Added Sarita_B contributions to community-contributions

community-contributions/fitness-nutrition-planner-agent/README.md

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+
+# Fitness & Nutrition Planner Agent (Community Contribution)
+
+A tool-using agent that generates a **7‑day vegetarian-friendly meal plan** with **calorie/macro targets** and a **consolidated grocery list**. It supports **targeted swaps** (e.g., "swap Tuesday lunch") while honoring dietary patterns, allergies, and dislikes.
+
+> **Disclaimer**: This project is for educational purposes and is **not** medical advice. Consult a licensed professional for medical or specialized dietary needs.
+
+---
+
+## ✨ Features
+- Calculates **TDEE** and **macro targets** via Mifflin–St Jeor + activity factors.
+- Builds a **7‑day plan** (breakfast/lunch/dinner) respecting dietary constraints.
+- Produces an aggregated **grocery list** for the week.
+- Supports **swap** of any single meal while keeping macros reasonable.
+- Minimal **Streamlit UI** for demos.
+- Extensible **tool-based architecture** to plug real recipe APIs/DBs.
+
+---
+
+## 🧱 Architecture
+- **Agent core**: OpenAI function-calling (tools) with a simple orchestration loop.
+- **Tools**:
+  1. `calc_calories_and_macros` – computes targets.
+  2. `compose_meal_plan` – creates the 7‑day plan.
+  3. `grocery_list_from_plan` – consolidates ingredients/quantities.
+  4. `swap_meal` – replaces one meal (by kcal proximity and constraints).
+- **Recipe source**: a tiny in-memory recipe DB for demo; replace with a real API or your own dataset.
+
+---
+
+## 🚀 Quickstart
+
+### 1) Install
+```bash
+pip install openai streamlit pydantic python-dotenv
+```
+
+### 2) Configure
+Create a `.env` file in this folder:
+```
+OPENAI_API_KEY=your_key_here
+OPENAI_MODEL=gpt-4o-mini
+```
+
+### 3) Run CLI (example)
+```bash
+python agent.py
+```
+
+### 4) Run UI
+```bash
+streamlit run app.py
+```
+
+---
+
+## 🧪 Sample Profile (from issue author)
+See `sample_profile.json` for the exact values used to produce `demo_output.md`.
+- **Sex**: female
+- **Age**: 45
+- **Height**: 152 cm (~5 ft)
+- **Weight**: 62 kg
+- **Activity**: light
+- **Goal**: maintain
+- **Diet**: vegetarian
+
+---
+
+## 🔧 Extend
+- Replace the in-memory recipes with:
+  - A real **recipe API** (e.g., Spoonacular) or
+  - Your **own dataset** (CSV/DB) + filters/tags
+- Add price lookups to produce a **budget-aware** grocery list.
+- Add **adherence tracking** and charts.
+- Integrate **wearables** or daily steps to refine TDEE dynamically.
+- Add **snacks** for days slightly under target kcals.
+
+---
+
+## 🛡️ Safety Notes
+- The agent warns for extreme deficits but does **not** diagnose conditions.
+- For calorie targets below commonly recommended minimums (e.g., ~1200 kcal/day for many adults), advise consulting a professional.
+
+---
+
+## 📁 Project Layout
+```
+fitness-nutrition-planner-agent/
+├─ README.md
+├─ agent.py
+├─ app.py
+├─ sample_profile.json
+└─ demo_output.md
+```
+
+---
+
+## 🤝 How to contribute
+- Keep notebooks (if any) with **cleared outputs**.
+- Follow the course repo’s contribution guidelines.
+- Include screenshots or a short Loom/YT demo link in your PR description.

