Upload 3 files

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_best2.hdf5 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,44 @@
+import streamlit as st
+import numpy as np
+from tensorflow.keras.models import load_model
+from PIL import Image
+
+st.title('Dog Classification')
+
+# import the model
+
+model = load_model('model_best2.hdf5')
+
+# define the preprocessing function
+
+def preprocess_image(image):
+    image = image.resize((240, 240))  # resize the image to the desired dimensions
+    image = image.convert("RGB")  # convert the image to RGB mode if needed
+    image = np.array(image)  # convert the image to a NumPy array
+    image = image / 255.0  # normalize the pixel values to the range of 0 to 1
+    image = np.expand_dims(image, axis=0)  # add an extra dimension for batch size
+    return image
+
+# define the prediction function
+
+def prediction(image):
+    preprocessed_image = preprocess_image(image)
+    classes = model.predict(preprocessed_image)
+    predicted_class_index = np.argmax(classes)
+    class_labels = ['Afghan', 'Bulldog', 'Chow']
+    predicted_class = class_labels[predicted_class_index]
+    return predicted_class
+
+# file uploader
+
+uploaded_file = st.file_uploader("Upload your Dog Picture.")
+
+# result
+
+if st.button('Predict'):
+    if uploaded_file is None:
+        st.write('Please upload your favorite dog to purchase picture first.')
+    else:
+        image = Image.open(uploaded_file)
+        result = prediction(image)
+        st.write('This Dog belongs to the {} class.'.format(result))

model_best2.hdf5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ce0f72cdaa4afeb9c783092a95e34c5a1cd5df7157498b575af29b4b167c09b
+size 86876584

requirements.txt

@@ -0,0 +1,4 @@
+streamlit
+numpy
+tensorflow
+Pillow