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Mood-Based Meal Planner

An AI-powered meal planning experience that adapts to how you feel, what you have, and what your goals are so meal decisions become fast, personalized, and healthier over time.

Mood DetectionChat UIRecipe MatchingNutritionExplainabilityVoice
Product OverviewImpactTeam

Product overview

Problem statement

People struggle with daily meal decisions due to mood, time, diet constraints, and not knowing what to cook with available ingredients leading to unhealthy choices and wasted groceries.

What it solves

The app detects your mood, remembers preferences, and recommends meals you can actually make then explains why each option fits your mood and nutrition goals.

Key capabilities

Mood-aware recommendations
Suggests meals based on user mood + preferences, with easy swaps for dietary needs.
Ingredient-first planning
Users can add available ingredients; AI prioritizes recipes that use what they already have.
Nutrition tracking
Calories + macro/micro estimates per meal, plus daily summaries.
Explainability
Shows “why this meal” (mood fit, ingredients matched, nutrition goal alignment).

Why this is different

  • • Mood-to-meal mapping (comfort, energy, calm, focus) instead of generic recipe search
  • • Ingredient-aware suggestions to reduce waste and increase adherence
  • • Explainable recommendations to build trust (“why this meal?”)

Tech stack

Frontend
Next.js (App Router) + Tailwind CSS
AI layer
LLM-powered chat + recommendation logic (swap in your model/provider)
Mood detection
Text sentiment + optional voice tone analysis (if enabled)
Data
User preferences, ingredients, meal logs (DB of choice)
Deployment
Vercel / Docker / Cloud (your choice)
Observability
Basic analytics + feedback loop for tuning

Team

Who built it and how we contribute to the community.

Madan Venkatesh
Senior Software Engineer - AI Specialist
What they do: Owns end-to-end product (UX → APIs → LLM orchestration). Builds mood detection, meal logic, dashboards, and integrations.
Community impact: Drives weekly demos, ships new features, and supports other teams with reusable AI patterns (RAG/chat, evals, deployment).
Shree Vamshika Manandi
Senior Software Engineer - Full Stack
What they do: Implements UI flows, onboarding, responsive design, and performance optimizations.
Community impact: Maintains shared UI components, helps other B360U teams move faster with consistent design patterns.
Sairam Konda
Senior Software Engineer - Full Stack
What they do: Improves recommendation quality, evaluates mood signals, and iterates on explainability (e.g., reasons behind suggestions).
Community impact: Creates evaluation dashboards and documentation so other teams can reuse model testing practices.

Collaboration notes

We share reusable prompts/components, document integrations, and run quick feedback cycles with the community to keep shipping.