Update app.py

app.py

@@ -1,7 +1,6 @@
 import streamlit as st
 import tensorflow as tf
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-from tensorflow.keras.preprocessing import image
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
 import numpy as np
 
 # Check TensorFlow version
@@ -29,14 +28,14 @@ if uploaded_file is not None:
     cnn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
 
     # Preprocess the uploaded image
-    img = image.load_img(uploaded_file, target_size=(64, 64))
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array /= 255.0  # Normalize the image data
+    img = load_img(uploaded_file, target_size=(64, 64))
+    img_array = img_to_array(img)
+    img_array = img_array.reshape((1, img_array.shape[0], img_array.shape[1], img_array.shape[2]))
+    img_array = img_array / 255.0  # Normalize pixel values to [0, 1]
 
     # Make prediction
     prediction = cnn.predict(img_array)
     if prediction[0][0] > 0.5:
-        st.write("Prediction: Cat")
-    else:
         st.write("Prediction: Dog")
+    else:
+        st.write("Prediction: Cat")