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endpoint: Deepseek
title: Translation App Project Outline
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title: Translation App Project Outline

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User Help me come up with a project outline for a (human) language translation app. It needs an API and a React v18 and shadcn-ui based frontend.

Translation Service API

Use docker compose.
Use Supabase.
Should be in modern, really high quality TypeScript.
Accept input markdown documents.
Use background jobs / task scheduler to translate documents.
Support many LLM backends, user configurable APIs and credentials.
Customizable target language per job, translate_job
Support a customizable system prompt per language.

Help me describe the schema and rest of services required.

Deepseek Here’s a detailed project outline for your human language translation app, including the Translation Service API and a React v18 + shadcn-ui frontend. The outline covers the schema, services, and architecture.

Project Outline

1. Translation Service API

The API will handle document translation using LLM backends, manage jobs, and store results in Supabase. It will be built with Docker Compose for containerization and written in high-quality TypeScript.

Key Features

Accept markdown documents as input.
Use background jobs/task scheduling for translation.
Support multiple LLM backends (e.g., OpenAI GPT, Anthropic Claude, Cohere, etc.).
Allow user-configurable API keys and credentials.
Customizable target language per job.
Customizable system prompt per language.

Schema

The schema will be stored in Supabase and will include the following tables:

translate_job
- id (UUID, primary key)
- user_id (UUID, foreign key to users table)
- input_document (text, markdown content)
- output_document (text, translated markdown content)
- source_language (text, e.g., "en")
- target_language (text, e.g., "es")
- status (text, e.g., "pending", "in_progress", "completed", "failed")
- created_at (timestamp)
- updated_at (timestamp)
- system_prompt (text, customizable prompt for the LLM)
- llm_backend (text, e.g., "openai", "anthropic", "cohere")
- llm_config (jsonb, stores API keys, model name, temperature, etc.)
users
- id (UUID, primary key)
- email (text, unique)
- password_hash (text, for authentication)
- created_at (timestamp)
- updated_at (timestamp)
llm_backend_config
- id (UUID, primary key)
- user_id (UUID, foreign key to users table)
- backend_name (text, e.g., "openai")
- api_key (text, encrypted)
- default_model (text, e.g., "gpt-4")
- created_at (timestamp)
- updated_at (timestamp)

Services

Job Management Service
- Handles creation, updating, and querying of translation jobs.
- Exposes REST endpoints:
  - POST /jobs (create a new job)
  - GET /jobs/:id (get job status and result)
  - GET /jobs (list all jobs for a user)
  - DELETE /jobs/:id (delete a job)
Translation Service
- Processes translation jobs in the background.
- Uses task scheduling (e.g., BullMQ or Celery) to handle queued jobs.
- Integrates with multiple LLM backends based on user configuration.
LLM Backend Integration Service
- Abstracts interactions with different LLM backends.
- Provides a unified interface for sending requests and receiving responses.
Authentication Service
- Handles user authentication and authorization.
- Uses Supabase Auth for user management.
File Storage Service
- Stores input and output documents (optional, if not using Supabase storage).

API Endpoints

Authentication
- POST /auth/register (register a new user)
- POST /auth/login (log in a user)
- POST /auth/logout (log out a user)
Translation Jobs
- POST /jobs (create a new translation job)
- GET /jobs/:id (get job details)
- GET /jobs (list all jobs for the authenticated user)
- DELETE /jobs/:id (delete a job)
LLM Backend Configuration
- POST /llm-config (add or update LLM backend config)
- GET /llm-config (list all LLM backend configs for the user)
- DELETE /llm-config/:id (delete an LLM backend config)

2. Frontend (React v18 + shadcn-ui)

The frontend will be a modern, responsive web app built with React v18 and styled using shadcn-ui components.

Key Features

User authentication (login, registration, logout).
Upload markdown documents for translation.
View and manage translation jobs.
Configure LLM backends and API keys.
Customize system prompts for each language.

Components

Header: Navigation bar with links to dashboard, create job, and settings.
Job Card: Displays job status, source/target languages, and actions (view, delete).
Markdown Viewer: Renders markdown content with syntax highlighting.
LLM Config Form: Form to add or update LLM backend configurations.

State Management

Use React Context or Zustand for global state management (e.g., user authentication, job list).

Styling

Use shadcn-ui for pre-built, customizable components.
Tailwind CSS for utility-first styling.

3. Docker Compose Setup

The app will be containerized using Docker Compose for easy development and deployment.

