feat: implement FastAPI chat API and connect React client

Dockerfile

@@ -1,34 +1,23 @@
-# Dockerfile
-
-# Base image to use
-FROM tiangolo/uvicorn-gunicorn-fastapi:python3.8
-
-# Set the working directory to /app
+# ---------- Frontend build ----------
+FROM node:20-bookworm-slim AS frontend-builder
+WORKDIR /app/frontend
+COPY frontend/package.json frontend/package-lock.json* ./
+RUN npm install
+COPY frontend .
+RUN npm run build
+
+# ---------- Backend runtime ----------
+FROM python:3.11-slim
 WORKDIR /app
 
-# Copy the backend application files
-COPY ./backend /app/backend
-
-# Copy the frontend application files
-COPY ./frontend /app/frontend
-
-# Install dependencies for the backend
+COPY backend/requirements.txt /app/backend/requirements.txt
 RUN pip install --no-cache-dir -r /app/backend/requirements.txt
 
-# Set the environment variables for FastAPI
-ENV MODULE_NAME=backend.main:app
-
-# Expose port 80 for the FastAPI app
-EXPOSE 80
-
-# Install Node.js for frontend
-RUN apt-get update && apt-get install -y nodejs npm
-
-# Build the React frontend
-RUN cd frontend && npm install && npm run build
+COPY backend /app/backend
+COPY --from=frontend-builder /app/frontend/dist /app/frontend/dist
 
-# Serve the React app
-RUN cp -R frontend/build /app/frontend/build
+# Hugging Face Spaces expone PORT (fallback 7860)
+ENV PORT=7860
+EXPOSE 7860
 
-# Start the FastAPI app
-CMD uvicorn backend.main:app --host 0.0.0.0 --port 80 --reload
+CMD sh -c "uvicorn backend.main:app --host 0.0.0.0 --port ${PORT}"

