first commit

app.py

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+import io
+import time
+
+import requests
+import streamlit as st
+from openfoodfacts.images import generate_image_url
+from PIL import Image
+
+
+@st.cache_data
+def send_prediction_request(image_url: str, model_name: str, server_base_url: str):
+    return requests.get(
+        f"{server_base_url}/api/v1/images/predict",
+        params={"image_url": image_url, "models": model_name, "output_image": 1},
+    )
+
+
+def get_product(barcode: str):
+    r = requests.get(f"https://world.openfoodfacts.org/api/v2/product/{barcode}")
+
+    if r.status_code == 404:
+        return None
+
+    return r.json()["product"]
+
+
+def run(barcode: str, model_names: list[str], server_base_url: str):
+    product = get_product(barcode)
+    st.markdown(f"[Product page](https://world.openfoodfacts.org/product/{barcode})")
+
+    if not product:
+        st.error(f"Product {barcode} not found")
+        return
+
+    images = product.get("images", [])
+
+    if not images:
+        st.error(f"No images found for product {barcode}")
+        return
+
+    for image_id, _ in images.items():
+        if not image_id.isdigit():
+            continue
+
+        image_url = generate_image_url(barcode, f"{image_id}")
+
+        for model_name in model_names:
+            start = time.monotonic()
+            response = send_prediction_request(image_url, model_name, server_base_url)
+            elapsed = time.monotonic() - start
+
+            if response.headers["Content-Type"] != "image/jpeg":
+                st.error(f"Error: {response.text}")
+                continue
+            image = Image.open(io.BytesIO(response.content))
+            st.write(f"Image {image_id}")
+            st.image(image, caption=f"Model: {model_name} ({elapsed:.2f}s)")
+        st.divider()
+
+
+st.title("Object detection demo")
+st.markdown(
+    "This Streamlit is useful to test object detection models running in production at Open Food Facts."
+)
+default_barcode = st.query_params["barcode"] if "barcode" in st.query_params else ""
+model_names = st.multiselect(
+    "Models",
+    options=[
+        "nutrition-table-yolo",
+        "nutrition-table",
+        "nutriscore",
+        "nutriscore-yolo",
+        "universal-logo-detector",
+    ],
+    help="Select the model(s) to use",
+    default=["nutrition-table-yolo", "nutrition-table"],
+)
+barcode = st.text_input(
+    "barcode", help="Barcode of the product", value=default_barcode
+).strip()
+st.query_params["barcode"] = barcode
+
+# Default server is staging
+server_base_url = "https://robotoff.openfoodfacts.net"
+
+if "env" in st.query_params:
+    if st.query_params["env"] == "prod":
+        server_base_url = "https://robotoff.openfoodfacts.net"
+    elif st.query_params["env"] == "dev":
+        server_base_url = "http://localhost:5000"
+
+
+if barcode:
+    run(barcode, model_names, server_base_url)

requirements.txt

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+requests==2.32.3
+streamlit==1.36.0
+openfoodfacts==0.4.0
+Pillow==10.4.0