Services

API Service
- Node.js/TypeScript app for the Translation Service API.
- Exposes port 3000.
Frontend Service
- React v18 app for the frontend.
- Exposes port 5173 (Vite default).
Supabase
- Local Supabase instance for database and authentication.
- Exposes ports 5432 (PostgreSQL), 8000 (Studio).
Redis
- Used for task scheduling and background job processing.
Worker Service
- Handles background translation jobs.
- Connects to Redis for task scheduling.

docker-compose.yml

version: '3.8'
services:
  api:
    build: ./api
    ports:
      - "3000:3000"
    environment:
      - DATABASE_URL=postgres://postgres:postgres@supabase:5432/postgres
      - REDIS_URL=redis://redis:6379
    depends_on:
      - supabase
      - redis

  frontend:
    build: ./frontend
    ports:
      - "5173:5173"
    environment:
      - VITE_API_URL=http://localhost:3000

  supabase:
    image: supabase/postgres
    ports:
      - "5432:5432"
      - "8000:8000"
    environment:
      - POSTGRES_PASSWORD=postgres

  redis:
    image: redis:alpine
    ports:
      - "6379:6379"

  worker:
    build: ./worker
    environment:
      - DATABASE_URL=postgres://postgres:postgres@supabase:5432/postgres
      - REDIS_URL=redis://redis:6379
    depends_on:
      - supabase
      - redis

4. Development Workflow

API Development
- Use TypeScript with Express.js or Fastify.
- Write unit and integration tests with Jest or Vitest.
Frontend Development
- Use Vite for fast development.
- Write tests with React Testing Library.
Database Management
- Use Supabase Studio for schema management and migrations.
Deployment
- Deploy to a cloud provider (e.g., Vercel for frontend, Render for API).
- Use GitHub Actions or CircleCI for CI/CD.

This outline provides a solid foundation for your translation app. Let me know if you need further details or help with specific parts!

User Update the plan with a metrics table, cost in/out, tokens in/out, speed, etc. Also, change translate_job.target_language to a JSONB translate_job.target_languages. It will be an array of ISO languages. Add a translate_result table to track progress and results for each target_language and translate_job.id. Use supabase realtime https://supabase.com/docs/guides/realtime?queryGroups=language&language=js

Deepseek Here’s the updated project outline with the requested changes, including a metrics table, cost/token tracking, speed measurements, and the new translate_result table for tracking progress per target language. Additionally, I’ve integrated Supabase Realtime for real-time updates.

Updated Project Outline

1. Translation Service API

Schema Updates

translate_job
- id (UUID, primary key)
- user_id (UUID, foreign key to users table)
- input_document (text, markdown content)
- status (text, e.g., "pending", "in_progress", "completed", "failed")
- created_at (timestamp)
- updated_at (timestamp)
- system_prompt (text, customizable prompt for the LLM)
- llm_backend (text, e.g., "openai", "anthropic", "cohere")
- llm_config (jsonb, stores API keys, model name, temperature, etc.)
- target_languages (jsonb, array of ISO language codes, e.g., ["es", "fr", "de"])
translate_result
- id (UUID, primary key)
- job_id (UUID, foreign key to translate_job table)
- target_language (text, ISO language code, e.g., "es")
- output_document (text, translated markdown content)
- status (text, e.g., "pending", "in_progress", "completed", "failed")
- created_at (timestamp)
- updated_at (timestamp)
- metrics (jsonb, stores cost, tokens, speed, etc.)
metrics
- id (UUID, primary key)
- result_id (UUID, foreign key to translate_result table)
- tokens_in (integer, number of input tokens)
- tokens_out (integer, number of output tokens)
- cost_in (float, cost of input tokens in USD)
- cost_out (float, cost of output tokens in USD)
- speed (float, time taken in seconds)
- created_at (timestamp)
users (unchanged)
- id (UUID, primary key)
- email (text, unique)
- password_hash (text, for authentication)
- created_at (timestamp)
- updated_at (timestamp)
llm_backend_config (unchanged)
- id (UUID, primary key)
- user_id (UUID, foreign key to users table)
- backend_name (text, e.g., "openai")
- api_key (text, encrypted)
- default_model (text, e.g., "gpt-4")
- created_at (timestamp)
- updated_at (timestamp)

2. Metrics Tracking

The metrics table will track cost, tokens, and speed for each translation result. This data will be populated by the Translation Service after each job is processed.