MIGRACION_REACT_HF.md

@@ -0,0 +1,140 @@
+# Plan de migración: lógica Python (Streamlit) → React moderno + backend compatible con Hugging Face Spaces
+
+## 1) Hallazgos de la revisión actual
+
+### Backend Python existente
+- `app.py` contiene prácticamente toda la lógica de producto: manejo de sesiones, títulos de chat, menú inicial, prompts de ejemplo y streaming de respuesta desde Gemini. Está fuertemente acoplado a Streamlit (`st.session_state`, `st.chat_message`, `st.chat_input`, `st.rerun`).
+- `session_state.py` implementa persistencia local por usuario (`data/<namespace>`) y encapsula llamadas a `google-genai` con `send_message_stream`.
+- `system_prompts.py` define el prompt maestro del flujo de 5 preguntas y formato final del email.
+- `firebase_store.py` ya anticipa un backend con persistencia remota (Firestore) para índice/historial de chats.
+
+### Frontend React existente
+- Ya hay estructura Vite + React + TypeScript.
+- `ChatWindow.tsx` consume `POST /api/chat/send`, pero ese endpoint no existe en `backend/main.py`.
+- `ChatWindow.tsx` actualmente reinicia mensajes al cambiar chat y no carga historial desde servidor.
+- `ChatHistory.tsx` y `App.tsx` mantienen datos en memoria local del navegador, sin sincronización real.
+
+### Desalineaciones de infraestructura
+- `backend/main.py` solo monta archivos estáticos y no expone API funcional.
+- `backend/gemini_service.py` usa un endpoint no estándar de Gemini (`https://gemini.googleapis.com/v1/email/generate`) y no coincide con el SDK usado en `session_state.py`.
+- `Dockerfile` espera `frontend/build`, pero Vite genera `dist`.
+
+## 2) Objetivo técnico recomendado
+
+Separar la app en capas limpias:
+1. **Frontend React (UI + estado de vista)**
+2. **Backend FastAPI (lógica de negocio + Gemini + persistencia)**
+3. **Proveedor de almacenamiento** (local JSON/SQLite para MVP y Firebase opcional)
+
+Esto te deja una base escalable para HF Spaces (Docker SDK) y para crecer a multiusuario real.
+
+## 3) Qué lógica debes mover de Python a React y cuál NO
+
+### Mover a React (presentación y UX)
+- Render de chat (mensajes, indicador "escribiendo", input).
+- Menú inicial y botones de ejemplos.
+- Sidebar de sesiones y navegación entre chats.
+- Estado visual (loading, error, disabled states).
+
+### Mantener en backend (dominio y seguridad)
+- Lógica de `get_enhanced_prompt` (saludo y modo ejemplo).
+- Generación de título de chat por IA.
+- Integración con Gemini y streaming.
+- Persistencia de historiales por usuario/chat.
+- Reglas de negocio del flujo guiado.
+
+> Regla práctica: si algo implica API keys, reglas de negocio o consistencia de datos, va en backend.
+
+## 4) Arquitectura de API sugerida (contratos)
+
+### Endpoints mínimos
+- `POST /api/chats` → crea chat y devuelve `{chat_id, title}`
+- `GET /api/chats` → lista chats del usuario
+- `GET /api/chats/{chat_id}/messages` → historial
+- `POST /api/chats/{chat_id}/messages` → agrega mensaje y devuelve respuesta IA
+- `GET /api/prompts/examples` → ejemplos iniciales (opcional, para no hardcodear)
+
+### Streaming recomendado
+Para una UX moderna:
+- Preferir **SSE** (`text/event-stream`) o **chunked responses** para tokens.
+- Frontend consume stream y va pintando texto incrementalmente.
+
+## 5) Plan de implementación por fases
+
+### Fase 0 — Alineación de base
+1. Consolidar `system_prompts.py` en backend y eliminar duplicados.
+2. Definir modelos Pydantic (`Chat`, `Message`, `SendMessageRequest`, `SendMessageResponse`).
+3. Corregir `backend/requirements.txt` para usar `google-genai`.
+
+### Fase 1 — Backend funcional
+1. Crear `backend/services/chat_service.py` con:
+   - `create_chat`
+   - `list_chats`
+   - `send_message`
+   - `load_history`
+2. Crear `backend/repositories` con interfaz de persistencia:
+   - `local_repo.py` (MVP)
+   - `firebase_repo.py` (adaptando `firebase_store.py`)
+3. Implementar rutas en `backend/main.py` para `/api/*`.
+
+### Fase 2 — Frontend robusto
+1. Crear capa de cliente API en `frontend/src/lib/api.ts`.
+2. Introducir estado global con **TanStack Query** + store liviano (Zustand o Context).
+3. Refactor de componentes:
+   - `ChatShell` (layout)
+   - `ChatSidebar`
+   - `MessageList`
+   - `Composer`
+4. Manejar errores con toasts y estados vacíos.
+
+### Fase 3 — Hugging Face Spaces
+1. Ajustar `Dockerfile` multi-stage:
+   - Stage Node: build de Vite (`dist`)
+   - Stage Python: FastAPI + copiar `dist` a carpeta estática
+2. FastAPI debe servir estáticos y fallback SPA (`index.html`).
+3. Variables HF:
+   - `GOOGLE_API_KEY`
+   - opcional: `FIREBASE_SERVICE_ACCOUNT_JSON`
+
+### Fase 4 — Escalabilidad
+1. Añadir auth (al menos token por usuario o Firebase Auth).
+2. Trazas/observabilidad (logs estructurados + request_id).
+3. Tests:
+   - unit tests de servicio
+   - contract tests de API
+   - e2e básico de chat
+
+## 6) Decisiones técnicas concretas recomendadas
+
+- **Gemini**: usar solo `google-genai` (evitar `requests` custom mientras no sea imprescindible).
+- **Persistencia**: iniciar con SQLite o JSON por simplicidad; dejar interfaz para Firebase.
+- **Estado frontend**:
+  - Server state: TanStack Query
+  - UI state: Zustand/Context
+- **Tipado**: compartir esquemas con OpenAPI generado por FastAPI + tipos TS (openapi-typescript).
+
+## 7) Riesgos actuales que debes corregir primero
+
+1. Endpoint `/api/chat/send` inexistente (rompe el frontend actual).
+2. `Dockerfile` incompatible con salida Vite (`dist` vs `build`).
+3. Doble fuente de verdad (Streamlit app y React app conviviendo sin contratos unificados).
+4. Persistencia local por archivos (`joblib`) no ideal para multiinstancia.
+
+## 8) Backlog accionable (orden sugerido)
+
+1. Implementar API real en `backend/main.py`.
+2. Conectar `ChatWindow.tsx` a nueva API de chats/mensajes.
+3. Reemplazar estado local de chats en `App.tsx` por datos del backend.
+4. Migrar streaming al frontend con SSE.
+5. Ajustar Docker para HF y probar build end-to-end.
+
+## 9) Criterios de “migración completada”
+
+- La UI React reproduce toda la UX importante de Streamlit (menu inicial, ejemplos, historial, multi-chat).
+- Toda llamada a Gemini pasa por FastAPI (ninguna key en frontend).
+- Persistencia de chats disponible al recargar.
+- Docker build exitoso y despliegue en HF Space operativo.
+
+---
+
+Si quieres, en la siguiente iteración puedo convertir este plan en tareas técnicas concretas por archivo (`backend/main.py`, `frontend/src/components/*`, `Dockerfile`) y proponerte un PR incremental fase por fase.

README.md

@@ -1,97 +1,51 @@
-# Chatbot Email App - React + FastAPI 2
+# Chatbot Email App - React + FastAPI
 
-Una aplicación moderna de chatbot para generar copies de correos con IA usando Gemini API.
+Aplicación de chat para generar emails narrativos con Gemini.
 
