app.py

import streamlit as st
from PIL import Image
from transformers import ViTForImageClassification
from config import UNTRAINED, labels, TRAINED
from utils import predict


model_untrained = ViTForImageClassification.from_pretrained(
    UNTRAINED,
    num_labels=len(labels),
    id2label={str(i): c for i, c in enumerate(labels)},
    label2id={c: str(i) for i, c in enumerate(labels)},
)

model_trained = ViTForImageClassification.from_pretrained(
    TRAINED,
    num_labels=len(labels),
    id2label={str(i): c for i, c in enumerate(labels)},
    label2id={c: str(i) for i, c in enumerate(labels)},
)

st.title("Detect Hurricane Damage")

col1, col2 = st.columns(2)
with col1:
    st.markdown("## Pre-Trained Model")
    file_name = st.file_uploader("Upload a satellite image")

    if file_name is not None:
        image = Image.open(file_name)
        col1.image(image, use_container_width=True)
        label = predict(model_untrained, image)
        st.write(f"Predicted label: {label}")

with col2:
    st.markdown("## Fine-Tuned Model")
    file_name = st.file_uploader("Upload a satellite image")

    if file_name is not None:
        image = Image.open(file_name)
        col2.image(image, use_container_width=True)
        label = predict(model_trained, image)
        st.write(f"Predicted label: {label}")