Metrics Fields

tokens_in: Number of tokens in the input document.
tokens_out: Number of tokens in the output document.
cost_in: Cost of input tokens (based on LLM pricing).
cost_out: Cost of output tokens (based on LLM pricing).
speed: Time taken to process the translation (in seconds).

Example Calculation

For OpenAI GPT-4:

Input tokens: $0.03 per 1,000 tokens.
Output tokens: $0.06 per 1,000 tokens.
Speed: Time from job start to completion.

3. Supabase Realtime Integration

Supabase Realtime will be used to provide real-time updates to the frontend about job status and progress.

Steps to Enable Realtime

Enable Realtime for the translate_job and translate_result tables in Supabase.

alter publication supabase_realtime add table translate_job;
alter publication supabase_realtime add table translate_result;

Subscribe to changes in the frontend:

import { createClient } from '@supabase/supabase-js';

const supabase = createClient(SUPABASE_URL, SUPABASE_ANON_KEY);

const channel = supabase
  .channel('translation-jobs')
  .on('postgres_changes', {
    event: '*',
    schema: 'public',
    table: 'translate_job',
  }, (payload) => {
    console.log('Job update:', payload);
  })
  .subscribe();

const resultChannel = supabase
  .channel('translation-results')
  .on('postgres_changes', {
    event: '*',
    schema: 'public',
    table: 'translate_result',
  }, (payload) => {
    console.log('Result update:', payload);
  })
  .subscribe();

Update the frontend UI in real-time as job and result statuses change.

4. Frontend Updates

The frontend will now display real-time updates for translation jobs and results. Each job will show progress for all target languages.

New Components

Job Progress Card
- Displays the overall job status.
- Shows progress for each target language (e.g., "es: completed", "fr: in progress").
Result Metrics Card
- Displays metrics (tokens, cost, speed) for each completed translation.
Real-Time Updates
- Use Supabase Realtime to update the UI as jobs and results change.

5. Docker Compose Setup

No changes to the Docker Compose setup are required for these updates.

6. Example Workflow

User Submits a Job
- Uploads a markdown document.
- Selects target languages (e.g., ["es", "fr", "de"]).
- Submits the job via the API.
API Processes the Job
- Creates a translate_job record.
- For each target language, creates a translate_result record with status "pending".
- Uses background workers to process each target language.
Real-Time Updates
- The frontend subscribes to Supabase Realtime for updates.
- As each target language is processed, the translate_result status is updated.
Metrics Tracking
- After each translation, the metrics table is populated with cost, tokens, and speed.
User Views Results
- The frontend displays the translated documents and metrics for each target language.

7. Example Metrics Table

Field	Description	Example Value
`tokens_in`	Number of input tokens	1,200
`tokens_out`	Number of output tokens	1,500
`cost_in`	Cost of input tokens (USD)	0.036
`cost_out`	Cost of output tokens (USD)	0.090
`speed`	Time taken to process (seconds)	12.5

8. Cost and Token Tracking

Input Tokens: Calculated based on the length of the input document.
Output Tokens: Calculated based on the length of the translated document.
Cost: Calculated using the LLM's pricing model (e.g., OpenAI GPT-4 pricing).
Speed: Measured from job start to completion.

This updated plan includes all requested features and ensures real-time updates, detailed metrics tracking, and support for multiple target languages. Let me know if you need further refinements!

User Give me a prompt or multiple prompts to build this entire project, for a detail oriented AI engineer.

Deepseek Here’s a detailed prompt (or set of prompts) tailored for a detail-oriented AI engineer to build the entire project. These prompts are structured to ensure clarity, completeness, and alignment with the project goals.

Prompt 1: Project Overview and Goals

Objective: Provide a high-level overview of the project and its goals.

Prompt:

You are tasked with building a **human language translation app** that includes a **Translation Service API** and a **React v18 + shadcn-ui frontend**. The app should allow users to upload markdown documents, translate them into multiple target languages using configurable LLM backends, and track progress and metrics in real-time.

Key features:
1. **Translation Service API**:
   - Built with Docker Compose and Supabase.
   - Written in high-quality TypeScript.
   - Accepts markdown documents as input.
   - Uses background jobs/task scheduling for translation.
   - Supports multiple LLM backends (e.g., OpenAI GPT, Anthropic Claude, Cohere).
   - Allows user-configurable API keys and credentials.
   - Customizable target languages per job (stored as JSONB array).
   - Customizable system prompts per language.