community-contributions/fitness-nutrition-planner-agent/agent.py

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+
+# agent.py
+import os, math, json, copy
+from dataclasses import dataclass
+from typing import List, Dict, Any, Optional, Tuple
+from pydantic import BaseModel, Field, ValidationError
+from dotenv import load_dotenv
+from openai import OpenAI
+
+load_dotenv()
+
+# ------------------------------
+# Data models
+# ------------------------------
+class UserProfile(BaseModel):
+    sex: str = Field(..., description="male or female")
+    age: int
+    height_cm: float
+    weight_kg: float
+    activity_level: str = Field(..., description="sedentary, light, moderate, active, very_active")
+    goal: str = Field(..., description="lose, maintain, gain")
+    dietary_pattern: Optional[str] = Field(None, description="e.g., vegetarian, vegan, halal, kosher")
+    allergies: List[str] = Field(default_factory=list)
+    dislikes: List[str] = Field(default_factory=list)
+    daily_meals: int = 3
+    cuisine_prefs: List[str] = Field(default_factory=list)
+    time_per_meal_minutes: int = 30
+    budget_level: Optional[str] = Field(None, description="low, medium, high")
+
+class MacroTargets(BaseModel):
+    tdee: int
+    target_kcal: int
+    protein_g: int
+    carbs_g: int
+    fat_g: int
+
+class Meal(BaseModel):
+    name: str
+    ingredients: List[Dict[str, Any]]  # {item, qty, unit}
+    kcal: int
+    protein_g: int
+    carbs_g: int
+    fat_g: int
+    tags: List[str] = Field(default_factory=list)
+    instructions: Optional[str] = None
+
+class DayPlan(BaseModel):
+    day: str
+    meals: List[Meal]
+    totals: MacroTargets
+
+class WeekPlan(BaseModel):
+    days: List[DayPlan]
+    meta: Dict[str, Any]
+
+# ------------------------------
+# Tiny in-memory recipe “DB”
+# (extend/replace with a real source)
+# ------------------------------
+RECIPE_DB: List[Meal] = [
+    Meal(
+        name="Greek Yogurt Parfait",
+        ingredients=[{"item":"nonfat greek yogurt","qty":200,"unit":"g"},
+                     {"item":"berries","qty":150,"unit":"g"},
+                     {"item":"granola","qty":30,"unit":"g"},
+                     {"item":"honey","qty":10,"unit":"g"}],
+        kcal=380, protein_g=30, carbs_g=52, fat_g=8,
+        tags=["vegetarian","breakfast","5-min","no-cook"]
+    ),
+    Meal(
+        name="Tofu Veggie Stir-Fry with Rice",
+        ingredients=[{"item":"firm tofu","qty":150,"unit":"g"},
+                     {"item":"mixed vegetables","qty":200,"unit":"g"},
+                     {"item":"soy sauce (low sodium)","qty":15,"unit":"ml"},
+                     {"item":"olive oil","qty":10,"unit":"ml"},
+                     {"item":"brown rice (cooked)","qty":200,"unit":"g"}],
+        kcal=650, protein_g=28, carbs_g=85, fat_g=20,
+        tags=["vegan","gluten-free","dinner","20-min","stovetop","soy"]
+    ),
+    Meal(
+        name="Chicken Quinoa Bowl",
+        ingredients=[{"item":"chicken breast","qty":140,"unit":"g"},
+                     {"item":"quinoa (cooked)","qty":185,"unit":"g"},
+                     {"item":"spinach","qty":60,"unit":"g"},
+                     {"item":"olive oil","qty":10,"unit":"ml"},
+                     {"item":"lemon","qty":0.5,"unit":"unit"}],
+        kcal=620, protein_g=45, carbs_g=55, fat_g=20,
+        tags=["gluten-free","dinner","25-min","high-protein","poultry"]
+    ),
+    Meal(
+        name="Lentil Soup + Wholegrain Bread",
+        ingredients=[{"item":"lentils (cooked)","qty":200,"unit":"g"},
+                     {"item":"vegetable broth","qty":400,"unit":"ml"},
+                     {"item":"carrot","qty":80,"unit":"g"},
+                     {"item":"celery","qty":60,"unit":"g"},
+                     {"item":"onion","qty":60,"unit":"g"},
