Add phase 4: AI cleanup and photo/text import via OpenAI
- recipes/ai.py: Chat Completions with strict JSON schema (Structured Outputs), Pydantic re-validation, plausibility clamping, prompt treats input as data only - "Mit KI aufbereiten" on create/edit form returns an editable draft - Import page: photo/screenshot (vision, downscaled JPEG, not stored) and pasted text; drafts pre-fill the recipe form with a preview - Clear UI messages for timeout, rate limit, quota, auth, model and connection errors; without OPENAI_API_KEY AI features show a notice - Optional per-user and global daily AI limits (default unlimited) - Tests for response parsing, schema strictness, error mapping, views Co-Authored-By: Claude Opus 5.5 <[email protected]>
This commit is contained in:
Kyle Dammann
co-authored by
Claude Opus 5.5
Claude Opus 5.5
parent
9256e94286
commit
1bf8ef8b65
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import json
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import httpx2
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import openai
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import pytest
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from django.urls import reverse
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from recipes import ai
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from recipes.models import Recipe
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from recipes.parsing import parse_ingredients
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from .recipe_helpers import form_data, image_upload
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VALID = {
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"is_recipe": True,
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"title": "Linsen-Dal",
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"description": "Cremig und würzig.",
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"servings": 4,
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"servings_label": "Portionen",
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"prep_minutes": 10,
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"cook_minutes": 25,
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"ingredients": [
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{"group": "", "amount": 200, "amount_max": None, "unit": "g", "name": "rote Linsen", "note": "gewaschen"},
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{"group": "", "amount": 0.5, "amount_max": None, "unit": "TL", "name": "Kurkuma", "note": ""},
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{"group": "Zum Servieren", "amount": 2, "amount_max": 3, "unit": "EL", "name": "Joghurt", "note": ""},
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{"group": "Zum Servieren", "amount": None, "amount_max": None, "unit": "", "name": "Koriander", "note": ""},
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],
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"steps": ["Zwiebeln anbraten.", " ", "Linsen zugeben und 20 Minuten köcheln."],
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"tags": ["Indisch", "#Vegetarisch", "indisch"],
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"author_notes": "",
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}
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def ai_answer(**changes):
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return json.dumps({**VALID, **changes})
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# --- Parsing & validation of AI answers ----------------------------------------------
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def test_parse_valid_response():
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recipe = ai.parse_response(ai_answer())
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assert recipe.title == "Linsen-Dal"
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assert recipe.steps == ["Zwiebeln anbraten.", "Linsen zugeben und 20 Minuten köcheln."]
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assert recipe.tags == ["indisch", "vegetarisch"]
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@pytest.mark.parametrize("content", ["", "kein json", "[]", '{"title": "x"}', json.dumps({**VALID, "ingredients": "200 g"})])
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def test_invalid_response_rejected(content):
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with pytest.raises(ai.AIError, match="ungültige Antwort"):
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ai.parse_response(content)
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def test_not_a_recipe():
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with pytest.raises(ai.AIError, match="kein Rezept"):
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ai.parse_response(ai_answer(is_recipe=False))
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with pytest.raises(ai.AIError, match="kein Rezept"):
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ai.parse_response(ai_answer(ingredients=[], steps=[]))
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def test_implausible_values_are_dropped():
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recipe = ai.parse_response(ai_answer(
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servings=0, prep_minutes=-5, cook_minutes=99999,
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ingredients=[{"group": "", "amount": -3, "amount_max": None, "unit": "g", "name": "Mehl", "note": ""}],
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))
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assert recipe.servings is None and recipe.prep_minutes is None and recipe.cook_minutes is None
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assert recipe.ingredients[0].amount is None
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def test_form_initial_roundtrips_into_data_model():
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initial = ai.parse_response(ai_answer()).to_form_initial()
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items = parse_ingredients(initial["ingredients_text"])
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assert [(i.group, str(i.amount) if i.amount is not None else None, i.unit, i.name, i.note) for i in items] == [
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("", "200", "g", "rote Linsen", "gewaschen"),
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("", "0.5", "TL", "Kurkuma", ""),
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("Zum Servieren", "2", "EL", "Joghurt", ""),
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("Zum Servieren", None, "", "Koriander", ""),
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]
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assert items[2].amount_max == 3
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assert initial["servings"] == 4 and initial["tags_text"] == "indisch, vegetarisch"
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def test_schema_is_strict_structured_output():
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def check(node):
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if node.get("type") == "object":
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assert node["additionalProperties"] is False
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assert set(node["required"]) == set(node["properties"])
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for child in node["properties"].values():
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check(child)
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if node.get("type") == "array":
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check(node["items"])
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check(ai.RECIPE_JSON_SCHEMA)
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# --- Error handling ----------------------------------------------------------------------