2. **Frontend**:
   - Built with React v18 and shadcn-ui.
   - Real-time updates using Supabase Realtime.
   - Displays job progress, translated documents, and metrics (cost, tokens, speed).

3. **Metrics and Cost Tracking**:
   - Track tokens in/out, cost in/out, and speed for each translation.
   - Store metrics in a dedicated `metrics` table.

4. **Database Schema**:
   - Use Supabase for PostgreSQL and authentication.
   - Include tables for `translate_job`, `translate_result`, `metrics`, `users`, and `llm_backend_config`.

Deliverables:
- A fully functional Translation Service API.
- A modern, responsive frontend.
- Docker Compose setup for local development.
- Detailed documentation for setup, usage, and deployment.

Prompt 2: Database Schema and Supabase Setup

Objective: Define the database schema and Supabase configuration.

Prompt:

Design and implement the database schema for the translation app using Supabase. The schema should include the following tables:

1. **`translate_job`**:
   - `id` (UUID, primary key)
   - `user_id` (UUID, foreign key to `users` table)
   - `input_document` (text, markdown content)
   - `status` (text, e.g., "pending", "in_progress", "completed", "failed")
   - `created_at` (timestamp)
   - `updated_at` (timestamp)
   - `system_prompt` (text, customizable prompt for the LLM)
   - `llm_backend` (text, e.g., "openai", "anthropic", "cohere")
   - `llm_config` (jsonb, stores API keys, model name, temperature, etc.)
   - `target_languages` (jsonb, array of ISO language codes, e.g., `["es", "fr", "de"]`)

2. **`translate_result`**:
   - `id` (UUID, primary key)
   - `job_id` (UUID, foreign key to `translate_job` table)
   - `target_language` (text, ISO language code, e.g., "es")
   - `output_document` (text, translated markdown content)
   - `status` (text, e.g., "pending", "in_progress", "completed", "failed")
   - `created_at` (timestamp)
   - `updated_at` (timestamp)
   - `metrics` (jsonb, stores cost, tokens, speed, etc.)

3. **`metrics`**:
   - `id` (UUID, primary key)
   - `result_id` (UUID, foreign key to `translate_result` table)
   - `tokens_in` (integer, number of input tokens)
   - `tokens_out` (integer, number of output tokens)
   - `cost_in` (float, cost of input tokens in USD)
   - `cost_out` (float, cost of output tokens in USD)
   - `speed` (float, time taken in seconds)
   - `created_at` (timestamp)

4. **`users`**:
   - `id` (UUID, primary key)
   - `email` (text, unique)
   - `password_hash` (text, for authentication)
   - `created_at` (timestamp)
   - `updated_at` (timestamp)

5. **`llm_backend_config`**:
   - `id` (UUID, primary key)
   - `user_id` (UUID, foreign key to `users` table)
   - `backend_name` (text, e.g., "openai")
   - `api_key` (text, encrypted)
   - `default_model` (text, e.g., "gpt-4")
   - `created_at` (timestamp)
   - `updated_at` (timestamp)

Tasks:
1. Write SQL scripts to create these tables in Supabase.
2. Enable Supabase Realtime for the `translate_job` and `translate_result` tables.
3. Document the schema and relationships between tables.

Prompt 3: Translation Service API

Objective: Build the Translation Service API.

Prompt:

Develop the **Translation Service API** using TypeScript, Docker Compose, and Supabase. The API should include the following services:

1. **Job Management Service**:
   - REST endpoints:
     - `POST /jobs` (create a new job)
     - `GET /jobs/:id` (get job status and result)
     - `GET /jobs` (list all jobs for a user)
     - `DELETE /jobs/:id` (delete a job)

2. **Translation Service**:
   - Processes translation jobs in the background using task scheduling (e.g., BullMQ).
   - Integrates with multiple LLM backends (e.g., OpenAI, Anthropic, Cohere).
   - Updates the `translate_result` table with progress and metrics.

3. **LLM Backend Integration Service**:
   - Abstracts interactions with different LLM backends.
   - Provides a unified interface for sending requests and receiving responses.

4. **Authentication Service**:
   - Uses Supabase Auth for user authentication and authorization.

5. **Metrics Tracking**:
   - Populates the `metrics` table with tokens, cost, and speed for each translation.

Tasks:
1. Write TypeScript code for the API services.
2. Set up Docker Compose for local development.
3. Write unit and integration tests for the API.
4. Document the API endpoints and usage.

