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572a7ae
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Parent(s):
cf36a3b
Create app.py
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app.py
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import streamlit as st
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from transformers import pipeline
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from PIL import Image
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pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
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st.title("Hot Dog? Or Not?")
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file_name = st.file_uploader("Upload a hot dog candidate image")
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if file_name is not None:
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col1, col2 = st.columns(2)
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image = Image.open(file_name)
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col1.image(image, use_column_width=True)
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predictions = pipeline(image)
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col2.header("Probabilities")
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for p in predictions:
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col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
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