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app.py

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+import streamlit as st
+from transformers import pipeline
+
+vision_classifier = pipeline(task="image-classification")
+
+text = st.text_area('Enter a link to an image:')
+
+if text:
+  result = vision_classifier(images=text)
+  st.text("\n".join([f"Class {d['label']} with score {round(d['score'], 4)}" for d in result]))
+  
+#result = vision_classifier(images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
+#print("\n".join([f"Class {d['label']} with score {round(d['score'], 4)}" for d in result]))
+#st.text("\n".join([f"Class {d['label']} with score {round(d['score'], 4)}" for d in result]))