Add application file

app.py

@@ -0,0 +1,68 @@
+import time
+import streamlit as st
+import numpy as np
+from PIL import Image
+import urllib.request
+from utils import *
+import requests
+
+labels = gen_labels()
+
+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:
+        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=256, caption='Uploaded Image')
+        if st.button('Predict'):
+            img = preprocess(image)
+
+            model = model_arc()
+
+            image_url = "https://drive.google.com/file/d/1jc_gp8qR8t6e8z8WOA_K-SqfurN7Z8vn/view?usp=sharing"
+            r = requests.get(image_url)
+
+            # Save the content of the response to a temporary file
+            with open("temp_weights.h5", "wb") as f:
+                f.write(r.content)
+
+            # Load the weights from the temporary file
+            model.load_weights("temp_weights.h5")
+
+            prediction = model.predict(img)
+            st.info('Hey! The uploaded image has been classified as "{} waste" '.format(labels[np.argmax(prediction)]))
+except Exception as e:
+    st.info(str(e))