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