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REQUEST = httpx2.Request("POST", "https://api.openai.com/v1/chat/completions")
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def api_error(cls, status, message="error", body=None):
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return cls(message, response=httpx2.Response(status, request=REQUEST), body=body)
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@pytest.mark.parametrize(
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"error, text",
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[
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(openai.APITimeoutError(request=REQUEST), "zu lange"),
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(api_error(openai.RateLimitError, 429, "Rate limit reached"), "ausgelastet"),
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(api_error(openai.RateLimitError, 429, "You exceeded your current quota", {"code": "insufficient_quota"}), "Guthaben"),
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(api_error(openai.AuthenticationError, 401), "API-Key"),
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(api_error(openai.NotFoundError, 404), "OPENAI_MODEL"),
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(api_error(openai.BadRequestError, 400), "nicht verarbeiten"),
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(openai.APIConnectionError(request=REQUEST), "nicht erreichbar"),
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(api_error(openai.InternalServerError, 500), "Fehler aufgetreten"),
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],
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)
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def test_api_errors_are_mapped(error, text):
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assert text in ai.map_api_error(error).user_message
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@pytest.fixture
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def ai_on(settings):
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settings.OPENAI_API_KEY = "sk-test-not-real"
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@pytest.fixture
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def fake_openai(monkeypatch, ai_on):
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calls = []
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def fake(messages):
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calls.append(messages)
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return ai_answer()
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monkeypatch.setattr(ai, "_create_completion", fake)
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return calls
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@pytest.mark.django_db
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def test_api_error_shown_in_ui(monkeypatch, ai_on, user_client):
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def boom(messages):
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raise openai.APITimeoutError(request=REQUEST)
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monkeypatch.setattr(ai, "_create_completion", boom)
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response = user_client.post(reverse("recipes:create"), form_data(ai="1"), follow=True)
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assert "zu lange gebraucht" in response.content.decode()
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assert not Recipe.objects.exists()
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# --- Without API key -------------------------------------------------------------------------
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@pytest.mark.django_db
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def test_without_key_ai_is_disabled_but_app_works(settings, user_client):
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settings.OPENAI_API_KEY = ""
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form_page = user_client.get(reverse("recipes:create")).content.decode()
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assert "Mit KI aufbereiten" not in form_page
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assert "nicht eingerichtet" in user_client.get(reverse("recipes:import")).content.decode()
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response = user_client.post(reverse("recipes:create"), form_data(ai="1"))
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assert "nicht eingerichtet" in response.content.decode()
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assert not Recipe.objects.exists()
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# Saving manually still works
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assert user_client.post(reverse("recipes:create"), form_data()).status_code == 302
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@pytest.mark.django_db
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def test_without_key_imports_give_clear_message(settings, user_client):
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settings.OPENAI_API_KEY = ""
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response = user_client.post(reverse("recipes:import_text"), {"text": "200 g Mehl, 2 Eier, alles verrühren und backen"})
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assert "nicht eingerichtet" in response.content.decode()
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# --- Views with a (fake) model ------------------------------------------------------------------
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@pytest.mark.django_db
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def test_ai_cleanup_returns_draft_without_saving(fake_openai, user_client):
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response = user_client.post(reverse("recipes:create"), form_data(ai="1", visibility="public"))
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assert response.status_code == 200
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assert not Recipe.objects.exists()
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form = response.context["form"]
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assert form.initial["title"] == "Linsen-Dal" and form.initial["visibility"] == "public"
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assert "rote Linsen" in form.initial["ingredients_text"]
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assert "bitte prüfen" in response.content.decode()
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# user text went to the model as data
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assert "Pfannkuchen" in fake_openai[0][1]["content"]
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@pytest.mark.django_db
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def test_ai_cleanup_on_edit_keeps_recipe_until_saved(fake_openai, user_client):
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user_client.post(reverse("recipes:create"), form_data())
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recipe = Recipe.objects.get()
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response = user_client.post(reverse("recipes:edit", args=[recipe.pk]), form_data(ai="1"))
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assert response.context["form"].initial["title"] == "Linsen-Dal"
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recipe.refresh_from_db()
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assert recipe.title == "Pfannkuchen"
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@pytest.mark.django_db
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def test_text_import_creates_draft(fake_openai, user_client):
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response = user_client.post(reverse("recipes:import_text"), {
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"text": "Dal: 200g rote Linsen, Kurkuma, Joghurt. Linsen kochen.", "source_url": "https://www.instagram.com/p/abc/",
