Classify JSON

Drop in a support message, get back an object you can branch on without ever

README Pruébalo Código

Showcase — Classify JSON

Drop in a support message, get back an object you can branch on without ever parsing text: a label from a fixed set, a confidence score, and a short list of reasons. Week 2 coaxed a label out as a string and hoped the format held. Here the schema makes it a guarantee.

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 Classification schema (Literal label + float + list) 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 object 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 classify-json.zip

unzip classify-json.zip
cd classify-json
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 2 (OpenAI): classify into a guaranteed-parseable object.

Week 2's few-shot showcase coaxed a label out as text and hoped it stayed
on format. This does the same job, but the schema makes the format a
guarantee: a label from a fixed set, a confidence float, and the reasons.
No parsing, no regex, no praying.
"""
import json
from typing import Literal

from openai import OpenAI
from pydantic import BaseModel

_client = OpenAI()


class Classification(BaseModel):
    label: Literal["billing", "bug", "feature_request", "praise", "other"]
    confidence: float
    reasons: list[str]


def run(text: str) -> str:
    response = _client.responses.parse(
        model="gpt-5.4-nano",
        input="Classify this support message. Give the label, your confidence "
              f"from 0 to 1, and a short reason or two.\n\n{text.strip()}",
        text_format=Classification,
    )
    return json.dumps(response.output_parsed.model_dump(), indent=2)

`backend/ai_gemini.py`

"""Week 4 - Showcase 2 (Gemini): classify into a guaranteed-parseable object."""
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 Classification(BaseModel):
    label: Literal["billing", "bug", "feature_request", "praise", "other"]
    confidence: float
    reasons: list[str]


def run(text: str) -> str:
    response = _client.models.generate_content(
        model="gemini-3.1-flash-lite",
        contents="Classify this support message. Give the label, your confidence "
                 f"from 0 to 1, and a short reason or two.\n\n{text.strip()}",
        config=types.GenerateContentConfig(
            response_mime_type="application/json",
            response_schema=Classification,
        ),
    )
    return json.dumps(response.parsed.model_dump(), indent=2)