app.py

import streamlit as st
from utils.image_utils import load_image, detect_hand_and_food_area
from utils.model import load_model, classify_food
from utils.matcher import oversæt_fuzzy

@st.cache_resource
def load_model_cached():
    return load_model()

st.title("Kalorieestimering fra billede")

uploaded_file = st.file_uploader("Upload et billede", type=["jpg", "jpeg", "png"])
if uploaded_file:
    image = load_image(uploaded_file)
    st.image(image, caption="Dit billede", use_column_width=True)

    with st.spinner("Analyserer billede..."):
        hand_img, food_area = detect_hand_and_food_area(image)
        processor, model = load_model_cached()
        prediction, confidence = classify_food(food_area, processor, model)

    if confidence < 0.7:
        st.warning("Modelen er ikke sikker – vælg manuelt:")
        prediction = st.selectbox("Vælg fødevare", ["æg", "kartoffel", "smør", "broccoli"])
    else:
        st.success(f"Modelen gættede: {prediction} ({confidence*100:.1f}%)")

    gram = st.number_input(f"Hvor mange gram {prediction}?", min_value=1, max_value=1000, value=100)

    # Analyse
    st.markdown("### Analyse af måltid:")
    st.markdown(f"- {gram} g {prediction}")

    # Feedback
    st.markdown("### Giv feedback")
    feedback = st.radio("Er gættet korrekt?", ["Ja", "Nej"])
    kommentar = st.text_input("Evt. kommentar")

    if st.button("Send feedback"):
        st.success("Tak for din feedback!")