-## 🚀 Características
+## Stack
+- Frontend: React + TypeScript + Vite
+- Backend: FastAPI
+- IA: Google Gemini (`google-genai`)
 
-- ✉️ Generación de emails narrativos con Gemini AI
-- 💬 Chat interactivo y responsive
-- 📱 Interfaz moderna con React + TypeScript
-- ⚡ Backend rápido con FastAPI
-- 🐳 Fácil de desplegar en HF Spaces con Docker
-
-## 📋 Requisitos
-
-- Docker (para HF Spaces)
-- Node.js 18+ (para desarrollo local)
-- Python 3.8+ (para desarrollo local)
-
-## 🛠️ Instalación Local
+## Desarrollo local
 
 ### Backend
-
 ```bash
 cd backend
 pip install -r requirements.txt
+uvicorn main:app --reload
 ```
 
 ### Frontend
-
 ```bash
 cd frontend
 npm install
-```
-
-## 🏃 Ejecutar Localmente
-
-### Terminal 1 - Backend
-
-```bash
-cd backend
-python -m uvicorn main:app --reload
-```
-
-### Terminal 2 - Frontend
-
-```bash
-cd frontend
 npm run dev
 ```
 
-Accede a `http://localhost:5173`
-
-## 🐳 Desplegar en HF Spaces
-
-1. Ve a [huggingface.co/spaces](https://huggingface.co/spaces)
-2. Click en "Create new Space"
-3. Selecciona "Docker" como SDK
-4. Conecta tu repositorio de GitHub
-5. Configura las variables de entorno:
-   - `GOOGLE_API_KEY`: Tu clave de Gemini API
-
-## 📁 Estructura del Proyecto
-
-```
-chatbot-email-app/
-├── backend/
-│   ├── main.py
-│   └── requirements.txt
-├── frontend/
-│   ├── src/
-│   │   ├── components/
-│   │   ├── App.tsx
-│   │   ├── App.css
-│   │   └── main.tsx
-│   ├── package.json
-│   ├── tsconfig.json
-│   ├── vite.config.ts
-│   └── index.html
-├── Dockerfile
-└── README.md
-```
-
-## 🔑 Variables de Entorno
+Frontend en `http://localhost:3000` y backend en `http://localhost:8000`.
 
-Crea un archivo `.env` en la raíz del backend:
+## Variables de entorno
 
-```
-GOOGLE_API_KEY=your_gemini_api_key_here
-```
-
-## 📄 Licencia
-
-Apache License 2.0
-
-## 👨‍💻 Autor
-
-**squaalldev** - Jesús Cabrera
+### Backend
+- `GOOGLE_API_KEY` (requerida)
+- `GEMINI_MODEL` (opcional, default: `gemini-2.5-flash`)
+- `APP_DATA_DIR` (opcional, default: `backend/data`)
+
+## Deploy en Hugging Face Spaces (Docker)
+
+1. Crea un Space tipo **Docker**.
+2. Sube este repositorio.
+3. En **Settings → Secrets**, agrega:
+   - `GOOGLE_API_KEY`
+4. (Opcional) agrega `GEMINI_MODEL`.
+
+> Sí, en HF puedes inyectar el secret directamente y esta app lo toma desde `os.environ["GOOGLE_API_KEY"]` en runtime.
+
+## Endpoints API
+- `GET /api/health`
+- `GET /api/chats`
+- `POST /api/chats`
+- `GET /api/chats/{chat_id}/messages`
+- `POST /api/chats/{chat_id}/messages`
+- Compatibilidad legacy: `POST /api/chat/send`

backend/gemini_service.py

@@ -1,31 +0,0 @@
-import requests
-import json
-
-class GeminiService:
-    def __init__(self, api_key):
-        self.api_key = api_key
-        self.endpoint = 'https://gemini.googleapis.com/v1/email/generate'
-
-    def generate_email(self, system_prompt, chat_history):
-        # Prepare the payload for the API request
-        payload = {
-            'system_prompt': system_prompt,
-            'chat_history': chat_history
-        }
-
-        headers = {
-            'Authorization': f'Bearer {self.api_key}',
-            'Content-Type': 'application/json'
-        }
-
-        try:
-            response = requests.post(self.endpoint, headers=headers, json=payload)
-            response.raise_for_status()  # Raise an error for bad responses
-            return response.json()  # Return the JSON response
-        except requests.exceptions.RequestException as e:
-            print(f'Error during API call: {e}')
-            return None
-
-# Example usage:
-# service = GeminiService('your_api_key')
-# email_response = service.generate_email('Generate an email for...', ['Chat history item 1', 'Chat history item 2'])