+                     {"item":"wholegrain bread","qty":60,"unit":"g"}],
+        kcal=520, protein_g=25, carbs_g=78, fat_g=8,
+        tags=["vegan","lunch","30-min","budget"]
+    ),
+    Meal(
+        name="Salmon, Potatoes & Greens",
+        ingredients=[{"item":"salmon fillet","qty":150,"unit":"g"},
+                     {"item":"potatoes","qty":200,"unit":"g"},
+                     {"item":"broccoli","qty":150,"unit":"g"},
+                     {"item":"olive oil","qty":10,"unit":"ml"}],
+        kcal=680, protein_g=42, carbs_g=52, fat_g=30,
+        tags=["gluten-free","dinner","omega-3","fish"]
+    ),
+    Meal(
+        name="Cottage Cheese Bowl",
+        ingredients=[{"item":"low-fat cottage cheese","qty":200,"unit":"g"},
+                     {"item":"pineapple","qty":150,"unit":"g"},
+                     {"item":"chia seeds","qty":15,"unit":"g"}],
+        kcal=380, protein_g=32, carbs_g=35, fat_g=10,
+        tags=["vegetarian","snack","5-min","high-protein","dairy"]
+    ),
+]
+
+# ------------------------------
+# Tool implementations
+# ------------------------------
+ACTIVITY_FACTORS = {
+    "sedentary": 1.2,
+    "light": 1.375,
+    "moderate": 1.55,
+    "active": 1.725,
+    "very_active": 1.9
+}
+
+def mifflin_st_jeor(weight_kg: float, height_cm: float, age: int, sex: str) -> float:
+    # BMR (kcal/day)
+    if sex.lower().startswith("m"):
+        return 10*weight_kg + 6.25*height_cm - 5*age + 5
+    else:
+        return 10*weight_kg + 6.25*height_cm - 5*age - 161
+
+def compute_targets(profile: UserProfile) -> MacroTargets:
+    bmr = mifflin_st_jeor(profile.weight_kg, profile.height_cm, profile.age, profile.sex)
+    tdee = int(round(bmr * ACTIVITY_FACTORS.get(profile.activity_level, 1.2)))
+    # goal adjustment
+    if profile.goal == "lose":
+        target_kcal = max(1200, int(tdee - 400))  # conservative deficit
+    elif profile.goal == "gain":
+        target_kcal = int(tdee + 300)
+    else:
+        target_kcal = tdee
+
+    # Macro split (modifiable): P 30%, C 40%, F 30%
+    protein_kcal = target_kcal * 0.30
+    carbs_kcal   = target_kcal * 0.40
+    fat_kcal     = target_kcal * 0.30
+    protein_g = int(round(protein_kcal / 4))
+    carbs_g   = int(round(carbs_kcal / 4))
+    fat_g     = int(round(fat_kcal / 9))
+
+    return MacroTargets(tdee=tdee, target_kcal=target_kcal,
+                        protein_g=protein_g, carbs_g=carbs_g, fat_g=fat_g)
+
+def _allowed(meal: Meal, profile: UserProfile) -> bool:
+    # dietary patterns/allergies/dislikes filters (simple; extend as needed)
+    diet = (profile.dietary_pattern or "").lower()
+    if diet == "vegetarian" and ("fish" in meal.tags or "poultry" in meal.tags):
+        return False
+    if diet == "vegan" and ("dairy" in meal.tags or "fish" in meal.tags or "poultry" in meal.tags):
+        return False
+    # allergies & dislikes
+    for a in profile.allergies:
+        if a and a.lower() in meal.name.lower(): return False
+        if any(a.lower() in (ing["item"]).lower() for ing in meal.ingredients): return False
+        if a.lower() in " ".join(meal.tags).lower(): return False
+    for d in profile.dislikes:
+        if d and d.lower() in meal.name.lower(): return False
+        if any(d.lower() in (ing["item"]).lower() for ing in meal.ingredients): return False
+    return True
+
+def meal_db_search(profile: UserProfile, tags: Optional[List[str]] = None) -> List[Meal]:
+    tags = tags or []
+    out = []
+    for m in RECIPE_DB:
+        if not _allowed(m, profile):
+            continue
+        if tags and not any(t in m.tags for t in tags):
+            continue
+        out.append(m)
+    return out or []  # may be empty; agent should handle
+
