Update app.py

app.py

@@ -1,10 +1,10 @@
-import time
 import streamlit as st
 import numpy as np
 from PIL import Image
-import urllib.request
 import io
-from utils import *
+import urllib.request
+import tensorflow as tf
+import matplotlib.pyplot as plt
 
 # Initialize labels and model
 labels = gen_labels()
@@ -26,36 +26,36 @@ st.markdown('''
 opt = st.selectbox("How do you want to upload the image for classification?", 
                    ('Please Select', 'Upload image via link', 'Upload image from device'))
 
-# Image processing based on user selection
-image = None
+# Streamlit UI
+st.title("Image Classifier with Streamlit")
+opt = st.selectbox("How do you want to upload the image?", ('Please Select', 'Upload image via link', 'Upload image from device'))
+
 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}")
+        img = Image.open(io.BytesIO(file.read())).resize((256, 256), Image.LANCZOS)
+        st.image(img, width=300, caption='Uploaded Image')
+        img_array = tf.keras.preprocessing.image.img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        prediction = model.predict(img_array)
+        confidence = np.max(prediction[0]) * 100
+        st.write(f"Predicted Label: {class_names[np.argmax(prediction[0])]}")
+        st.write(f"Confidence: {confidence:.2f}%")
 
 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!")
-
-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
+            img = Image.open(response).resize((256, 256), Image.LANCZOS)
+            st.image(img, width=300, caption='Uploaded Image')
+            img_array = tf.keras.preprocessing.image.img_to_array(img)
+            img_array = np.expand_dims(img_array, axis=0)
+            prediction = model.predict(img_array)
+            confidence = np.max(prediction[0]) * 100
+            st.write(f"Predicted Label: {class_names[np.argmax(prediction[0])]}")
+            st.write(f"Confidence: {confidence:.2f}%")
+        except Exception as e:
+            st.error("Error processing the image. Please try again.")
+
+# Add any other Streamlit components or functionalities as needed.