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import streamlit as st
from transformers import pipeline
from PIL import Image
#pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
#pipeline = pipeline(task="image-classification", model="Rajaram1996/FacialEmoRecog")
pipeline = pipeline(task="image-classification", model="Bazaar/cv_apple_leaf_disease_detection")
st.title("Hot Dog? Or Not?")
file_name = st.file_uploader("Upload a facial emote 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)}%") |