first commit

main.py

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+import enum
+import os
+from typing import Optional
+
+import requests
+import streamlit as st
+
+
+http_session = requests.Session()
+
+@enum.unique
+class NeuralCategoryClassifierModel(enum.Enum):
+    keras_2_0 = "keras-2.0"
+    keras_sota_3_0 = "keras-sota-3-0"
+    keras_ingredient_ocr_3_0 = "keras-ingredient-ocr-3.0"
+    keras_baseline_3_0 = "keras-baseline-3.0"
+    keras_original_3_0 = "keras-original-3.0"
+    keras_product_name_only_3_0 = "keras-product-name-only-3.0"
+
+
+LOCAL_DB = False
+
+if LOCAL_DB:
+    ROBOTOFF_BASE_URL = "http://localhost:5500/api/v1"
+else:
+    ROBOTOFF_BASE_URL = "https://robotoff.openfoodfacts.org/api/v1"
+
+PREDICTION_URL = ROBOTOFF_BASE_URL + "/predict/category"
+
+
+@st.cache_data()
+def get_predictions(barcode: str, model_name: str, threshold: Optional[float] = None):
+    data = {"barcode": barcode, "predictors": ["neural"], "neural_model_name": model_name}
+    if threshold is not None:
+        data["threshold"] = threshold
+
+    r = requests.post(PREDICTION_URL, json=data)
+    r.raise_for_status()
+    return r.json()["neural"]
+
+def display_predictions(
+    barcode: str,
+    model_names: list[str],
+    threshold: Optional[float] = None,
+):
+    debug_showed = False
+    for model_name in model_names:
+        response = get_predictions(barcode, model_name, threshold)
+        
+        if "debug" in response:
+            if not debug_showed:
+                debug_showed = True
+                st.write(response["debug"])
+            response.pop("debug")
+        st.write(f"** {model_name} **")
+        st.write(response)
+
+
+
+st.sidebar.title("Category Prediction Demo")
+barcode = st.sidebar.text_input(
+    "Product barcode"
+)
+threshold = st.sidebar.number_input("Threshold", format="%f") or None
+model_names = st.multiselect(
+    "Name of the model",
+    [x.name for x in NeuralCategoryClassifierModel],
+    default=NeuralCategoryClassifierModel.keras_sota_3_0.name,
+)
+
+if barcode:
+    barcode = barcode.strip()
+    display_predictions(
+        barcode=barcode,
+        threshold=threshold,
+        model_names=model_names,
+    )

requirements.txt

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+requests
+streamlit