Prompt 4: Frontend Development

Objective: Build the React v18 + shadcn-ui frontend.

Prompt:

Develop the frontend for the translation app using **React v18** and **shadcn-ui**. The frontend should include the following features:

1. **Pages**:
   - Login / Register
   - Dashboard (list of translation jobs)
   - Create Job (upload markdown, select target languages, customize system prompt)
   - Job Details (view job status, translated documents, and metrics)
   - LLM Backend Configuration (add/update API keys and models)
   - User Settings (update profile and password)

2. **Components**:
   - Header (navigation bar)
   - Job Card (displays job status and actions)
   - Markdown Viewer (renders markdown content)
   - LLM Config Form (add/update LLM backend configurations)

3. **Real-Time Updates**:
   - Use Supabase Realtime to subscribe to changes in `translate_job` and `translate_result`.
   - Update the UI in real-time as jobs and results change.

4. **Styling**:
   - Use shadcn-ui for pre-built, customizable components.
   - Use Tailwind CSS for utility-first styling.

Tasks:
1. Write React components and pages.
2. Integrate Supabase Realtime for real-time updates.
3. Write tests for the frontend.
4. Document the frontend setup and usage.

Prompt 5: Metrics and Cost Tracking

Objective: Implement metrics and cost tracking.

Prompt:

Implement metrics and cost tracking for the translation app. The system should:
1. Track **tokens in/out**, **cost in/out**, and **speed** for each translation.
2. Store metrics in the `metrics` table.
3. Calculate costs based on LLM pricing (e.g., OpenAI GPT-4 pricing).

Tasks:
1. Write code to calculate tokens, cost, and speed for each translation.
2. Populate the `metrics` table with this data.
3. Display metrics in the frontend (e.g., in the Job Details page).
4. Document the metrics calculation and storage process.

Prompt 6: Deployment and Documentation

Objective: Deploy the app and provide documentation.

Prompt:

Deploy the translation app to a cloud provider (e.g., Vercel for frontend, Render for API). Provide detailed documentation for:
1. **Local Development**:
   - Docker Compose setup.
   - Supabase configuration.
2. **Deployment**:
   - Steps to deploy the API and frontend.
   - Environment variables and configuration.
3. **Usage**:
   - How to use the API and frontend.
   - Example workflows.
4. **Testing**:
   - How to run unit and integration tests.

These prompts provide a clear, step-by-step guide for a detail-oriented AI engineer to build the entire project. Let me know if you need further refinements!

User Provide instructions for Cline Code.

Deepseek Here’s a detailed set of instructions for Claude Code (or any AI engineer) to build the entire project. These instructions are structured to ensure clarity and completeness, covering all aspects of the project from setup to deployment.

Instructions for Building the Translation App

1. Project Setup

Clone the Repository:

git clone https://github.com/your-username/translation-app.git
cd translation-app

Set Up Docker Compose:

Create a docker-compose.yml file with the following services:

version: '3.8'
services:
  api:
    build: ./api
    ports:
      - "3000:3000"
    environment:
      - DATABASE_URL=postgres://postgres:postgres@supabase:5432/postgres
      - REDIS_URL=redis://redis:6379
    depends_on:
      - supabase
      - redis

  frontend:
    build: ./frontend
    ports:
      - "5173:5173"
    environment:
      - VITE_API_URL=http://localhost:3000

  supabase:
    image: supabase/postgres
    ports:
      - "5432:5432"
      - "8000:8000"
    environment:
      - POSTGRES_PASSWORD=postgres

  redis:
    image: redis:alpine
    ports:
      - "6379:6379"

  worker:
    build: ./worker
    environment:
      - DATABASE_URL=postgres://postgres:postgres@supabase:5432/postgres
      - REDIS_URL=redis://redis:6379
    depends_on:
      - supabase
      - redis

Set Up Supabase:

Create a Supabase project at supabase.com.

