Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from transformers import BeitFeatureExtractor, BeitForImageClassification
+from PIL import Image
+import requests
+
+pipeline = pipeline(task = "image-classification", model = "microsoft/beit-base-patch16-224-pt22k-ft22k")
+
+st.title("Predict the class of an image")
+
+file_name = st.file_uploader("Upload an image here")
+
+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)}%")
+    
+