Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+
+extractor = AutoFeatureExtractor.from_pretrained("Amite5h/convnext-tiny-finetuned-eurosat")
+
+model = AutoModelForImageClassification.from_pretrained("Amite5h/convnext-tiny-finetuned-eurosat")
+
+
+
+#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 = model(image)
+
+    col2.header("Probabilities")
+    for p in predictions:
+        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")