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1분 만에 확인하기Eltiokarma/App-Profesional-de-Apuestas
저장소 README에서 가져옴
README 전체를 불러오는 중입니다. 아래는 저장된 발췌입니다.
Web app desktop-first y responsive para análisis pre-partido de fútbol y apuestas. Implementada en React + Vite + TypeScript a partir del prototipo de Claude Design (project/SAD.dc.html). Estética oscura tipo terminal deportiva, con tema claro/oscuro, tipografía editorial (Archivo) y monospace (IBM Plex Mono) para cuotas y métricas.README 전문 보기 ↗
Web app desktop-first y responsive para análisis pre-partido de fútbol y apuestas.
Implementada en React + Vite + TypeScript a partir del prototipo de Claude Design
(project/SAD.dc.html). Estética oscura tipo terminal deportiva, con tema claro/oscuro,
tipografía editorial (Archivo) y monospace (IBM Plex Mono) para cuotas y métricas.
Incluye header de fixture persistente, menú de partidos tipo BeSoccer agrupado por competición, estados vacíos, loading skeletons, vista de celular (tab bar inferior + previsualización en marco de teléfono con el botón 📱) y datos de ejemplo realistas. Todo en español.
src/motor/)Port fiel a TypeScript del motor de niveles y constantes K documentado en
docs/MOTOR_SAD_EXTRACCION.md (SAD v3.4), verificado contra su ejemplo numérico oficial:
levels.ts — nivel continuo por ventana móvil de 20 partidos
(Nivel = P + G + 1, G sobre los últimos 5), con la regla retroactiva del partido nº 20.constants.ts — valores instantáneos q* = dif × res × nivel_del_rival
(multiplicador visitante 1.4, fallback de nivel 1.0) y los 12 acumuladores k*
con reseteo al cambiar el signo (k_goles_recibido acumula valor absoluto;
los k local/visita conservan valor fuera de su condición).discretizer.ts — fusión k = k⁺ + k⁻ y bins fijos v6 (0–9, Sin datos → Élite).history.ts — fuente de fixtures: al no haber backend, sintetiza una historia
determinista (~40 partidos por equipo, PRNG sembrado) que alimenta el pipeline auténtico.engine.ts — orquestador memoizado: historia → niveles → constantes → fusión.npm run test:motor # verifica el motor contra el doc (§3.5, §3.2, §2, §4.1)
La app está lista para trabajar contra el backend SAD (FastAPI + PostgreSQL):
docs/openapi.yaml (fixtures, niveles, constantes K,
predicciones §5, análisis pre-partido, cuotas). El backend lo implementa; este
frontend ya lo consume.src/services/datasource.ts): mock (motor
local demo, default) o http (backend real) vía .env — ver .env.example.
El indicador de feed del sidebar hace health-check real en modo http.src/api/ (timeout, errores, bearer opcional).test:motor + build en cada push/PR.Plan completo de infraestructura (FastAPI, Neon/Supabase, API-Football, costos)
e integración: docs/SERVICIOS_EXTERNOS.md.
Solo la web (datos demo del motor local):
npm install
npm run dev # http://localhost:5173
Con datos reales (backend incluido en backend/): pega tus 4 bases
(sad.db, levels.db, constants.db, discreto.db) en la raíz del repo
(git las ignora) y usa dos terminales:
# Terminal 1 — backend
pip install -r backend/requirements.txt
python -m uvicorn backend.app:app --port 8000
# Terminal 2 — web
npm install
cp .env.example .env # VITE_DATA_SOURCE=http · VITE_API_BASE_URL=http://localhost:8000/api/v1
npm run dev # http://localhost:5173
Guía completa (demo sin bases, variables, endpoints): backend/README.md.
npm run build # typecheck + build de producción a dist/
npm run preview # sirve el build de producción
src/data/ — equipos, partidos, tablas, definiciones de mercados/skills/niveles (tipados).src/motor/ — el Motor SAD (niveles, constantes K, fusión y bins; ver sección anterior).src/lib/ — helpers puros: PRNG determinista, series de cuotas, construcción de la gráfica
(chart.ts), sparklines, vista de partido (view.ts).src/store.ts — estado central de la app (hook useSad), espejo del DCLogic del prototipo.src/components/ — sidebar, headers desktop/móvil, match picker, bottom nav, SVG de la gráfica, estados.src/sections/ — las 4 secciones (Cuotas, Burbujas, Skills, Estadísticas).project/, chats/ — bundle original de Claude Design (referencia de diseño e intención).This is a handoff bundle from Claude Design (claude.ai/design).
A user mocked up designs in HTML/CSS/JS using an AI design tool, then exported this bundle so a coding agent can implement the designs for real.
Read the chat transcripts first. There are 1 chat transcript(s) in chats/. The transcripts show the full back-and-forth between the user and the design assistant — they tell you what the user actually wants and where they landed after iterating. Don't skip them. The final HTML files are the output, but the chat is where the intent lives.
Read project/SAD.dc.html in full. The user had this file open when they triggered the handoff, so it's almost certainly the primary design they want built. Read it top to bottom — don't skim. Then follow its imports: open every file it pulls in (shared components, CSS, scripts) so you understand how the pieces fit together before you start implementing.
If anything is ambiguous, ask the user to confirm before you start implementing. It's much cheaper to clarify scope up front than to build the wrong thing.
The design medium is HTML/CSS/JS — these are prototypes, not production code. Your job is to recreate them pixel-perfectly in whatever technology makes sense for the target codebase (React, Vue, native, whatever fits). Match the visual output; don't copy the prototype's internal structure unless it happens to fit.
Don't render these files in a browser or take screenshots unless the user asks you to. Everything you need — dimensions, colors, layout rules — is spelled out in the source. Read the HTML and CSS directly; a screenshot won't tell you anything they don't.
README.md — this filechats/ — conversation transcripts (read these!)project/ — the SAD: análisis pre-partido project files (HTML prototypes, assets, components)