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app.py

@@ -1,41 +1,51 @@
 
 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
+from PIL import Image
+import torch
+from torchvision import transforms
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import io
 
 @st.cache_resource
-def load_model_cached():
-    return load_model()
+def load_model():
+    processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224")
+    model = AutoModelForImageClassification.from_pretrained("microsoft/beit-base-patch16-224")
+    return processor, model
 
-st.title("Kalorieestimering fra billede")
+processor, model = load_model()
+
+st.title("Kalorieestimering med AI")
+
+uploaded_file = st.file_uploader("Upload et billede af din mad", type=["jpg", "jpeg", "png"])
 
-uploaded_file = st.file_uploader("Upload et billede", type=["jpg", "jpeg", "png"])
 if uploaded_file:
-    image = load_image(uploaded_file)
+    image = Image.open(uploaded_file).convert("RGB")
     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)
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    logits = outputs.logits
+    probs = torch.nn.functional.softmax(logits, dim=1)
+    top_probs, top_labels = torch.topk(probs, k=1)
 
-    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}%)")
+    confidence = top_probs[0].item() * 100
+    label = model.config.id2label[top_labels[0].item()]
 
-    gram = st.number_input(f"Hvor mange gram {prediction}?", min_value=1, max_value=1000, value=100)
+    st.markdown(f"### Identificeret: **{label}** ({confidence:.1f}% sikkerhed)")
 
-    # Analyse
-    st.markdown("### Analyse af måltid:")
-    st.markdown(f"- {gram} g {prediction}")
+    if confidence < 70:
+        st.warning("Usikker klassificering – vælg manuelt")
+        manual = st.selectbox("Vælg fødevare manuelt", sorted(model.config.id2label.values()))
+        label = manual
 
-    # Feedback
-    st.markdown("### Giv feedback")
-    feedback = st.radio("Er gættet korrekt?", ["Ja", "Nej"])
-    kommentar = st.text_input("Evt. kommentar")
+    st.markdown("### Antaget portionsstørrelse og estimeret energiindhold")
+    st.markdown(f"- 1 portion **{label}** (ca. 200g)")
+    st.markdown("- Estimeret energi: **~300 kcal** *(eksempelværdi)*")
 
-    if st.button("Send feedback"):
-        st.success("Tak for din feedback!")
+    feedback = st.radio("Er forslaget korrekt?", ["Ja", "Nej", "Ved ikke"])
+    if feedback == "Nej":
+        korrekt_label = st.text_input("Hvad forestiller billedet egentlig?")
+        if korrekt_label:
+            st.success(f"Tak for dit input: *{korrekt_label}* gemt.")

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+torch
+torchvision
+transformers
+pillow