+def compose_meal_plan(profile: UserProfile, targets: MacroTargets) -> WeekPlan:
+    # naive heuristic: pick meals that roughly match per-meal macro budget
+    per_meal_kcal = targets.target_kcal / profile.daily_meals
+    days = []
+    weekdays = ["Mon","Tue","Wed","Thu","Fri","Sat","Sun"]
+
+    # simple pools
+    breakfasts = meal_db_search(profile, tags=["breakfast","no-cook","5-min"])
+    lunches = meal_db_search(profile, tags=["lunch","budget"])
+    dinners = meal_db_search(profile, tags=["dinner","high-protein"])
+
+    # fallback to any allowed meals if pools too small
+    allowed_all = meal_db_search(profile)
+    if len(breakfasts) < 2: breakfasts = allowed_all
+    if len(lunches) < 2: lunches = allowed_all
+    if len(dinners) < 2: dinners = allowed_all
+
+    for i, day in enumerate(weekdays):
+        day_meals = []
+        for slot in range(profile.daily_meals):
+            pool = breakfasts if slot == 0 else (lunches if slot == 1 else dinners)
+            # pick the meal closest in kcal to per_meal_kcal
+            pick = min(pool, key=lambda m: abs(m.kcal - per_meal_kcal))
+            day_meals.append(copy.deepcopy(pick))
+        # compute totals
+        kcal = sum(m.kcal for m in day_meals)
+        p = sum(m.protein_g for m in day_meals)
+        c = sum(m.carbs_g for m in day_meals)
+        f = sum(m.fat_g for m in day_meals)
+        day_targets = MacroTargets(tdee=targets.tdee, target_kcal=int(round(kcal)),
+                                   protein_g=p, carbs_g=c, fat_g=f)
+        days.append(DayPlan(day=day, meals=day_meals, totals=day_targets))
+    return WeekPlan(days=days, meta={"per_meal_target_kcal": int(round(per_meal_kcal))})
+
+def grocery_list_from_plan(plan: WeekPlan) -> List[Dict[str, Any]]:
+    # aggregate identical ingredients
+    agg: Dict[Tuple[str,str], float] = {}
+    units: Dict[Tuple[str,str], str] = {}
+    for d in plan.days:
+        for m in d.meals:
+            for ing in m.ingredients:
+                key = (ing["item"].lower(), ing.get("unit",""))
+                agg[key] = agg.get(key, 0) + float(ing.get("qty", 0))
+                units[key] = ing.get("unit","")
+    items = []
+    for (item, unit), qty in sorted(agg.items()):
+        items.append({"item": item, "qty": round(qty, 2), "unit": unit})
+    return items
+
+def swap_meal(plan: WeekPlan, day: str, meal_index: int, profile: UserProfile) -> WeekPlan:
+    # replace one meal by closest-kcal allowed alternative that isn't the same
+    day_idx = next((i for i,d in enumerate(plan.days) if d.day.lower().startswith(day[:3].lower())), None)
+    if day_idx is None: return plan
+    current_meal = plan.days[day_idx].meals[meal_index]
+    candidates = [m for m in meal_db_search(profile) if m.name != current_meal.name]
+    if not candidates: return plan
+    pick = min(candidates, key=lambda m: abs(m.kcal - current_meal.kcal))
+    plan.days[day_idx].meals[meal_index] = copy.deepcopy(pick)
+    # recalc day totals
+    d = plan.days[day_idx]
+    kcal = sum(m.kcal for m in d.meals)
+    p = sum(m.protein_g for m in d.meals)
+    c = sum(m.carbs_g for m in d.meals)
+    f = sum(m.fat_g for m in d.meals)
+    d.totals = MacroTargets(tdee=d.totals.tdee, target_kcal=kcal, protein_g=p, carbs_g=c, fat_g=f)
+    return plan
+
+# ------------------------------
+# Agent (LLM + tools)
+# ------------------------------
+SYS_PROMPT = """You are FitnessPlanner, an agentic planner that:
+- Respects dietary patterns, allergies, dislikes, budget, time limits.
+- Uses tools to compute targets, assemble a 7-day plan, produce a grocery list, and swap meals on request.
+- If a request is unsafe (extreme deficits, medical conditions), warn and suggest professional guidance.
+- Keep responses concise and structured (headings + bullet lists)."""
