Explain a snippet

Paste a piece of code or a shell one-liner. The model returns one short

README Pruébalo Código

Showcase — Explain a snippet

Paste a piece of code or a shell one-liner. The model returns one short paragraph of plain-English explanation. One API call, useful from the first minute.

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 OpenAI call (this week's lesson, applied)
backend/ai_gemini.py — the Gemini call (same lesson, other SDK)
backend/main.py — FastAPI that loads ai_<PROVIDER> and exposes /api/ai
frontend/app/page.tsx — single form + result <pre>
docker-compose.yml — two services, secrets mounted from ./secrets/

Stop

docker compose down

Reclaim disk after a session (the Next.js image is ~150 MB):

docker compose down --rmi all -v

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 explain-snippet.zip

unzip explain-snippet.zip
cd explain-snippet
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 1 - Showcase 1 (OpenAI): explain a code or shell snippet.

Same one API call as the basic example, pointed at something useful.
"""
from openai import OpenAI

_client = OpenAI()  # reads OPENAI_API_KEY from the environment.


def run(snippet: str) -> str:
    prompt = (
        "Explain the following code or shell one-liner in one short paragraph. "
        "Be precise about what each non-obvious flag, operator, or function call "
        "means. Prose only; do not include code in your answer."
        f"\n\n---\n{snippet}\n---"
    )
    # max_output_tokens caps the response length so an unexpected
    # input can't run up the bill. 2048 tokens (~6k chars) is plenty
    # for a one-paragraph explanation.
    response = _client.responses.create(
        model="gpt-5.4-nano", input=prompt, max_output_tokens=2048,
    )
    return response.output_text

`backend/ai_gemini.py`

"""Week 1 - Showcase 1 (Gemini): explain a code or shell snippet."""
import os

from google import genai
from google.genai import types

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


def run(snippet: str) -> str:
    prompt = (
        "Explain the following code or shell one-liner in one short paragraph. "
        "Be precise about what each non-obvious flag, operator, or function call "
        "means. Prose only; do not include code in your answer."
        f"\n\n---\n{snippet}\n---"
    )
    # Cap the response length so a pathological input can't make the
    # model write an essay. 2048 tokens (~6k chars) is plenty for a
    # one-paragraph explanation.
    response = _client.models.generate_content(
        model="gemini-3.1-flash-lite", contents=prompt,
        config=types.GenerateContentConfig(max_output_tokens=2048),
    )
    return response.text