Create app.py

app.py

@@ -0,0 +1,48 @@
+# app.py
+from utils import load_model, predict
+import torch
+import json
+import requests
+import io
+from PIL import Image
+import streamlit as st
+
+st.title('Dog Breed Classification')
+
+# set header
+st.header('Please upload a image')
+
+# upload file
+file = st.file_uploader('', type=['jpeg', 'jpg', 'png'])
+url = st.text_input("Or enter image URL:")
+st.button("Load Image")
+
+# load classifier
+model = load_model('model_scripted.pt')
+
+if file is not None:
+    image = Image.open(file).convert('RGB')
+    st.image(image, use_column_width=True)
+
+    # classify image
+    predicted, probability = predict(model, image)
+
+    # write classification
+    st.write("## {}".format(predicted))
+    st.write("### Probability: {}%".format(int(probability * 100)))
+
+if url != '':
+    try:
+        response = requests.get(url)
+        image = Image.open(io.BytesIO(response.content)).convert('RGB')
+        st.image(image, caption="Image from URL", use_column_width=True)
+
+        # classify image
+        predicted, probability = predict(model, image)
+
+        # write classification
+        st.write("## {}".format(predicted))
+        st.write("### score: {}%".format(int(probability * 100)))
+
+    except Exception as e:
+        st.error(f"Error loading image from URL: {e}")