backend/main.py

@@ -1,11 +1,245 @@
-from fastapi import FastAPI
+from __future__ import annotations
+
+import json
+import os
+import time
+import uuid
+from pathlib import Path
+from typing import Literal
+
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles
+from google import genai
+from google.genai import types
+from pydantic import BaseModel, Field
+
+from backend.system_prompts import get_enhanced_prompt, get_unified_email_prompt
+
+DEFAULT_MODEL = os.environ.get("GEMINI_MODEL", "gemini-2.5-flash")
+DATA_DIR = Path(os.environ.get("APP_DATA_DIR", "backend/data"))
+FRONTEND_DIST = Path("frontend/dist")
+
+
+class MessageIn(BaseModel):
+    content: str = Field(min_length=1)
+    is_example: bool = False
+
+
+class CreateChatIn(BaseModel):
+    title: str | None = None
+
+
+class ChatSummary(BaseModel):
+    chat_id: str
+    title: str
+    updated_at: float
+
+
+class MessageOut(BaseModel):
+    role: Literal["user", "assistant"]
+    content: str
+
+
+class ChatMessageResponse(BaseModel):
+    response: str
+    chat_id: str
+
+
+app = FastAPI(title="Chatbot Email API")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+if FRONTEND_DIST.exists():
+    app.mount("/assets", StaticFiles(directory=FRONTEND_DIST / "assets"), name="assets")
+
+
+def _namespace(request: Request) -> str:
+    user_id = request.headers.get("x-user-id") or request.query_params.get("uid") or "default"
+    return "".join(ch for ch in user_id if ch.isalnum() or ch in ("-", "_")) or "default"
+
+
+def _user_dir(namespace: str) -> Path:
+    user_dir = DATA_DIR / namespace
+    user_dir.mkdir(parents=True, exist_ok=True)
+    return user_dir
+
+
+def _index_path(namespace: str) -> Path:
+    return _user_dir(namespace) / "chats_index.json"
+
+
+def _chat_path(namespace: str, chat_id: str) -> Path:
+    return _user_dir(namespace) / f"{chat_id}.json"
+
+
+def _read_json(path: Path, default):
+    if not path.exists():
+        return default
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def _write_json(path: Path, payload) -> None:
+    path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
+
+
+def _load_index(namespace: str) -> dict[str, dict]:
+    return _read_json(_index_path(namespace), default={})
+
+
+def _save_index(namespace: str, data: dict[str, dict]) -> None:
+    _write_json(_index_path(namespace), data)
+
+
+def _load_messages(namespace: str, chat_id: str) -> list[dict]:
+    payload = _read_json(_chat_path(namespace, chat_id), default={"messages": []})
+    return payload.get("messages", [])
+
+
+def _save_messages(namespace: str, chat_id: str, messages: list[dict]) -> None:
+    _write_json(_chat_path(namespace, chat_id), {"messages": messages})
+
+
+def _generate_title(client: genai.Client, prompt: str) -> str:
+    try:
+        result = client.models.generate_content(
+            model=DEFAULT_MODEL,
+            contents=(
+                "Genera un título natural y humano en español (3 a 6 palabras) "
+                "que resuma esta consulta. Devuelve solo el título final: "
+                f"'{prompt}'"
+            ),
+        )
+        title = " ".join((result.text or "").strip().replace('"', "").split())
+        return " ".join(title.split()[:6]) or f"Sesión-{int(time.time())}"
+    except Exception:
+        return f"Sesión-{int(time.time())}"
+
+
+def _build_contents(messages: list[dict]) -> list[types.Content]:
+    contents: list[types.Content] = []
+    for msg in messages:
+        role = "user" if msg["role"] == "user" else "model"
+        contents.append(types.Content(role=role, parts=[types.Part(text=msg["content"])]))
+    return contents
+
+
+@app.get("/api/health")
+def health_check():
+    return {"ok": True}
+
+
+@app.get("/api/chats", response_model=list[ChatSummary])
+def list_chats(request: Request):
+    index = _load_index(_namespace(request))
+    rows = [
+        ChatSummary(chat_id=chat_id, title=row["title"], updated_at=row.get("updated_at", 0.0))
+        for chat_id, row in index.items()
+    ]
+    return sorted(rows, key=lambda row: row.updated_at, reverse=True)
+
+
+@app.post("/api/chats", response_model=ChatSummary)
+def create_chat(payload: CreateChatIn, request: Request):
+    namespace = _namespace(request)
+    chat_id = str(int(time.time() * 1000)) + uuid.uuid4().hex[:6]
+    index = _load_index(namespace)
+    now = time.time()
+    title = payload.title or f"Sesión-{chat_id[-6:]}"
+    index[chat_id] = {"title": title, "updated_at": now}
+    _save_index(namespace, index)
+    _save_messages(namespace, chat_id, [])
+    return ChatSummary(chat_id=chat_id, title=title, updated_at=now)
+
+
+@app.get("/api/chats/{chat_id}/messages", response_model=list[MessageOut])
+def get_messages(chat_id: str, request: Request):
+    namespace = _namespace(request)
+    index = _load_index(namespace)
+    if chat_id not in index:
+        raise HTTPException(status_code=404, detail="Chat no encontrado")
+    messages = _load_messages(namespace, chat_id)
+    return [MessageOut(role=m["role"], content=m["content"]) for m in messages]
+
+
+@app.post("/api/chats/{chat_id}/messages", response_model=ChatMessageResponse)
+def send_message(chat_id: str, payload: MessageIn, request: Request):
+    api_key = os.environ.get("GOOGLE_API_KEY")
+    if not api_key:
+        raise HTTPException(
+            status_code=500,
+            detail=(
+                "GOOGLE_API_KEY no está configurada. En Hugging Face Spaces agrega este valor en Settings > Secrets."
+            ),
+        )
+
+    namespace = _namespace(request)
+    index = _load_index(namespace)
+    if chat_id not in index:
+        raise HTTPException(status_code=404, detail="Chat no encontrado")
+
+    messages = _load_messages(namespace, chat_id)
+    user_message = {"role": "user", "content": payload.content}
+    messages.append(user_message)
+
+    client = genai.Client(api_key=api_key)
+    first_user_message = len([m for m in messages if m["role"] == "user"]) == 1
+    enhanced_prompt = get_enhanced_prompt(
+        payload.content,
+        is_example=payload.is_example,
+        is_first_user_message=first_user_message,
+    )
+
+    messages_for_model = messages[:-1] + [{"role": "user", "content": enhanced_prompt}]
+
+    response = client.models.generate_content(
+        model=DEFAULT_MODEL,
+        contents=_build_contents(messages_for_model),
+        config=types.GenerateContentConfig(system_instruction=get_unified_email_prompt()),
+    )
+
+    assistant_text = (response.text or "").strip()
+    if not assistant_text:
+        assistant_text = "No pude generar respuesta en este intento."
+
+    messages.append({"role": "assistant", "content": assistant_text})
+    _save_messages(namespace, chat_id, messages)
+
+    if not index[chat_id].get("title") or index[chat_id]["title"].startswith("Sesión-"):
+        index[chat_id]["title"] = _generate_title(client, payload.content)
+    index[chat_id]["updated_at"] = time.time()
+    _save_index(namespace, index)
+
+    return ChatMessageResponse(response=assistant_text, chat_id=chat_id)
+
 
