Add application file

app.py

@@ -0,0 +1,41 @@
+import streamlit as st
+from PIL import Image
+import numpy as np
+import keras
+
+# Load pre-trained model
+model = keras.models.load_model('./image_classification_model.keras')
+image_size = (180, 180)
+
+# Function to make prediction
+def predict(image):
+    image_size = (180, 180)
+    img = keras.utils.load_img(image, target_size=image_size)
+
+    img_array = keras.utils.img_to_array(img)
+    img_array = np.expand_dims(img_array, 0)  # Create batch axis
+
+    predictions = model.predict(img_array)
+    score = float(keras.activations.sigmoid(predictions[0][0]))
+    return score
+
+# Streamlit app
+def main():
+    st.title("Image Classification from Scratch")
+    st.write("Upload an image to predict whether the image contains a cat or a dog.")
+
+    uploaded_image = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+
+    if uploaded_image is not None:
+        image = Image.open(uploaded_image)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+
+        if st.button('Predict'):
+            score = predict(uploaded_image)
+            if (1 - score) > score:
+                st.write('Prediction Result: {:.2f}% Cat'.format(100 * (1 - score)))
+            else:
+                st.write('Prediction Result: {:.2f}% Dog'.format(100 * score))
+
+if __name__ == '__main__':
+    main()