Recipe scaler

Paste a recipe and say "make it for 12" or "cut it in half". The model parses

README Pruébalo Código

Showcase — Recipe scaler

Paste a recipe and say "make it for 12" or "cut it in half". The model parses the free-text ingredient list into a typed array — the tool schema's array-of- objects parameter is the lesson here — and Python scales every quantity exactly, rendering 0.75 back as 3/4. Models scaling recipes in their head drop ingredients and fumble fractions; a structured function call can't.

Run

bash bootstrap-secrets.sh              # reads ../../../../.env, writes secrets/
docker compose up --build              # default: PROVIDER=openai

Open http://localhost:3000.

To run against Gemini instead:

PROVIDER=gemini docker compose up --build

What's where

backend/ai_openai.py — scale_recipe with the array-of-objects schema, Fraction-based pretty quantities, and the round trip
backend/ai_gemini.py — same tool via nested types.Schema objects
backend/main.py — identical FastAPI loader; reads PROVIDER and dispatches
frontend/app/page.tsx — textarea + result
docker-compose.yml — two services, secrets mounted from ./secrets/

The scaled quantities come back through fractions.Fraction, so 1/3 cup doubled is 2/3 cup — not 0.6666666.

Stop

docker compose down

Ejecútalo en tu máquina

Descarga el proyecto como ZIP y córrelo con Docker. Levanta un backend FastAPI y un frontend Next.js en localhost:3000.

Descargar recipe-scaler.zip

unzip recipe-scaler.zip
cd recipe-scaler
bash bootstrap-secrets.sh   # one-time: pulls API keys into ./secrets
docker compose up --build   # default provider: openai
# or:  PROVIDER=gemini docker compose up --build

Escribe algo, elige un proveedor y ejecuta el mismo código de Código contra la API real. Requiere iniciar sesión.

Entrada

Proveedor

Output

Los mismos módulos que ejecuta el botón Run. El proyecto completo (frontend, Dockerfile, compose) está en el ZIP, pestaña README.

`backend/ai_openai.py`

"""Week 5 - Showcase 3 (OpenAI): scale a recipe with exact arithmetic.

The interesting part of the tool schema here is the array parameter: the
model parses the whole ingredient list out of free text into a typed
array of objects, and Python multiplies every quantity exactly. LLMs
drop items and fumble fractions when they scale recipes in their head;
a structured call can't.
"""
import json
from fractions import Fraction

from openai import OpenAI

_client = OpenAI()

_MODEL = "gpt-5.4-nano"


def _pretty(qty: float) -> str:
    """Render 0.75 as 3/4 — kitchen-friendly amounts, not floats."""
    frac = Fraction(qty).limit_denominator(8)
    whole, rem = divmod(frac.numerator, frac.denominator)
    if rem == 0:
        return str(whole)
    if whole == 0:
        return f"{rem}/{frac.denominator}"
    return f"{whole} {rem}/{frac.denominator}"


def _scale_recipe(ingredients: list, factor: float) -> dict:
    scaled = [{
        "name": item["name"],
        "quantity": _pretty(float(item["quantity"]) * factor),
        "unit": item.get("unit", ""),
    } for item in ingredients]
    return {"factor": factor, "ingredients": scaled}


_TOOLS = [{
    "type": "function",
    "name": "scale_recipe",
    "description": "Scale every ingredient quantity by a factor, exactly. "
                   "Pass ALL ingredients found in the recipe text.",
    "parameters": {
        "type": "object",
        "properties": {
            "ingredients": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "name": {"type": "string"},
                        "quantity": {"type": "number"},
                        "unit": {"type": "string"},
                    },
                    "required": ["name", "quantity"],
                },
            },
            "factor": {"type": "number",
                       "description": "e.g. 3 to triple, 0.5 to halve."},
        },
        "required": ["ingredients", "factor"],
    },
}]


def run(recipe_request: str) -> str:
    response = _client.responses.create(
        model=_MODEL,
        instructions="Scale recipes. Parse the ingredients and the requested "
                     "factor from the text, call scale_recipe with all of them, "
                     "then present the scaled list. Never multiply quantities yourself.",
        input=recipe_request.strip(),
        tools=_TOOLS,
    )

    calls = [item for item in response.output if item.type == "function_call"]
    if not calls:
        return response.output_text

    outputs = []
    for call in calls:
        args = json.loads(call.arguments)
        try:
            result = _scale_recipe(**args)
        except (KeyError, TypeError, ValueError) as exc:
            result = {"error": str(exc)}
        outputs.append({
            "type": "function_call_output",
            "call_id": call.call_id,
            "output": json.dumps(result),
        })

    final = _client.responses.create(
        model=_MODEL,
        previous_response_id=response.id,
        input=outputs,
        tools=_TOOLS,
    )
    return final.output_text

`backend/ai_gemini.py`

"""Week 5 - Showcase 3 (Gemini): scale a recipe with exact arithmetic."""
import os
from fractions import Fraction

from google import genai
from google.genai import types

_client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

_MODEL = "gemini-3.1-flash-lite"


def _pretty(qty: float) -> str:
    """Render 0.75 as 3/4 — kitchen-friendly amounts, not floats."""
    frac = Fraction(qty).limit_denominator(8)
    whole, rem = divmod(frac.numerator, frac.denominator)
    if rem == 0:
        return str(whole)
    if whole == 0:
        return f"{rem}/{frac.denominator}"
    return f"{whole} {rem}/{frac.denominator}"


def _scale_recipe(ingredients: list, factor: float) -> dict:
    scaled = [{
        "name": item["name"],
        "quantity": _pretty(float(item["quantity"]) * factor),
        "unit": item.get("unit", ""),
    } for item in ingredients]
    return {"factor": factor, "ingredients": scaled}


_CONFIG = types.GenerateContentConfig(
    system_instruction="Scale recipes. Parse the ingredients and the requested "
                       "factor from the text, call scale_recipe with all of them, "
                       "then present the scaled list. Never multiply quantities yourself.",
    tools=[types.Tool(function_declarations=[types.FunctionDeclaration(
        name="scale_recipe",
        description="Scale every ingredient quantity by a factor, exactly. "
                    "Pass ALL ingredients found in the recipe text.",
        parameters=types.Schema(
            type=types.Type.OBJECT,
            properties={
                "ingredients": types.Schema(
                    type=types.Type.ARRAY,
                    items=types.Schema(
                        type=types.Type.OBJECT,
                        properties={
                            "name": types.Schema(type=types.Type.STRING),
                            "quantity": types.Schema(type=types.Type.NUMBER),
                            "unit": types.Schema(type=types.Type.STRING),
                        },
                        required=["name", "quantity"],
                    ),
                ),
                "factor": types.Schema(type=types.Type.NUMBER,
                                       description="e.g. 3 to triple, 0.5 to halve."),
            },
            required=["ingredients", "factor"],
        ),
    )])],
)


def run(recipe_request: str) -> str:
    contents = [types.Content(role="user", parts=[types.Part(text=recipe_request.strip())])]
    response = _client.models.generate_content(
        model=_MODEL, contents=contents, config=_CONFIG,
    )

    if not response.function_calls:
        return response.text or ""

    contents.append(response.candidates[0].content)
    parts = []
    for fc in response.function_calls:
        try:
            result = _scale_recipe(**fc.args)
        except (KeyError, TypeError, ValueError) as exc:
            result = {"error": str(exc)}
        parts.append(types.Part.from_function_response(name=fc.name, response=result))
    contents.append(types.Content(role="user", parts=parts))

    final = _client.models.generate_content(
        model=_MODEL, contents=contents, config=_CONFIG,
    )
    return final.text or ""