-app = FastAPI()
+# Backward compatibility for previous React call
+@app.post("/api/chat/send", response_model=ChatMessageResponse)
+def send_message_legacy(payload: MessageIn, request: Request):
+    namespace = _namespace(request)
+    index = _load_index(namespace)
+    if not index:
+        chat = create_chat(CreateChatIn(title="Nuevo chat"), request)
+    else:
+        latest_chat = sorted(index.items(), key=lambda item: item[1].get("updated_at", 0), reverse=True)[0]
+        chat = ChatSummary(chat_id=latest_chat[0], title=latest_chat[1]["title"], updated_at=latest_chat[1]["updated_at"])
+    return send_message(chat.chat_id, payload, request)
 
-# Serve the React frontend build
-app.mount("/static", StaticFiles(directory="static"), name="static")
 
-@app.get("/")
-async def read_root():
-    return {"message": "Welcome to the FastAPI backend serving the React app"}
+@app.get("/{full_path:path}")
+def serve_react_app(full_path: str):
+    index_file = FRONTEND_DIST / "index.html"
+    if index_file.exists():
+        return FileResponse(index_file)
+    return {
+        "message": "API running. Frontend build not found.",
+        "hint": "Build frontend with: cd frontend && npm run build",
+    }

backend/requirements.txt

@@ -1,2 +1,5 @@
 fastapi
-gemini
+uvicorn
+google-genai
+pydantic
+python-dotenv

backend/system_prompts.py

@@ -1,47 +1,110 @@
-# Email Generation Prompt System
+from __future__ import annotations
 
-def email_generation_prompt():
-    print("Welcome to the Email Generation System! Please answer the following questions:")
 
-    questions = [
-        "1. What is the main purpose of your email? (e.g., inquiry, response, update)",
-        "2. Who is the recipient of your email? (e.g., colleague, client, friend)",
-        "3. What specific information do you want to convey?",
-        "4. What tone do you want to use? (e.g., formal, casual, friendly)",
-        "5. Do you want to include any attachments? (yes/no)"
-    ]
+def get_unified_email_prompt() -> str:
+    return """### [IMPRIMACIÓN COGNITIVA]
+- Modelos fundacionales: Storytelling Marketing, Show Don't Tell, PAS, Golden Circle (empezar con el porqué).
+- Corpus de conocimiento: estilo tipo Seth Godin, estructura narrativa tipo StoryBrand, persuasión sutil estilo Cialdini.
+- Léxico clave: Anécdota catalizadora, Puente narrativo, Epifanía, Lección clave, Llamada a la acción contextual, Resonancia emocional.
+- Usa marcos fundacionales de copy (AIDA, PASA y PASTOR) solo como estructura interna.
+
+### [PERSONA]
+Actúa como estratega de email marketing y storyteller experto en copy conversacional.
+Tono: empático, amable, curioso, conversacional y perspicaz.
+Audiencia: suscriptores con relación de confianza, que esperan valor y no venta agresiva.
+
+### [MISIÓN]
+Guiar de forma interactiva para crear emails de marketing.
+Antes de redactar el email final, debes recopilar estos datos:
+1) Audiencia objetivo.
+2) Producto a promover.
+3) Nombre para firma.
+4) Llamado a la acción (CTA).
+5) Ángulo (anécdota/situación/observación; puede incluir personajes de Disney/anime).
+NO debes generar ningún email final antes de recibir los 5 elementos.
+
+### [PRIMERA RESPUESTA OBLIGATORIA]
+Si aún no tienes los 5 datos, inicia con la PRIMERA pregunta del flujo operativo (no pidas todo junto).
+
+### [FLUJO OPERATIVO]
+Haz solo 1 pregunta a la vez y espera respuesta:
+1) AUDIENCIA:
+   "¿A quién le vas a escribir este email? Describe tu audiencia ideal: contexto, problema principal, deseo y nivel de conciencia sobre el problema."
+2) PRODUCTO:
+   "¿Qué producto o servicio vas a promover y qué transformación principal consigue la persona que lo compra?"
+3) NOMBRE:
+   "¿Con qué nombre quieres firmar el correo?"
+4) CTA:
+   "¿Qué acción concreta quieres que la audiencia realice al final del email? (responder, agendar llamada, comprar, visitar enlace, etc.)"
+5) ÁNGULO (OBLIGATORIO):
+   "¿Qué ángulo, anécdota o situación específica quieres usar en este correo? (Puedes apoyarte en referencias como Disney/anime si encaja)."
+
+### [RAZONAMIENTO PASO A PASO]
+1) Descubrimiento guiado (5 preguntas, una por vez; ángulo obligatorio).
+2) Identificación de dolor/deseo central y transformación del producto.
+3) Construir puente narrativo a partir del ángulo dado y redactar con enfoque conversacional.
+4) Usar AIDA, PASA o PASTOR internamente para ordenar el mensaje cuando haga falta.
+5) Adaptar lenguaje al nivel de conciencia de la audiencia y cerrar con CTA explícito.
+6) Usar el nombre de firma proporcionado en el cierre final del email.
 
