.")
                elif img == 'plastic':
                    return (
                        ' therefore you item may have a chance of being recyclable. Since this model has yet to recognize types of plastics, please refer to https://www.bing.com/ck/a?!&&p=c1474e95017548dfJmltdHM9MTcwMzcyMTYwMCZpZ3VpZD0xNmNjOTFiOS1hMDgwLTY5MmItMzBmNi04MmE1YTE3ODY4NDImaW5zaWQ9NTIyMA&ptn=3&ver=2&hsh=3&fclid=16cc91b9-a080-692b-30f6-82a5a1786842&psq=what+type+of+plastic+can+be+recycled&u=a1aHR0cHM6Ly93d3cucGxhc3RpY3Nmb3JjaGFuZ2Uub3JnL2Jsb2cvd2hpY2gtcGxhc3RpYy1jYW4tYmUtcmVjeWNsZWQ&ntb=1 to check if this item can be recycled.')
                else:
                    return ('Your item is not recyclable. Please discard it safely.')

            st.info(message(labels[np.argmax(prediction[0], axis=-1)]))

except Exception as e:
    st.info(e)
    pass