Moodish is a mood-based food planning app for Swiggy-style ordering.

You tell it what you feel like eating, your maximum budget, dietary needs, and whether you want something familiar, balanced, or completely new. Moodish returns craving-first meal options, explains exact matches and alternatives, and lets you build a cart only after confirmation.

Live app: https://moodish.onrender.com/

The main experience is now conversational: sign in, describe the mood, and Moodish asks only for missing hard constraints such as food mode and maximum budget. A complete prompt such as spicy Chinese, non-veg, under ₹450 with a cold drink goes directly to recommendations.

• Plans a solo meal from a mood like rainy spicy biryani craving.
• Creates private group-meal preference sessions for web, Slack, Teams, and Discord.
• Supports manager choice, team voting, or automatic ranking with creator-only cart confirmation.
• Suggests optional, separately fulfilled Instamart add-ons.
• Uses OpenRouter for the AI summary when configured.
• Lets reviewers use their own OpenRouter API key for a single browser session.
• Shows a transparent recommendation trace: mood tokens, ranking scores, AI prompt, and AI response.
• Keeps ordering safe: cart build requires confirmation, and real checkout/order placement is not implemented.

Moodish defaults to a visibly labelled local demo catalog for Swiggy-like restaurant and Instamart data.

That means:

• Recommendations, group sessions, voting, and cart previews are functional.
• OpenRouter AI inference is real when AI_PROVIDER=openrouter is configured.
• Real Swiggy MCP ordering is not live yet.
• SWIGGY_MODE=live is reserved for when Swiggy grants live MCP access.

Mood input
  -> structured craving intent
  -> dish-first fixture/live Swiggy search
  -> hard availability, maximum-budget, and dietary filtering
  -> ranked shortlist
  -> AI summary
  -> confirmed cart preview

The AI does not secretly pick the restaurant. The deterministic recommender ranks options first. The AI writes a summary, and Moodish shows the full trace so the result is inspectable.

npm install
npm test
npm run smoke
npm run dev

Open:

http://127.0.0.1:8787

Create .env.local from .env.example.

For real AI summaries:

SWIGGY_MODE=fixture
AI_PROVIDER=openrouter
OPENROUTER_API_KEY=your_openrouter_key
OPENROUTER_MODEL=openai/gpt-4o-mini
AI_PROVIDER_TIMEOUT_MS=4500

For durable profiles and group sessions:

DATABASE_URL=postgresql://...
TOKEN_ENCRYPTION_KEY=a-long-random-secret
GROUP_SESSION_SIGNING_KEY=a-different-long-random-secret

Google login additionally needs GOOGLE_CLIENT_ID and GOOGLE_CLIENT_SECRET. Swiggy login uses the MCP OAuth 2.1 + PKCE flow and remains disabled until Swiggy has whitelisted the deployed client and redirect URI; set SWIGGY_OAUTH_ENABLED=true only after that approval. Fixture deployments expose clearly labelled demo access so reviewers can test without pretending that an OAuth provider is connected.

Collaboration webhooks require platform signing credentials, and manager dashboard access requires the matching Slack, Discord, or Microsoft OAuth client credentials listed in .env.example. All three adapters use the same group-session service, reject unsigned requests, and bind private dashboard access to the platform identity that created or manages the session.

Do not commit .env.local or API keys.

Reviewers can also open Developer view in the app and paste their own OpenRouter key. That key is sent only with recommendation requests and is not stored in recommendation memory or returned in the trace.

This is a Node web service. The repo includes render.yaml for Render.

1. Push the repo to GitHub.
2. Create a Render Blueprint from the repo.
3. Add OPENROUTER_API_KEY as a secret env var.
4. Keep SWIGGY_MODE=fixture until live Swiggy OAuth has been completed.

Personal requests accept mood, maxBudget, dietMode (veg, non_veg, both), optional dietaryRules and allergies, discoveryMode (comfort, balanced, explore), addressId, and includeInstamartAddOns.

Legacy budget and numeric novelty remain accepted. Novelty values 1-2 map to comfort, 3 to balanced, and 4-5 to explore.

Create a group session through POST /api/group-sessions, privately submit preferences, close and rank the session, vote or select an option, then have the session creator explicitly confirm the cart. Public session views contain aggregate counts and never include participant allergies or private submissions.

Production command:

npm start

Health check:

/health

See the Moodish Enterprise testing guide for a one-click demo-team journey, a two-browser test, production secret setup, and Slack/Teams/Discord adapter checks.

npm test
npm run smoke

The tests cover mood ranking, dietary filtering, AI failure behavior, cart safety, and the web/API server.