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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)}%") |