| | import streamlit as st |
| | from transformers import pipeline |
| | from PIL import Image |
| | |
| | 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 = pipeline(image) |
| |
|
| | col2.header("Probabilities") |
| | for p in predictions: |
| | col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%") |
| |
|