Search

import { Hono } from "npm:hono@4.4.12";
// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai";
import type { Context } from "npm:hono@4.4.12";
import { streamText } from "npm:hono/streaming";
  try {
    const openai = new OpenAI();
    const agentStream = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: [{ role: "system", content: systemPromptWithContext }, { role: "user", content: "Proceed with the task." }],
  const { refined_prompt, company_context } = await c.req.json<GenerateCriteriaBody>();
  try {
    const openai = new OpenAI();
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [
  const { refined_prompt, criteria, raw_output, language } = await c.req.json<EvaluateBody>();
  try {
    const openai = new OpenAI();
    const stream = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: [
  const { refined_prompt, raw_output, evaluation, language } = await c.req.json<RefineBody>();
  try {
    const openai = new OpenAI();
    const stream = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: [

// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
// --- TYPE DEFINITIONS ---
async function processTestCase(
  testCase: TestCase,
  openai: OpenAI,
): Promise<TestResult> {
  // Phase 1: Solver
  const solverCompletion = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [{ role: "system", content: SOLVER_PROMPT }, {
  // Phase 2: Verifier
  const verifierCompletion = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [
    reasoning: solverOutput.chain_of_thought,
  };
  const evaluatorCompletion = await openai.chat.completions.create({
    model: "gpt-4o-2024-05-13",
    messages: [
        throw new Error("Invalid or empty test cases provided.");
      }
      const openai = new OpenAI();
      const results = await Promise.all(
        testCases.map((testCase) => processTestCase(testCase, openai)),
      );
      return new Response(JSON.stringify(results), {

import {OpenAI} from "https://esm.town/v/std/openai"
import {getQuizTeams} from "./database.ts"
  }
  // Check if OpenAI API key is available
  const apiKey = Deno.env.get("OPENAI_API_KEY")
  if (!apiKey) {
    console.log("OpenAI API key not available, using fallback suggestions")
    return {
      suggestions: getFallbackSuggestions(language),
  try {
    const openai = new OpenAI()
    const completion = await openai.chat.completions.create({
      model: "gpt-5-nano",
      messages: [
    const response = completion.choices[0]?.message?.content || ""
    console.log(`OpenAI response for ${language}:`, response)
    // Parse response into array of suggestions
      }
    } else {
      console.log("OpenAI returned insufficient suggestions, using fallback")
      return {
        suggestions: getFallbackSuggestions(language),
  } catch (error) {
    console.error("OpenAI API error:", error)
    return {
      suggestions: getFallbackSuggestions(language),

## Usage
The application generates a single workout using OpenAI while avoiding the last 2 generated workouts to ensure variety. The three workout components are displayed horizontally for easy viewing on one screen.

import { Hono } from "https://esm.sh/hono@3.11.7";
import { OpenAI } from "https://esm.town/v/std/openai";
import { readFile, serveFile } from "https://esm.town/v/std/utils@85-main/index.ts";
import { blob } from "https://esm.town/v/std/blob";
app.onError((err) => Promise.reject(err));
const openai = new OpenAI();
interface WorkoutComponent {
  try {
    const completion = await openai.chat.completions.create({
      messages: [{ role: "user", content: prompt }],
      model: "gpt-4o-mini",
    const content = completion.choices[0]?.message?.content;
    if (!content) {
      throw new Error("No content received from OpenAI");
    }

Note: When changing a SQLite table's schema, change the table's name (e.g., add _2 or _3) to create a fresh table.
### OpenAI
```ts
import { OpenAI } from "https://esm.town/v/std/openai";
const openai = new OpenAI();
const completion = await openai.chat.completions.create({
  messages: [
    { role: "user", content: "Say hello in a creative way" },

#### Issues in Epic 3:
##### Issue #3.1: OpenAI Integration Service
**Labels**: `backend`, `ai`, `medium-priority`  
**Story Points**: 5  
**Dependencies**: None (can be done in parallel)
Integrate OpenAI API for generating culturally appropriate team name suggestions.
**Acceptance Criteria:**
- [ ] OpenAI API integration with proper error handling
- [ ] Multi-language prompt engineering (Hindi, English, Gujarati)
- [ ] Rate limiting to prevent API abuse
**Technical Tasks:**
- Implement OpenAI API client
- Create language-specific prompts
- Add rate limiting middleware
**Labels**: `frontend`, `ui`, `medium-priority`  
**Story Points**: 3  
**Dependencies**: Issue #3.1 (OpenAI Integration), Issue #1.4 (Registration Form)
Add team name suggestion interface to registration form.
    F --> G[3.2 Team Suggestions UI]
    
    H[3.1 OpenAI Integration] --> G
    
    F --> I[4.1 Enhanced UX]
- **Mitigation**: Implement proper connection handling and test early
**Issue #3.1 - OpenAI Integration**: 🟡 **MEDIUM RISK**  
- **Risk**: External API dependency and rate limits
- **Impact**: AI features may not work consistently
### External Dependencies
- **OpenAI API Access**: Required for Issue #3.1
- **Admin Key Environment Variable**: Required for admin features
- **Val Town Platform**: All issues depend on platform constraints

└─────────────────┘    └──────────────────┘    └─────────────────┘
│                      │                      │
├─ Quiz Form           ├─ Quiz API Routes     ├─ OpenAI API
├─ Team Management     ├─ Database Layer     ├─ Val Town Runtime
├─ Admin Interface     ├─ Validation         └─ SQLite
```
## OpenAI Integration
### API Configuration
```typescript
import { OpenAI } from "https://esm.town/v/std/openai";
export async function generateTeamNameSuggestions(
  userContext?: string
): Promise<string[]> {
  const openai = new OpenAI();
  
  const prompts = {
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [
      
  } catch (error) {
    console.error("OpenAI API error:", error);
    return getFallbackSuggestions(language);
  }
interface QuizEnvironment {
  ADMIN_KEY: string;          // Admin access key
  OPENAI_API_KEY?: string;    // OpenAI API key (optional)
  QUIZ_ENABLED?: string;      // Feature flag
  DEBUG_MODE?: string;        // Debug logging
  const checks = {
    database: await checkDatabase(),
    openai: await checkOpenAI(),
    quiz_system: 'up' as const
  };

| Requirement ID | Description | Priority |
|---|---|---|
| QR-007 | AI-powered team name suggestions via OpenAI | Should Have |
| QR-008 | Duplicate team name prevention | Should Have |
| QR-009 | Mobile-responsive design | Must Have |
### External Dependencies
- **OpenAI API**: For team name generation
- **Val Town Platform**: Runtime and hosting constraints
- **SQLite**: Database engine limitations
### High-Risk Items
1. **OpenAI API Dependency**
   - Risk: Service unavailability affects team name suggestions
   - Mitigation: Graceful fallback, feature remains optional
### External Dependencies
- OpenAI API for team name generation
- Internet connectivity for users (mobile data)
- Modern web browser support (ES6+)

// @ts-nocheck
import { Hono } from "npm:hono@4.4.12";
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
import { blob } from "https://esm.town/v/std/blob?v=11";
const app = new Hono();
const openai = new OpenAI();
async function getSim(run_id) {
    messages.push({ role: "user", content: `Current State: ${JSON.stringify(status)}. Formulate a plan and use 'execute_command_sequence'.` });
    
    const completion = await openai.chat.completions.create({ model: "gpt-4o", messages: messages, tools: getToolSpec(), tool_choice: "auto" });
    const responseMessage = completion.choices[0].message;
    messages.pop();