Enable Realtime for the translate_job and translate_result tables:

alter publication supabase_realtime add table translate_job;
alter publication supabase_realtime add table translate_result;

Set Up Environment Variables:

Create a .env file in the api directory:

DATABASE_URL=postgres://postgres:postgres@supabase:5432/postgres
REDIS_URL=redis://redis:6379
SUPABASE_URL=your-supabase-url
SUPABASE_ANON_KEY=your-supabase-anon-key

2. Database Schema

Create Tables:

Run the following SQL scripts in Supabase SQL Editor:

-- Users table
create table users (
  id uuid primary key default uuid_generate_v4(),
  email text unique not null,
  password_hash text not null,
  created_at timestamp default now(),
  updated_at timestamp default now()
);

-- LLM Backend Config table
create table llm_backend_config (
  id uuid primary key default uuid_generate_v4(),
  user_id uuid references users(id),
  backend_name text not null,
  api_key text not null,
  default_model text not null,
  created_at timestamp default now(),
  updated_at timestamp default now()
);

-- Translate Job table
create table translate_job (
  id uuid primary key default uuid_generate_v4(),
  user_id uuid references users(id),
  input_document text not null,
  status text not null default 'pending',
  created_at timestamp default now(),
  updated_at timestamp default now(),
  system_prompt text,
  llm_backend text not null,
  llm_config jsonb,
  target_languages jsonb not null
);

-- Translate Result table
create table translate_result (
  id uuid primary key default uuid_generate_v4(),
  job_id uuid references translate_job(id),
  target_language text not null,
  output_document text,
  status text not null default 'pending',
  created_at timestamp default now(),
  updated_at timestamp default now(),
  metrics jsonb
);

-- Metrics table
create table metrics (
  id uuid primary key default uuid_generate_v4(),
  result_id uuid references translate_result(id),
  tokens_in integer not null,
  tokens_out integer not null,
  cost_in float not null,
  cost_out float not null,
  speed float not null,
  created_at timestamp default now()
);

Enable Realtime:
- Enable Realtime for the translate_job and translate_result tables as shown earlier.

3. Translation Service API

Set Up the API:

Create an api directory and initialize a Node.js project:

mkdir api
cd api
npm init -y
npm install express typescript ts-node @types/node supabase redis bullmq

Write API Code:

Create an index.ts file:

import express from 'express';
import { createClient } from '@supabase/supabase-js';
import { Queue } from 'bullmq';

const app = express();
const supabase = createClient(process.env.SUPABASE_URL, process.env.SUPABASE_ANON_KEY);
const redisConnection = { connection: { host: 'redis', port: 6379 } };
const translationQueue = new Queue('translation', redisConnection);

app.use(express.json());

// Job creation endpoint
app.post('/jobs', async (req, res) => {
  const { input_document, target_languages, system_prompt, llm_backend, llm_config } = req.body;
  const job = await supabase.from('translate_job').insert({
    input_document,
    target_languages,
    system_prompt,
    llm_backend,
    llm_config,
    status: 'pending'
  }).single();
  res.json(job);
});

// Job status endpoint
app.get('/jobs/:id', async (req, res) => {
  const { id } = req.params;
  const job = await supabase.from('translate_job').select('*').eq('id', id).single();
  res.json(job);
});

app.listen(3000, () => console.log('API running on port 3000'));

Set Up Background Workers:

Create a worker.ts file:

import { Worker } from 'bullmq';
import { createClient } from '@supabase/supabase-js';

const supabase = createClient(process.env.SUPABASE_URL, process.env.SUPABASE_ANON_KEY);
const redisConnection = { connection: { host: 'redis', port: 6379 } };

const worker = new Worker('translation', async (job) => {
  const { input_document, target_languages, system_prompt, llm_backend, llm_config } = job.data;
  // Implement translation logic here
}, redisConnection);

worker.on('completed', (job) => {
  console.log(`Job ${job.id} completed`);
});

4. Frontend Development

Set Up the Frontend:

Create a frontend directory and initialize a React project:

mkdir frontend
cd frontend
npm create vite@latest . -- --template react-ts
npm install @supabase/supabase-js shadcn-ui

Write React Components:

Create a JobCard.tsx component:

import { useEffect, useState } from 'react';
import { createClient } from '@supabase/supabase-js';

const supabase = createClient(process.env.VITE_SUPABASE_URL, process.env.VITE_SUPABASE_ANON_KEY);

export default function JobCard({ jobId }: { jobId: string }) {
  const [job, setJob] = useState(null);

  useEffect(() => {
    const channel = supabase
      .channel('translation-jobs')
      .on('postgres_changes', {
        event: '*',
        schema: 'public',
        table: 'translate_job',
        filter: `id=eq.${jobId}`
      }, (payload) => {
        setJob(payload.new);
      })
      .subscribe();

    return () => {
      channel.unsubscribe();
    };
  }, [jobId]);

  return (
    <div>
      <h2>Job Status: {job?.status}</h2>
      <p>Input Document: {job?.input_document}</p>
    </div>
  );
}

5. Deployment

Deploy the API:
- Use a service like Render or Heroku to deploy the API.
Deploy the Frontend:
- Use Vercel to deploy the React frontend.
Set Up CI/CD:
- Use GitHub Actions or CircleCI for continuous integration and deployment.

