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app.py

@@ -1,54 +1,45 @@
 import streamlit as st
 from PIL import Image
-import torch
-from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+from transformers import pipeline
 import pandas as pd
-from utils.matcher import fuzzy_match
 
-@st.cache_resource
-def load_model():
-    extractor = AutoFeatureExtractor.from_pretrained("gabrielganan/efficientnet_b1-food101")
-    model = AutoModelForImageClassification.from_pretrained("gabrielganan/efficientnet_b1-food101")
-    return extractor, model
-
-# Load model and data
-extractor, model = load_model()
+# Load calorie data
 df = pd.read_csv("kaloriedata.csv")
 food_list = df["navn"].tolist()
 
-st.title("🍽️ WebKalorier – AI-baseret fødevareklassificering")
-
-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="Uploadet billede", use_column_width=True)
-
-    with st.spinner("Analyserer billede..."):
-        inputs = extractor(images=image, return_tensors="pt")
-        with torch.no_grad():
-            outputs = model(**inputs)
-        logits = outputs.logits
-        probs = torch.nn.functional.softmax(logits, dim=1)[0]
-        confidence, idx = torch.max(probs, dim=0)
-        label = model.config.id2label[idx.item()]
-        conf_score = confidence.item()
-
-    st.markdown(f"**Modelgæt:** `{label}` med **{conf_score*100:.1f}%** sikkerhed")
+# Load zero-shot pipeline for image classification
+@st.cache_resource
+def get_classifier():
+    return pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32")
 
-    if conf_score < 0.7:
-        selected = st.selectbox("Model usikker – vælg fødevare manuelt:", food_list)
-    else:
-        selected = fuzzy_match(label, food_list)
+classifier = get_classifier()
 
-    gram = st.number_input(f"Angiv mængde af {selected} i gram:", min_value=1, max_value=1000, value=100)
-    kcal_per_100g = float(df.loc[df["navn"] == selected, "kcal_pr_100g"].iloc[0])
-    total_kcal = gram * kcal_per_100g / 100
+st.title("🍽️ WebKalorier – Kalorieestimering via CLIP")
 
+uploaded = st.file_uploader("Upload billede af mad", type=["jpg", "jpeg", "png"])
+if uploaded:
+    image = Image.open(uploaded).convert("RGB")
+    st.image(image, caption="Uploadet billede", use_column_width=True)
+    with st.spinner("Analyserer..."):
+        outputs = classifier(image, candidate_labels=food_list)
+    # outputs: list of dicts with labels and scores
+    best = outputs[0]
+    label = best['labels'][0]
+    score = best['scores'][0]
+    st.markdown(f"**Modelgæt:** {label} ({score:.1%} sikkerhed)")
+    # fallback
+    if score < 0.7:
+        label = st.selectbox("Modellen er usikker – vælg manuelt:", food_list, index=food_list.index(label))
+    # grams input
+    gram = st.number_input(f"Angiv mængde af {label} i gram:", 1, 2000, 100)
+    # lookup calories
+    kcal_per_100g = df.loc[df["navn"] == label, "kcal_pr_100g"].iloc[0]
+    kcal = gram * kcal_per_100g / 100
     st.subheader("🔍 Analyse af måltid")
-    st.write(f"- {gram} g {selected} → {total_kcal:.1f} kcal")
-
-    feedback = st.text_input("Feedback eller rettelse (valgfrit)")
+    st.write(f"- **{gram} g {label}** → **{kcal:.1f} kcal**")
+    # feedback
+    feedback = st.text_input("Feedback eller korrektion (valgfrit)")
     if st.button("Send feedback"):
         with open("feedback_log.csv", "a") as f:
-            f.write(f"{label},{selected},{conf_score:.2f},{feedback}\n")
+            f.write(f"{label},{score:.2f},{feedback}\n")
         st.success("Tak for din feedback!")

kaloriedata.csv

@@ -1,16 +1,11 @@
 navn,kcal_pr_100g
+æg,155
+kartofler,77
+smør,717
+broccoli,35
 salat,15
-tomat,18
 ris,130
 pasta,131
 kylling,239
-æg,155
-smør,717
-kartoffel,77
-broccoli,35
-laks,210
+fisk,206
 oksekød,250
-gulerod,41
-pizza,270
-burger,280
-brød,260

requirements.txt

@@ -1,6 +1,5 @@
 streamlit
-torch
 transformers
-pillow
+torch
+Pillow
 pandas
-rapidfuzz