app.py

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("Leaf disease?")

file_name = st.file_uploader("Upload a leaf 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("Confidence Score")
    for p in predictions:
        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")