test

app.py

@@ -1,15 +1,20 @@
-import gradio as gr
+import streamlit as st
 from transformers import pipeline
+from PIL import Image
 
 pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-def predict(image):
+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)
-    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()
+
+    col2.header("Probabilities")
+    for p in predictions:
+        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")