Update app.py

app.py

@@ -1,136 +1,131 @@
-import time
-import streamlit as st
+import cv2
 import numpy as np
+import streamlit as st
 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
+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>
+ <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>
+ <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
-    
+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'))
+          ('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}")
+  file = st.file_uploader('Select', type=['jpg', 'png', 'jpeg'])
+  if file:
+    try:
+      image = cv2.imread(file)
+      image = cv2.resize(image, (256, 256))
+    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!")
+  img_url = st.text_input('Enter the Image Address')
+  if st.button('Submit'):
+    try:
+      response = urllib.request.urlopen(img_url)
+      image = cv2.imread(io.BytesIO(response.read()))
+      image = cv2.resize(image, (256, 256))
+    except ValueError:
+      st.error("Please Enter a valid Image Address!")
 
 elif opt == 'Capture a picture':
-    take_photo()
+  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)]))
+  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
+  st.info(e)
+  pass