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import requests |
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import streamlit as st |
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from annotated_text import annotated_text |
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from openfoodfacts.images import generate_image_url, generate_json_ocr_url |
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@st.cache_data |
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def send_prediction_request(ocr_url: str, model_version: str): |
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return requests.get( |
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"https://robotoff.openfoodfacts.net/api/v1/predict/ingredient_list", |
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params={"ocr_url": ocr_url, "model_version": model_version}, |
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).json() |
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def get_product(barcode: str): |
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r = requests.get(f"https://world.openfoodfacts.org/api/v2/product/{barcode}") |
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if r.status_code == 404: |
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return None |
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return r.json()["product"] |
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def display_ner_tags(text: str, entities: list[dict]): |
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spans = [] |
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previous_idx = 0 |
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for entity in entities: |
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score = entity["score"] |
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lang = entity["lang"]["lang"] |
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start_idx = entity["start"] |
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end_idx = entity["end"] |
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spans.append(text[previous_idx:start_idx]) |
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spans.append((text[start_idx:end_idx], f"score={score:.3f} | lang={lang}")) |
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previous_idx = end_idx |
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spans.append(text[previous_idx:]) |
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annotated_text(spans) |
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def run( |
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barcode: str, |
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model_version: str, |
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min_threshold: float = 0.5, |
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): |
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product = get_product(barcode) |
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st.markdown(f"[Product page](https://world.openfoodfacts.org/product/{barcode})") |
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if not product: |
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st.error(f"Product {barcode} not found") |
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return |
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images = product.get("images", []) |
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if not images: |
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st.error(f"No images found for product {barcode}") |
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return |
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for image_id, _ in images.items(): |
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if not image_id.isdigit(): |
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continue |
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ocr_url = generate_json_ocr_url(barcode, image_id) |
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prediction = send_prediction_request(ocr_url, model_version) |
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st.divider() |
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image_url = generate_image_url(barcode, image_id) |
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st.markdown(f"[Image {image_id}]({image_url}), [OCR]({ocr_url})") |
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st.image(image_url) |
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if "error" in prediction: |
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st.warning(f"Error: {prediction['description']}") |
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continue |
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entities = prediction["entities"] |
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text = prediction["text"] |
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filtered_entities = [e for e in entities if e["score"] >= min_threshold] |
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display_ner_tags(text, filtered_entities) |
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query_params = st.experimental_get_query_params() |
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default_barcode = query_params["barcode"][0] if "barcode" in query_params else "" |
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st.title("Ingredient extraction demo") |
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st.markdown( |
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"This demo leverages the ingredient entity detection model, " |
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"that takes the OCR text as input and predict ingredient lists." |
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) |
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barcode = st.text_input( |
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"barcode", help="Barcode of the product", value=default_barcode |
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).strip() |
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model_version = "1" |
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st.experimental_set_query_params(barcode=barcode) |
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threshold = st.number_input( |
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"threshold", |
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help="Minimum threshold for entity predictions", |
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min_value=0.0, |
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max_value=1.0, |
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value=0.98, |
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) |
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if barcode: |
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run(barcode, model_version=model_version, min_threshold=threshold) |
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