app.py

import streamlit as st
from transformers import pipeline
from PIL import Image

# Load a food classification pipeline
food_pipeline = pipeline(task="image-classification", model="microsoft/resnet-50")

st.title("Food Recognition Agent 🍕")

# Upload the image
file_name = st.file_uploader("Upload a food image")

if file_name is not None:
    col1, col2 = st.columns(2)

    # Display the uploaded image
    image = Image.open(file_name)
    col1.image(image, use_container_width=True, caption="Uploaded Image")
    
    # Make predictions
    predictions = food_pipeline(image)

    # Display probabilities
    col2.header("Food Predictions 🍽️")
    for p in predictions:
        col2.subheader(f"{p['label']}: {round(p['score'] * 100, 1)}%")