test

app.py

@@ -1,14 +1,20 @@
 import streamlit as st
+from transformers import pipeline
+from PIL import Image
 
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-def app():
-    st.title("Pawpularity score prediction")
-    st.markdown('<h2 style="color:gray;">This model predicts popularity of pet photo (cat or dog)</h2>', unsafe_allow_html=True)
-    text='<h3 style="color:gray;"> Model based on competition [PetFinder.my - Pawpularity Contest](https://www.kaggle.com/competitions/petfinder-pawpularity-score/overview)</h3>'
-    st.markdown(text, unsafe_allow_html=True)
-    x = st.slider('Select a value')
-    st.write(x, 'squared is', x * x)
+st.title("Hot Dog? Or Not?")
 
+file_name = st.file_uploader("Upload a hot dog candidate image")
 
-if __name__ == "__main__":
-    app()
+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)
+
+    col2.header("Probabilities")
+    for p in predictions:
+        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")