| 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("IS THIS A MOTHERTUCKING HOTDOG???") | |
| file_name = st.file_uploader("Upload your hotdog 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)}%") |