app.py

import streamlit as st
from PIL import Image


from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("Amite5h/convnext-tiny-finetuned-eurosat")

model = AutoModelForImageClassification.from_pretrained("Amite5h/convnext-tiny-finetuned-eurosat")



#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)
    # Convert grayscale image to RGB format
    if image.mode != "RGB":
        image = image.convert("RGB")
    image_tensor = extractor(images=image, return_tensors="pt")["pixel_values"]
    predictions = model(image)

    col2.header("Probabilities")
    for p in predictions:
        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")