-    responses = []
+### [RESTRICCIONES]
+- No uses clichés de marketing.
+- No fuerces la conexión historia-producto; pide más contexto si hace falta.
+- No te enfoques en características, precios o descuentos.
+- Nunca menciones al usuario qué fórmula interna usaste (AIDA, PASA o PASTOR).
 
-    for question in questions:
-        response = input(question + " ")
-        responses.append(response)
+### [FORMATO DE SALIDA FINAL - EXACTO]
+**Asunto:** [Texto del Asunto en negrita]
 
-    print("Thank you for your responses! Here is a draft of your email:")
-    draft_email(responses)
+---
 
+**Cuerpo del Email:**
 
-def draft_email(responses):
-    purpose, recipient, info, tone, attachments = responses
-    draft = f"Dear {recipient},\n\n"
+[Párrafo 1: La anécdota]
 
-    if tone == "formal":
-        draft += f"I am writing to you regarding {purpose}.\n"
-    elif tone == "casual":
-        draft += f"Hey {recipient}, just wanted to talk about {purpose}.\n"
-    else:
-        draft += f"Hello {recipient}, I hope this message finds you well. I wanted to discuss {purpose}.\n"
+[Párrafo 2: La transición hacia la lección]
 
-    draft += f"Here is the information you requested: {info}.\n"
+[Párrafo 3: La lección clave y cómo se conecta con un problema general]
+
+[Párrafo 4: Presentación del producto como la herramienta para aplicar la lección]
+
+[Párrafo 5: Llamada a la acción clara y directa]
+
+[Cierre personal]
+"""
+
+
+def is_greeting(text: str, is_first_user_message: bool) -> bool:
+    normalized = (text or "").lower().strip()
+    greetings = [
+        "hola",
+        "hey",
+        "saludos",
+        "buenos días",
+        "buenas tardes",
+        "buenas noches",
+        "hi",
+        "hello",
+    ]
+    is_simple = any(greeting in normalized for greeting in greetings) and len(normalized.split()) < 4
+    return is_simple and is_first_user_message
 
-    if attachments.lower() == "yes":
-        draft += "Please find the attachments included.\n"
-    else:
-        draft += "If you need any additional information, feel free to reach out.\n"
 
-    draft += "\nBest regards,\nYour Name"
+def get_enhanced_prompt(prompt: str, *, is_example: bool, is_first_user_message: bool) -> str:
+    if is_greeting(prompt, is_first_user_message):
+        return (
+            "Responde ÚNICAMENTE con esta frase, sin agregar nada más: "
+            "\"¡Perfecto! Empecemos por la primera: "
+            "¿Quién es tu audiencia ideal para este correo? "
+            "Descríbela con detalle (contexto, problema principal, deseo y nivel de conciencia).\""
+        )
 
-    print(draft)
+    if is_example:
+        return (
+            f"El usuario seleccionó esta pregunta del menú: '{prompt}'. "
+            "Respóndela de forma directa, útil y conversacional, con ejemplos concretos. "
+            "Después de responder, invita al usuario a iniciar el flujo de 5 preguntas "
+            "en este orden: audiencia, producto, nombre, CTA y ángulo."
+        )
 
-# Uncomment to run the email generation prompt
-# email_generation_prompt()
+    return prompt

frontend/package-lock.json

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+        "loose-envify": "^1.1.0"
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+      "dev": true,
+      "license": "ISC"
+    }
+  }
+}

frontend/src/App.tsx

@@ -1,32 +1,61 @@
-import React, { useState } from 'react';
+import React, { useEffect, useState } from 'react';
 import './App.css';
 import ChatWindow from './components/ChatWindow';
 import ChatHistory from './components/ChatHistory';
 
+export interface ChatSummary {
+  chat_id: string;
+  title: string;
+  updated_at: number;
+}
+
 function App() {
-  const [currentChatId, setCurrentChatId] = useState(Date.now().toString());
-  const [chats, setChats] = useState<Record<string, any[]>>({});
+  const [currentChatId, setCurrentChatId] = useState<string | null>(null);
+  const [chats, setChats] = useState<ChatSummary[]>([]);
+
+  const fetchChats = async () => {
+    const response = await fetch('/api/chats');
+    if (!response.ok) {
+      throw new Error('No se pudieron cargar los chats');
+    }
+    const data: ChatSummary[] = await response.json();
+    setChats(data);
+    if (!currentChatId && data.length > 0) {
+      setCurrentChatId(data[0].chat_id);
+    }
+  };
+
+  useEffect(() => {
+    fetchChats().catch(console.error);
+  }, []);
+
+  const handleNewChat = async () => {
+    const response = await fetch('/api/chats', {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({}),
+    });
+    if (!response.ok) {
+      throw new Error('No se pudo crear el chat');
+    }
+
+    const created: ChatSummary = await response.json();
+    await fetchChats();
+    setCurrentChatId(created.chat_id);
+  };
 