These instructions provide a step-by-step guide for building the entire project. Let me know if you need further assistance!

User Can you give me instructions for Cursor?

Deepseek Certainly! Below are detailed instructions for using Cursor, an AI-powered code editor, to build the Translation App project. These instructions assume you’re familiar with Cursor and its features, such as AI autocomplete, code generation, and debugging.

Instructions for Building the Translation App with Cursor

1. Set Up the Project

Install Cursor:
- Download and install Cursor from cursor.sh.
- Open Cursor and log in to your account.
Create a New Project:
- In Cursor, click File > New Project.
- Name the project translation-app and choose a directory.

Set Up Docker Compose:

Create a docker-compose.yml file in the root directory.

Use Cursor’s AI to generate the file by typing:

/create docker-compose.yml for a translation app with Supabase, Redis, and a Node.js API

Paste the following content:

version: '3.8'
services:
  api:
    build: ./api
    ports:
      - "3000:3000"
    environment:
      - DATABASE_URL=postgres://postgres:postgres@supabase:5432/postgres
      - REDIS_URL=redis://redis:6379
    depends_on:
      - supabase
      - redis

  frontend:
    build: ./frontend
    ports:
      - "5173:5173"
    environment:
      - VITE_API_URL=http://localhost:3000

  supabase:
    image: supabase/postgres
    ports:
      - "5432:5432"
      - "8000:8000"
    environment:
      - POSTGRES_PASSWORD=postgres

  redis:
    image: redis:alpine
    ports:
      - "6379:6379"

  worker:
    build: ./worker
    environment:
      - DATABASE_URL=postgres://postgres:postgres@supabase:5432/postgres
      - REDIS_URL=redis://redis:6379
    depends_on:
      - supabase
      - redis

Set Up Supabase:

Create a Supabase project at supabase.com.

Enable Realtime for the translate_job and translate_result tables:

alter publication supabase_realtime add table translate_job;
alter publication supabase_realtime add table translate_result;

Set Up Environment Variables:

Create a .env file in the api directory:

DATABASE_URL=postgres://postgres:postgres@supabase:5432/postgres
REDIS_URL=redis://redis:6379
SUPABASE_URL=your-supabase-url
SUPABASE_ANON_KEY=your-supabase-anon-key

2. Database Schema

Create Tables:

Use Cursor’s AI to generate SQL scripts:

/generate SQL script for a translation app with Supabase

Paste the following SQL into Supabase’s SQL Editor:

-- Users table
create table users (
  id uuid primary key default uuid_generate_v4(),
  email text unique not null,
  password_hash text not null,
  created_at timestamp default now(),
  updated_at timestamp default now()
);

-- LLM Backend Config table
create table llm_backend_config (
  id uuid primary key default uuid_generate_v4(),
  user_id uuid references users(id),
  backend_name text not null,
  api_key text not null,
  default_model text not null,
  created_at timestamp default now(),
  updated_at timestamp default now()
);

-- Translate Job table
create table translate_job (
  id uuid primary key default uuid_generate_v4(),
  user_id uuid references users(id),
  input_document text not null,
  status text not null default 'pending',
  created_at timestamp default now(),
  updated_at timestamp default now(),
  system_prompt text,
  llm_backend text not null,
  llm_config jsonb,
  target_languages jsonb not null
);

-- Translate Result table
create table translate_result (
  id uuid primary key default uuid_generate_v4(),
  job_id uuid references translate_job(id),
  target_language text not null,
  output_document text,
  status text not null default 'pending',
  created_at timestamp default now(),
  updated_at timestamp default now(),
  metrics jsonb
);

-- Metrics table
create table metrics (
  id uuid primary key default uuid_generate_v4(),
  result_id uuid references translate_result(id),
  tokens_in integer not null,
  tokens_out integer not null,
  cost_in float not null,
  cost_out float not null,
  speed float not null,
  created_at timestamp default now()
);

Enable Realtime:
- Enable Realtime for the translate_job and translate_result tables as shown earlier.

