| print("Hello") | |
| # 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)}%") | |