   return (
     <div className="app-container">
       <div className="sidebar">
-        <ChatHistory 
+        <ChatHistory
           chats={chats}
           currentChatId={currentChatId}
           onSelectChat={setCurrentChatId}
-          onNewChat={() => setCurrentChatId(Date.now().toString())}
+          onNewChat={handleNewChat}
         />
       </div>
       <div className="main-content">
-        <ChatWindow 
-          chatId={currentChatId}
-          onUpdateChat={(messages: any[]) => {
-            setChats(prev => ({
-              ...prev,
-              [currentChatId]: messages
-            }));
-          }}
-        />
+        <ChatWindow chatId={currentChatId} onRefreshChats={fetchChats} />
       </div>
     </div>
   );

frontend/src/components/ChatHistory.tsx

@@ -1,23 +1,28 @@
 import React from 'react';
+import type { ChatSummary } from '../App';
 
-function ChatHistory({ chats, currentChatId, onSelectChat, onNewChat }: any) {
+interface Props {
+  chats: ChatSummary[];
+  currentChatId: string | null;
+  onSelectChat: (chatId: string) => void;
+  onNewChat: () => void;
+}
+
+function ChatHistory({ chats, currentChatId, onSelectChat, onNewChat }: Props) {
   return (
     <div className="chat-history">
       <button className="new-chat-btn" onClick={onNewChat}>
         ＋ Nuevo chat
       </button>
       <div className="chat-list">
-        {Object.entries(chats).map(([chatId, messages]: any) => (
+        {chats.map((chat) => (
           <div
-            key={chatId}
-            className={`chat-item ${chatId === currentChatId ? 'active' : ''}`}
-            onClick={() => onSelectChat(chatId)}
+            key={chat.chat_id}
+            className={`chat-item ${chat.chat_id === currentChatId ? 'active' : ''}`}
+            onClick={() => onSelectChat(chat.chat_id)}
           >
-            {chatId === currentChatId ? '● ' : ''}
-            {messages && messages.length > 0 
-              ? messages[0].content.substring(0, 30) + '...'
-              : `Chat ${chatId}`
-            }
+            {chat.chat_id === currentChatId ? '● ' : ''}
+            {chat.title}
           </div>
         ))}
       </div>

frontend/src/components/ChatWindow.tsx

@@ -1,70 +1,118 @@
-import React, { useState, useEffect } from 'react';
+import React, { useEffect, useState } from 'react';
 
 interface Message {
-  id: string;
   role: 'user' | 'assistant' | 'error';
   content: string;
   avatar: string;
 }
 
-function ChatWindow({ chatId, onUpdateChat }: any) {
+interface ApiMessage {
+  role: 'user' | 'assistant';
+  content: string;
+}
+
+interface Props {
+  chatId: string | null;
+  onRefreshChats: () => Promise<void>;
+}
+
+const EXAMPLE_PROMPTS = [
+  'Ayúdame a definir una audiencia concreta para este correo: dolor principal, deseo y nivel de conciencia.',
+  'Convierte mi producto en una promesa clara de transformación sin listar características aburridas.',
+  'Dame 3 opciones de CTA claras para este email, con baja fricción y orientadas a una sola acción.',
+  'Propón 5 asuntos y 3 ganchos de apertura para aumentar aperturas y clics de este correo.',
+];
+
+function toUiMessage(msg: ApiMessage): Message {
+  return {
+    role: msg.role,
+    content: msg.content,
+    avatar: msg.role === 'user' ? '👤' : '🤖',
+  };
+}
+
+function ChatWindow({ chatId, onRefreshChats }: Props) {
   const [messages, setMessages] = useState<Message[]>([]);
   const [input, setInput] = useState('');
   const [loading, setLoading] = useState(false);
 
+  const loadMessages = async (targetChatId: string) => {
+    const response = await fetch(`/api/chats/${targetChatId}/messages`);
+    if (!response.ok) {
+      throw new Error('No se pudo cargar el historial');
+    }
+
+    const data: ApiMessage[] = await response.json();
+    setMessages(data.map(toUiMessage));
+  };
+
   useEffect(() => {
-    setMessages([]);
+    if (!chatId) {
+      setMessages([]);
+      return;
+    }
+
+    loadMessages(chatId).catch((error) => {
+      console.error(error);
+      setMessages([]);
+    });
   }, [chatId]);
 
-  const handleSendMessage = async () => {
-    if (!input.trim()) return;
+  const sendToCurrentChat = async (content: string, isExample = false) => {
+    if (!chatId) return;
 
     const userMessage: Message = {
-      id: Date.now().toString(),
       role: 'user',
-      content: input,
+      content,
       avatar: '👤',
     };
 
-    setMessages(prev => [...prev, userMessage]);
-    setInput('');
+    setMessages((prev) => [...prev, userMessage]);
     setLoading(true);
 
     try {
-      const response = await fetch('/api/chat/send', {
+      const response = await fetch(`/api/chats/${chatId}/messages`, {
         method: 'POST',
         headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({
-          chat_id: chatId,
-          content: input,
-        }),
+        body: JSON.stringify({ content, is_example: isExample }),
       });
 
