Form filler

Type an expense the way you'd scribble it on a receipt — "lunch with the

README Pruébalo Código

Showcase — Form filler

Type an expense the way you'd scribble it on a receipt — "lunch with the client, 38.50 eur, last tuesday" — and get back a validated record ready to insert: merchant, amount as a real number, currency, a category from your set, and a date. The schema does double duty: it shapes the request and validates the reply.

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 — the Expense schema (typed + Literal category) and the OpenAI call
backend/ai_gemini.py — same schema, Gemini call
backend/main.py — identical FastAPI loader; reads PROVIDER and dispatches
frontend/app/page.tsx — textarea + result (rendered as JSON)
docker-compose.yml — two services, secrets mounted from ./secrets/

run(input: str) -> str returns the validated record as pretty JSON, keeping the contract identical across the week's showcases.

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 form-filler.zip

unzip form-filler.zip
cd form-filler
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 4 - Showcase 3 (OpenAI): turn a messy note into a validated record.

This is the one closest to real work: a one-line expense note becomes a
structured row ready to insert. The schema does double duty — it tells the
model the shape and it validates the reply, so amount is a number and
category is one of yours, not whatever the model felt like typing.
"""
import json
from typing import Literal

from openai import OpenAI
from pydantic import BaseModel

_client = OpenAI()


class Expense(BaseModel):
    merchant: str
    amount: float
    currency: str
    category: Literal["travel", "meals", "software", "hardware", "other"]
    date: str


def run(text: str) -> str:
    response = _client.responses.parse(
        model="gpt-5.4-nano",
        input="Turn this expense note into a structured record. Infer the "
              "category. Use ISO format for the date if one is given.\n\n"
              f"{text.strip()}",
        text_format=Expense,
    )
    return json.dumps(response.output_parsed.model_dump(), indent=2)

`backend/ai_gemini.py`

"""Week 4 - Showcase 3 (Gemini): turn a messy note into a validated record."""
import json
import os
from typing import Literal

from google import genai
from google.genai import types
from pydantic import BaseModel

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


class Expense(BaseModel):
    merchant: str
    amount: float
    currency: str
    category: Literal["travel", "meals", "software", "hardware", "other"]
    date: str


def run(text: str) -> str:
    response = _client.models.generate_content(
        model="gemini-3.1-flash-lite",
        contents="Turn this expense note into a structured record. Infer the "
                 "category. Use ISO format for the date if one is given.\n\n"
                 f"{text.strip()}",
        config=types.GenerateContentConfig(
            response_mime_type="application/json",
            response_schema=Expense,
        ),
    )
    return json.dumps(response.parsed.model_dump(), indent=2)