Re-add extract_from_price_tags for history

open_prices/proofs/ml.py

@@ -158,6 +158,10 @@ class Label(typing.TypedDict):
     product_name: str
 
 
+class Labels(typing.TypedDict):
+    labels: list[Label]
+
+
 def extract_from_price_tag(image: Image.Image) -> Label:
     """Extract price tag information from an image.
 
@@ -192,6 +196,48 @@ def extract_from_price_tag(image: Image.Image) -> Label:
     return json.loads(response.text)
 
 
+def extract_from_price_tags(images: Image.Image) -> Labels:
+    """
+    Extract price tag information from a list of images.
+
+    Warning:
+    Gemini sometimes skips some images when prediction price tag labels,
+    leading to mismatch between price tag and predictions.
+    Use extract_from_price_tag instead.
+    """
+
+    # Gemini model max payload size is 20MB
+    # To prevent the payload from being too large, we resize the images before
+    # upload
+    resized_images = []
+    max_size = 1024
+    for image in images:
+        if image.width > max_size or image.height > max_size:
+            resized_image = image.copy()
+            resized_image.thumbnail((max_size, max_size))
+            resized_images.append(resized_image)
+        else:
+            resized_images.append(image)
+
+    response = model.generate_content(
+        [
+            (
+                f"Here are {len(resized_images)} pictures containing a label. "
+                "For each picture of a label, please extract all the following attributes: "
+                "the product category matching product name, the origin category matching country of origin, the price, "
+                "is the product organic, the unit (per KILOGRAM or per UNIT) and the barcode (valid EAN-13 usually). "
+                f"I expect a list of {len(resized_images)} labels in your reply, no more, no less. "
+                "If you cannot decode an attribute, set it to an empty string"
+            )
+        ]
+        + resized_images,
+        generation_config=genai.GenerationConfig(
+            response_mime_type="application/json", response_schema=Labels
+        ),
+    )
+    return json.loads(response.text)
+
+
 def predict_proof_type(
     image: Image.Image,
     model_name: str = PROOF_CLASSIFICATION_MODEL_NAME,