stub

app.py

@@ -1,5 +1,27 @@
+# import streamlit as st
+
+# text = st.text_area("Prefix", value="DM: You enter the room.")
+# batch = st.number_input("Variants", value=5)
+# st.markdown(f"{text} {batch}")
+
+
 import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+
+st.title("Hot Dog? Or Not?")
+
+file_name = st.file_uploader("Upload a hot dog candidate image")
+
+if file_name is not None:
+  col1, col2 = st.columns(2)
+
+  image = Image.open(file_name)
+  col1.image(image, use_column_width=True)
+  predictions = pipeline(image)
 
-text = st.text_area("Prefix", value="DM: You enter the room.")
-batch = st.number_input("Variants", value=5)
-st.markdown(f"{text} {batch}")
+  col2.header("Probabilities")
+  for p in predictions:
+    col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")