Update app.py

app.py

@@ -1,20 +1,16 @@
-import streamlit as st
-from transformers import pipeline
+from transformers import ViTImageProcessor, ViTForImageClassification
 from PIL import Image
+import requests
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
 
-st.title("Hot Dog? Or Not?")
+processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
+model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
 
-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)
-
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits
+# model predicts one of the 1000 ImageNet classes
+predicted_class_idx = logits.argmax(-1).item()
+print("Predicted class:", model.config.id2label[predicted_class_idx])