Update app.py

app.py

@@ -1,20 +1,15 @@
-import streamlit as st
+import gradio as gr
 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)
+def predict(image):
     predictions = pipeline(image)
-
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+    return {p["label"]: p["score"] for p in predictions}
+
+gr.Interface(
+    predict,
+    inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
+    outputs=gr.outputs.Label(num_top_classes=2),
+    title="Hot Dog? Or Not?",
+).launch()