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app.py

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+
+import streamlit as st
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import torch
+import requests
+from utils.matcher import oversaet_fuzzy
+
+@st.cache_resource
+def load_model():
+    processor = AutoImageProcessor.from_pretrained("timm/food101-vit-base-patch16-224")
+    model = AutoModelForImageClassification.from_pretrained("timm/food101-vit-base-patch16-224")
+    return processor, model
+
+processor, model = load_model()
+
+st.title("Kalorieestimat og Fødevareklassificering")
+uploaded_file = st.file_uploader("Upload et billede", type=["jpg", "jpeg", "png"])
+confidence_threshold = 0.7
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploadet billede", use_column_width=True)
+
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class_idx = logits.argmax(-1).item()
+        confidence = torch.softmax(logits, dim=-1)[0][predicted_class_idx].item()
+
+    label = model.config.id2label[predicted_class_idx]
+    label_dk = oversaet_fuzzy(label)
+
+    st.markdown(f"**Modelgæt:** {label} ({confidence*100:.1f}%)")
+    st.markdown(f"**Oversat (fuzzy):** {label_dk}")
+
+    if confidence < confidence_threshold:
+        manual = st.selectbox("Modellen er usikker – vælg manuelt fødevaretype:", options=["æg", "kartofler", "smør", "broccoli"])
+        st.markdown(f"**Manuelt valg:** {manual}")
+
+    feedback = st.text_input("Har du feedback eller en mere præcis betegnelse?")
+    if feedback:
+        st.success("Tak for din feedback!")
+
+    st.subheader("Eksempel på fødevareanalyse:")
+    st.markdown("- 100 g æg
+- 200 g kartofler
+- 50 g smør
+- 25 g broccoli")

utils/matcher.py

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+
+def oversaet_fuzzy(label):
+    oversaettelser = {
+        "mashed_potato": "kartoffelmos",
+        "omelette": "æg",
+        "broccoli": "broccoli",
+        "butter": "smør",
+        "french_fries": "pommes frites",
+        "pizza": "pizza",
+        "sushi": "sushi"
+    }
+    return oversaettelser.get(label.lower(), f"(ukendt: {label})")