Create new file

app.py

@@ -0,0 +1,70 @@
+from tensorflow import keras
+from keras.models import load_model
+
+# importing the preprocessing steps for the model architecture which i used for transfer learning
+from keras.applications.xception import preprocess_input
+
+import numpy as np
+from keras.preprocessing.image import load_img, img_to_array
+import streamlit as st
+import cv2
+
+
+# import tensorflow as tf
+# print(tf.__version__)
+# print(np.__version__)
+# print(st.__version__)
+# print(cv2.__version__)
+
+
+st.write('# Cat and Dog Classifier')
+st.markdown(
+    '''
+        This app uses transfer learning on  the Xception model to predict images of cats and dogs.
+        It achieved an accuracy of approx. 99 percent on the validation set.
+        
+        > ### Enter an image of either a cat or a dog for the model to predict.
+    '''
+)
+
+# image_path = 'sample_images/hang-niu-Tn8DLxwuDMA-unsplash.jpg'
+
+model = load_model('cat_and_dog_classifier.h5')
+
+
+def test_image(object_image):
+    # Convert the file to an opencv image.
+    file_bytes = np.asarray(bytearray(object_image.read()), dtype=np.uint8)
+    opencv_image = cv2.imdecode(file_bytes, 1)
+    opencv_image = cv2.resize(opencv_image, (200, 200))
+    opencv_image.shape = (1, 200, 200, 3)
+    opencv_image = preprocess_input(opencv_image)
+    predictions = model.predict(opencv_image)
+
+    if predictions[0, 0] >= 0.5:
+        result = 'DOG'
+        confidence = predictions[0, 0] * 100
+    else:
+        result = 'CAT'
+        confidence = 100 - (predictions[0, 0] * 100)
+
+    return result, round(confidence, 2)
+    # it returns the predicted label and the precision i.e the confidence score
+
+
+object_image = st.file_uploader("Upload an image...", type=[
+                                'png', 'jpg', 'webp', 'jpeg'])
+submit = st.button('Predict')
+
+if submit:
+    if object_image is not None:
+        output = test_image(object_image)
+
+        # Displaying the image
+        st.image(object_image, channels="BGR")
+        st.markdown(f"""## This is an image of a: {output[0]} """)
+        st.write(f'# Confidence: ${ output[1]}$ %')
+
+
+# print(f'The image was predicted as a {test_image(image_path)}')
+