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import streamlit as st
from PIL import Image
from transformers import pipeline
# Load the pre-trained model
classifier = pipeline("image-classification", model="https://teachablemachine.withgoogle.com/models/lcNO3nb0s/")
st.title("Korean Jelly Identifier")
uploaded_file = st.file_uploader("Choose an image...", type="jpg")
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image.', use_column_width=True)
st.write("")
st.write("Classifying...")
# Classify the image
results = classifier(image)
jelly_type = results[0]['label']
sugar_level = get_sugar_level(jelly_type)
hazard = get_hazard_level(sugar_level)
st.write(f'Jelly Type: {jelly_type}')
st.write(f'Sugar Level: {sugar_level}')
st.write(f'Hazard: {hazard}')
def get_sugar_level(jelly_type):
# Dummy data for demonstration purposes
sugar_data = {
'jellyA': 10,
'jellyB': 20,
'jellyC': 30
}
return sugar_data.get(jelly_type, 0)
def get_hazard_level(sugar_level):
if sugar_level > 25:
return 'Red (High Hazard)'
elif sugar_level > 15:
return 'Yellow (Moderate Hazard)'
else:
return 'Green (Low Hazard)'
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