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})
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assert response.status_code == 200
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assert response.context["form"].initial["source_url"] == "https://www.instagram.com/p/abc/"
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assert response.context["form_action"] == reverse("recipes:create")
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assert not Recipe.objects.exists()
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@pytest.mark.django_db
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def test_photo_import_sends_downscaled_jpeg(fake_openai, user_client):
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response = user_client.post(reverse("recipes:import_photo"), {"photos": [image_upload(size=(4000, 3000))]})
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assert response.status_code == 200
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parts = fake_openai[0][1]["content"]
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image_part = next(p for p in parts if p["type"] == "image_url")
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assert image_part["image_url"]["url"].startswith("data:image/jpeg;base64,")
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assert response.context["form"].initial["title"] == "Linsen-Dal"
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assert not Recipe.objects.exists()
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@pytest.mark.django_db
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def test_photo_import_rejects_non_image(fake_openai, user_client):
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from django.core.files.uploadedfile import SimpleUploadedFile
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bad = SimpleUploadedFile("x.jpg", b"nope", content_type="image/jpeg")
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response = user_client.post(reverse("recipes:import_photo"), {"photos": [bad]})
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assert "kein unterstütztes Bild" in response.content.decode()
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assert not fake_openai
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@pytest.mark.django_db
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def test_ai_rate_limit_per_user(fake_openai, user_client, settings):
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settings.AI_RATE_LIMIT_PER_USER_PER_DAY = 2
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for _ in range(3):
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response = user_client.post(reverse("recipes:create"), form_data(ai="1"))
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assert len(fake_openai) == 2
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assert "Tageslimit" in response.content.decode()
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@pytest.mark.django_db
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def test_ai_global_limit(fake_openai, client, user, other_user, settings):
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settings.AI_RATE_LIMIT_GLOBAL_PER_DAY = 1
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for u in (user, other_user):
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client.force_login(u)
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response = client.post(reverse("recipes:create"), form_data(ai="1"))
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assert len(fake_openai) == 1
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assert "Kontingent" in response.content.decode()
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@pytest.mark.django_db
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def test_ai_unlimited_by_default(fake_openai, user_client):
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for _ in range(5):
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user_client.post(reverse("recipes:create"), form_data(ai="1"))
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assert len(fake_openai) == 5
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def test_text_too_short():
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with pytest.raises(ai.AIError, match="mehr Text"):
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ai.structure_text(None, "Mehl")
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# --- The real request builder against a stub client --------------------------------------
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class StubClient:
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def __init__(self, content=None, refusal=None, finish_reason="stop"):
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from types import SimpleNamespace
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self.kwargs = None
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message = SimpleNamespace(content=content, refusal=refusal)
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self._response = SimpleNamespace(choices=[SimpleNamespace(message=message, finish_reason=finish_reason)])
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self.chat = SimpleNamespace(completions=SimpleNamespace(create=self._create))
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def _create(self, **kwargs):
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self.kwargs = kwargs
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return self._response
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def test_request_uses_strict_json_schema(monkeypatch, settings):
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settings.OPENAI_MODEL = "test-model"
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settings.OPENAI_REASONING_EFFORT = "low"
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stub = StubClient(content=ai_answer())
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monkeypatch.setattr(ai, "_client", lambda: stub)
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assert ai._create_completion([{"role": "user", "content": "x"}]) == ai_answer()
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assert stub.kwargs["model"] == "test-model"
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assert stub.kwargs["reasoning_effort"] == "low"
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fmt = stub.kwargs["response_format"]
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assert fmt["type"] == "json_schema" and fmt["json_schema"]["strict"] is True
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def test_reasoning_effort_omitted_when_empty(monkeypatch, settings):
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settings.OPENAI_REASONING_EFFORT = ""
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stub = StubClient(content=ai_answer())
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monkeypatch.setattr(ai, "_client", lambda: stub)
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ai._create_completion([])
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assert "reasoning_effort" not in stub.kwargs
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def test_refusal_and_truncation(monkeypatch):
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monkeypatch.setattr(ai, "_client", lambda: StubClient(refusal="nein"))
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with pytest.raises(ai.AIError, match="abgelehnt"):
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ai._create_completion([])
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monkeypatch.setattr(ai, "_client", lambda: StubClient(content='{"is_re', finish_reason="length"))
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with pytest.raises(ai.AIError, match="zu lang"):
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ai._create_completion([])
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