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app.py

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+import streamlit as st
+from PIL import Image
+import pandas as pd
+from transformers import pipeline
+
+st.set_page_config(page_title="WebKalorier")
+
+st.title("🍽️ WebKalorier – Kalorieestimering via Billede")
+
+@st.cache_resource(show_spinner=False)
+def get_classifier():
+    return pipeline("image-classification", model="eslamxm/vit-base-food101")
+
+classifier = get_classifier()
+
+# Load calorie data
+df = pd.read_csv("kaloriedata.csv")
+food_list = df["navn"].tolist()
+
+uploaded_file = st.file_uploader("Upload et billede af din mad", type=["jpg","jpeg","png"])
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Analyserer...", use_column_width=True)
+    with st.spinner("Klassificerer billede..."):
+        results = classifier(image, top_k=3)
+    # Display top result
+    top = results[0]
+    label = top["label"]
+    score = top["score"]
+    st.markdown(f"**Modelgæt:** {label} ({score*100:.1f}% sikkerhed)")
+    if score < 0.7:
+        chosen = st.selectbox("Modellen er usikker – vælg fødevare manuelt:", food_list)
+    else:
+        # Fuzzy matching could be added via utils/matcher.py
+        chosen = label.replace('_', ' ').lower()
+    gram = st.number_input(f"Hvor mange gram {chosen}?", min_value=1, max_value=2000, value=100)
+    kcal_per_100g = float(df.loc[df['navn']==chosen, 'kcal_pr_100g'].squeeze()) if chosen in df['navn'].values else 0
+    kcal = gram * kcal_per_100g / 100
+    st.markdown("### Analyse af måltid")
+    st.write(f"- {gram} g **{chosen}** = {kcal:.1f} kcal")
+    feedback = st.text_input("Har du feedback eller rettelse?")
+    if st.button("Send feedback"):
+        st.success("Tak for din feedback!")

kaloriedata.csv

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+navn,kcal_pr_100g
+æg,155
+kartoffel,77
+smør,717
+broccoli,35
+salat,15
+tomat,18
+ris,130
+pasta,131
+kylling,239
+æble,52

requirements.txt

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+streamlit
+transformers
+torch
+pillow
+pandas