3. Translation Service API

Set Up the API:

Use Cursor to create an api directory and initialize a Node.js project:
```
/create Node.js API for a translation app
```

Paste the following code into api/index.ts:

import express from 'express';
import { createClient } from '@supabase/supabase-js';
import { Queue } from 'bullmq';

const app = express();
const supabase = createClient(process.env.SUPABASE_URL, process.env.SUPABASE_ANON_KEY);
const redisConnection = { connection: { host: 'redis', port: 6379 } };
const translationQueue = new Queue('translation', redisConnection);

app.use(express.json());

// Job creation endpoint
app.post('/jobs', async (req, res) => {
  const { input_document, target_languages, system_prompt, llm_backend, llm_config } = req.body;
  const job = await supabase.from('translate_job').insert({
    input_document,
    target_languages,
    system_prompt,
    llm_backend,
    llm_config,
    status: 'pending'
  }).single();
  res.json(job);
});

// Job status endpoint
app.get('/jobs/:id', async (req, res) => {
  const { id } = req.params;
  const job = await supabase.from('translate_job').select('*').eq('id', id).single();
  res.json(job);
});

app.listen(3000, () => console.log('API running on port 3000'));

Set Up Background Workers:

Use Cursor to create a worker.ts file:

/create background worker for a translation app

Paste the following code:

import { Worker } from 'bullmq';
import { createClient } from '@supabase/supabase-js';

const supabase = createClient(process.env.SUPABASE_URL, process.env.SUPABASE_ANON_KEY);
const redisConnection = { connection: { host: 'redis', port: 6379 } };

const worker = new Worker('translation', async (job) => {
  const { input_document, target_languages, system_prompt, llm_backend, llm_config } = job.data;
  // Implement translation logic here
}, redisConnection);

worker.on('completed', (job) => {
  console.log(`Job ${job.id} completed`);
});

4. Frontend Development

Set Up the Frontend:

Use Cursor to create a frontend directory and initialize a React project:
```
/create React frontend for a translation app
```

Paste the following code into frontend/src/JobCard.tsx:

import { useEffect, useState } from 'react';
import { createClient } from '@supabase/supabase-js';

const supabase = createClient(process.env.VITE_SUPABASE_URL, process.env.VITE_SUPABASE_ANON_KEY);

export default function JobCard({ jobId }: { jobId: string }) {
  const [job, setJob] = useState(null);

  useEffect(() => {
    const channel = supabase
      .channel('translation-jobs')
      .on('postgres_changes', {
        event: '*',
        schema: 'public',
        table: 'translate_job',
        filter: `id=eq.${jobId}`
      }, (payload) => {
        setJob(payload.new);
      })
      .subscribe();

    return () => {
      channel.unsubscribe();
    };
  }, [jobId]);

  return (
    <div>
      <h2>Job Status: {job?.status}</h2>
      <p>Input Document: {job?.input_document}</p>
    </div>
  );
}

5. Deployment

Deploy the API:
- Use a service like Render or Heroku to deploy the API.
Deploy the Frontend:
- Use Vercel to deploy the React frontend.
Set Up CI/CD:
- Use GitHub Actions or CircleCI for continuous integration and deployment.

These instructions leverage Cursor’s AI capabilities to streamline development. Let me know if you need further assistance!

justsml/Translation_App Project Outline.md

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Translation Service API

Project Outline

1. Translation Service API

Key Features

Schema

Services

API Endpoints

2. Frontend (React v18 + shadcn-ui)

Key Features

Pages

Components

State Management

Styling

3. Docker Compose Setup

Services

docker-compose.yml

4. Development Workflow

Updated Project Outline

1. Translation Service API

Schema Updates

2. Metrics Tracking

Metrics Fields

Example Calculation

3. Supabase Realtime Integration

Steps to Enable Realtime

4. Frontend Updates

New Components

5. Docker Compose Setup

6. Example Workflow

7. Example Metrics Table

8. Cost and Token Tracking

Prompt 1: Project Overview and Goals

Prompt 2: Database Schema and Supabase Setup

Prompt 3: Translation Service API

Prompt 4: Frontend Development

Prompt 5: Metrics and Cost Tracking

Prompt 6: Deployment and Documentation

Instructions for Building the Translation App

1. Project Setup

2. Database Schema

3. Translation Service API

4. Frontend Development

5. Deployment

Instructions for Building the Translation App with Cursor

1. Set Up the Project

2. Database Schema

3. Translation Service API

4. Frontend Development

5. Deployment