-      if (!response.ok) throw new Error('Error sending message');
+      if (!response.ok) {
+        const payload = await response.json().catch(() => ({}));
+        throw new Error(payload.detail || 'Error enviando mensaje');
+      }
 
       const data = await response.json();
       const assistantMessage: Message = {
-        id: (Date.now() + 1).toString(),
         role: 'assistant',
         content: data.response,
         avatar: '🤖',
       };
 
-      setMessages(prev => [...prev, assistantMessage]);
-      onUpdateChat([...messages, userMessage, assistantMessage]);
+      setMessages((prev) => [...prev, assistantMessage]);
+      await onRefreshChats();
     } catch (error) {
-      console.error('Error:', error);
-      setMessages(prev => [...prev, {
-        id: Date.now().toString(),
-        role: 'error',
-        content: 'Error sending message',
-        avatar: '⚠️',
-      }]);
+      const msg = error instanceof Error ? error.message : 'Error enviando mensaje';
+      setMessages((prev) => [
+        ...prev,
+        {
+          role: 'error',
+          content: msg,
+          avatar: '⚠️',
+        },
+      ]);
     } finally {
       setLoading(false);
     }
   };
 
+  const handleSendMessage = async () => {
+    if (!input.trim()) return;
+    const content = input;
+    setInput('');
+    await sendToCurrentChat(content, false);
+  };
+
   return (
     <div className="chat-window">
       <div className="messages">
@@ -73,15 +121,23 @@ function ChatWindow({ chatId, onUpdateChat }: any) {
             <h1>Email Story Creator</h1>
             <p>✉️ Experto en emails narrativos que conectan historias con ventas</p>
             <div className="example-buttons">
-              <button className="example-btn">Definir audiencia 🎯</button>
-              <button className="example-btn">Propuesta de valor 💎</button>
-              <button className="example-btn">CTA que convierte 🚀</button>
-              <button className="example-btn">Asunto + gancho ✉️</button>
+              <button className="example-btn" onClick={() => sendToCurrentChat(EXAMPLE_PROMPTS[0], true)}>
+                Definir audiencia 🎯
+              </button>
+              <button className="example-btn" onClick={() => sendToCurrentChat(EXAMPLE_PROMPTS[1], true)}>
+                Propuesta de valor 💎
+              </button>
+              <button className="example-btn" onClick={() => sendToCurrentChat(EXAMPLE_PROMPTS[2], true)}>
+                CTA que convierte 🚀
+              </button>
+              <button className="example-btn" onClick={() => sendToCurrentChat(EXAMPLE_PROMPTS[3], true)}>
+                Asunto + gancho ✉️
+              </button>
             </div>
           </div>
         )}
-        {messages.map(msg => (
-          <div key={msg.id} className={`message ${msg.role}`}>
+        {messages.map((msg, idx) => (
+          <div key={`${msg.role}-${idx}-${msg.content.slice(0, 20)}`} className={`message ${msg.role}`}>
             <span className="avatar">{msg.avatar}</span>
             <div className="content">{msg.content}</div>
           </div>
@@ -94,11 +150,11 @@ function ChatWindow({ chatId, onUpdateChat }: any) {
           type="text"
           value={input}
           onChange={(e) => setInput(e.target.value)}
-          onKeyPress={(e) => e.key === 'Enter' && handleSendMessage()}
-          placeholder="Escribe aquí tus instrucciones"
-          disabled={loading}
+          onKeyDown={(e) => e.key === 'Enter' && handleSendMessage()}
+          placeholder={chatId ? 'Escribe aquí tus instrucciones' : 'Crea un chat con “+ Nuevo chat”'}
+          disabled={loading || !chatId}
         />
-        <button onClick={handleSendMessage} disabled={loading}>
+        <button onClick={handleSendMessage} disabled={loading || !chatId}>
           Enviar
         </button>
       </div>

frontend/tsconfig.json

@@ -1 +1,18 @@
-{\n  "compilerOptions": {\n    "target": "es5",\n    "module": "commonjs",\n    "strict": true,\n    "jsx": "react",\n    "moduleResolution": "node",\n    "esModuleInterop": true,\n    "skipLibCheck": true,\n    "forceConsistentCasingInFileNames": true\n  },\n  "include": [\n    "src/**/*"\n  ]\n}
+{
+  "compilerOptions": {
+    "target": "ES2020",
+    "useDefineForClassFields": true,
+    "lib": ["ES2020", "DOM", "DOM.Iterable"],
+    "module": "ESNext",
+    "skipLibCheck": true,
+    "moduleResolution": "Bundler",
+    "allowImportingTsExtensions": true,
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "noEmit": true,
+    "jsx": "react-jsx",
+    "strict": true
+  },
+  "include": ["src"],
+  "references": [{ "path": "./tsconfig.node.json" }]
+}

frontend/tsconfig.node.json

@@ -0,0 +1,10 @@
+{
+  "compilerOptions": {
+    "composite": true,
+    "skipLibCheck": true,
+    "module": "ESNext",
+    "moduleResolution": "Bundler",
+    "allowSyntheticDefaultImports": true
+  },
+  "include": ["vite.config.ts"]
+}