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feat(proofs): added process_with_gemini endpoint
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Original file line number | Diff line number | Diff line change |
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from typing import List, Dict | ||
import google.generativeai as genai | ||
import typing_extensions as typing | ||
import json | ||
import enum | ||
import os | ||
from django.conf import settings | ||
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genai.configure(api_key=settings.GOOGLE_GEMINI_API_KEY) | ||
model = genai.GenerativeModel(model_name="gemini-1.5-flash") | ||
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# TODO: what about orther categories ? | ||
class Products(enum.Enum): | ||
OTHER = "other" | ||
APPLES = "en:apples" | ||
APRICOTS = "en:apricots" | ||
ARTICHOKES = "en:artichokes" | ||
ASPARAGUS = "en:asparagus" | ||
AUBERGINES = "en:aubergines" | ||
AVOCADOS = "en:avocados" | ||
BANANAS = "en:bananas" | ||
BEET = "en:beet" | ||
BERRIES = "en:berries" | ||
BLACKBERRIES = "en:blackberries" | ||
BLUEBERRIES = "en:blueberries" | ||
BOK_CHOY = "en:bok-choy" | ||
BROCCOLI = "en:broccoli" | ||
CABBAGES = "en:cabbages" | ||
CARROTS = "en:carrots" | ||
CAULIFLOWERS = "en:cauliflowers" | ||
CELERY = "en:celery" | ||
CELERY_STALK = "en:celery-stalk" | ||
CEP_MUSHROOMS = "en:cep-mushrooms" | ||
CHANTERELLES = "en:chanterelles" | ||
CHERRIES = "en:cherries" | ||
CHERRY_TOMATOES = "en:cherry-tomatoes" | ||
CHICKPEAS = "en:chickpeas" | ||
CHIVES = "en:chives" | ||
CLEMENTINES = "en:clementines" | ||
COCONUTS = "en:coconuts" | ||
CRANBERRIES = "en:cranberries" | ||
CUCUMBERS = "en:cucumbers" | ||
DATES = "en:dates" | ||
ENDIVES = "en:endives" | ||
FIGS = "en:figs" | ||
GARLIC = "en:garlic" | ||
GINGER = "en:ginger" | ||
GRAPEFRUITS = "en:grapefruits" | ||
GRAPES = "en:grapes" | ||
GREEN_BEANS = "en:green-beans" | ||
KIWIS = "en:kiwis" | ||
KAKIS = "en:kakis" | ||
LEEKS = "en:leeks" | ||
LEMONS = "en:lemons" | ||
LETTUCES = "en:lettuces" | ||
LIMES = "en:limes" | ||
LYCHEES = "en:lychees" | ||
MANDARIN_ORANGES = "en:mandarin-oranges" | ||
MANGOES = "en:mangoes" | ||
MELONS = "en:melons" | ||
MUSHROOMS = "en:mushrooms" | ||
NECTARINES = "en:nectarines" | ||
ONIONS = "en:onions" | ||
ORANGES = "en:oranges" | ||
PAPAYAS = "en:papayas" | ||
PASSION_FRUITS = "en:passion-fruits" | ||
PEACHES = "en:peaches" | ||
PEARS = "en:pears" | ||
PEAS = "en:peas" | ||
PEPPERS = "en:peppers" | ||
PINEAPPLE = "en:pineapple" | ||
PLUMS = "en:plums" | ||
POMEGRANATES = "en:pomegranates" | ||
POMELOS = "en:pomelos" | ||
POTATOES = "en:potatoes" | ||
PUMPKINS = "en:pumpkins" | ||
RADISHES = "en:radishes" | ||
RASPBERRIES = "en:raspberries" | ||
RHUBARBS = "en:rhubarbs" | ||
SCALLIONS = "en:scallions" | ||
SHALLOTS = "en:shallots" | ||
SPINACHS = "en:spinachs" | ||
SPROUTS = "en:sprouts" | ||
STRAWBERRIES = "en:strawberries" | ||
TOMATOES = "en:tomatoes" | ||
TURNIP = "en:turnip" | ||
WATERMELONS = "en:watermelons" | ||
WALNUTS = "en:walnuts" | ||
ZUCCHINI = "en:zucchini" | ||
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# TODO: what about other origins ? | ||
class Origin(enum.Enum): | ||
FRANCE = "en:france" | ||
ITALY = "en:italy" | ||
SPAIN = "en:spain" | ||
POLAND = "en:poland" | ||
CHINA = "en:china" | ||
BELGIUM = "en:belgium" | ||
MOROCCO = "en:morocco" | ||
PERU = "en:peru" | ||
PORTUGAL = "en:portugal" | ||
MEXICO = "en:mexico" | ||
OTHER = "other" | ||
UNKNOWN = "unknown" | ||
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class Unit(enum.Enum): | ||
KILOGRAM = "KILOGRAM" | ||
UNIT = "UNIT" | ||
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class Label(typing.TypedDict): | ||
product: Products | ||
price: float | ||
origin: Origin | ||
unit: Unit | ||
organic: bool | ||
barcode: str | ||
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class Labels(typing.TypedDict): | ||
labels: list[Label] | ||
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def handle_bulk_labels(images): | ||
response = model.generate_content( | ||
["Here are " + str(len(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. I expect a list of " + str(len(images)) + " labels in your reply, no more, no less. If you cannot decode an attribute, set it to an empty string"] + images, | ||
generation_config=genai.GenerationConfig( | ||
response_mime_type="application/json", response_schema=Labels | ||
) | ||
) | ||
vals = json.loads(response.text) | ||
return vals |
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