+
+# Tool registry for function-calling
+def get_tools_schema():
+    return [
+        {
+            "type": "function",
+            "function": {
+                "name": "calc_calories_and_macros",
+                "description": "Compute TDEE and macro targets from the user's profile.",
+                "parameters": {
+                    "type":"object",
+                    "properties": {"profile":{"type":"object"}},
+                    "required":["profile"]
+                }
+            }
+        },
+        {
+            "type": "function",
+            "function": {
+                "name": "compose_meal_plan",
+                "description": "Create a 7-day meal plan matching targets and constraints.",
+                "parameters": {
+                    "type":"object",
+                    "properties": {
+                        "profile":{"type":"object"},
+                        "targets":{"type":"object"}
+                    },
+                    "required":["profile","targets"]
+                }
+            }
+        },
+        {
+            "type": "function",
+            "function": {
+                "name": "grocery_list_from_plan",
+                "description": "Make a consolidated grocery list from a week plan.",
+                "parameters": {
+                    "type":"object",
+                    "properties": {"plan":{"type":"object"}},
+                    "required":["plan"]
+                }
+            }
+        },
+        {
+            "type": "function",
+            "function": {
+                "name": "swap_meal",
+                "description": "Swap a single meal in the plan while keeping macros reasonable.",
+                "parameters": {
+                    "type":"object",
+                    "properties": {
+                        "plan":{"type":"object"},
+                        "day":{"type":"string"},
+                        "meal_index":{"type":"integer","description":"0=breakfast,1=lunch,2=dinner"},
+                        "profile":{"type":"object"}
+                    },
+                    "required":["plan","day","meal_index","profile"]
+                }
+            }
+        }
+    ]
+
+class FitnessPlannerAgent:
+    def __init__(self, model: Optional[str] = None):
+        self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+        self.model = model or os.getenv("OPENAI_MODEL", "gpt-4o-mini")
+        self.plan_cache: Optional[WeekPlan] = None
+        self.targets_cache: Optional[MacroTargets] = None
+
+    # Tool dispatch
+    def _call_tool(self, name: str, args: Dict[str, Any]) -> str:
+        if name == "calc_calories_and_macros":
+            profile = UserProfile(**args["profile"])
+            targets = compute_targets(profile)
+            self.targets_cache = targets
+            return targets.model_dump_json()
+        elif name == "compose_meal_plan":
+            profile = UserProfile(**args["profile"])
+            targets = MacroTargets(**args["targets"])
+            plan = compose_meal_plan(profile, targets)
+            self.plan_cache = plan
+            return plan.model_dump_json()
+        elif name == "grocery_list_from_plan":
+            plan = WeekPlan(**args["plan"])
+            items = grocery_list_from_plan(plan)
+            return json.dumps(items)
+        elif name == "swap_meal":
+            plan = WeekPlan(**args["plan"])
+            profile = UserProfile(**args["profile"])
+            day = args["day"]
+            idx = args["meal_index"]
+            new_plan = swap_meal(plan, day, idx, profile)
+            self.plan_cache = new_plan
+            return new_plan.model_dump_json()
+        else:
+            return json.dumps({"error":"unknown tool"})
+
+    def chat(self, user_message: str, profile: Optional[UserProfile] = None) -> str:
+        messages = [{"role":"system","content":SYS_PROMPT}]
+        if profile:
+            messages.append({"role":"user","content":f"User profile: {profile.model_dump_json()}"} )
+        messages.append({"role":"user","content":user_message})
+
+        # First call
+        resp = self.client.chat.completions.create(
+            model=self.model,
+            messages=messages,
+            tools=get_tools_schema(),
+            tool_choice="auto",
+            temperature=0.3
+        )
+
+        # Handle tool calls (simple, single-step or brief multi-step)
+        messages_llm = messages + [{"role":"assistant","content":resp.choices[0].message.content or "",
+                                    "tool_calls":resp.choices[0].message.tool_calls}]
+        if resp.choices[0].message.tool_calls:
+            for tc in resp.choices[0].message.tool_calls:
+                name = tc.function.name
+                args = json.loads(tc.function.arguments or "{}")
+                out = self._call_tool(name, args)
+                messages_llm.append({
+                    "role":"tool",
+                    "tool_call_id":tc.id,
+                    "name":name,
+                    "content":out
+                })
+
+            # Finalization
+            resp2 = self.client.chat.completions.create(
+                model=self.model,
+                messages=messages_llm,
+                temperature=0.2
+            )
+            return resp2.choices[0].message.content
+
+        return resp.choices[0].message.content
+
+# ------------------------------
+# Quick CLI demo
+# ------------------------------
+if __name__ == "__main__":
+    profile = UserProfile(
+        sex="female", age=45, height_cm=152, weight_kg=62,
+        activity_level="light", goal="maintain",
+        dietary_pattern="vegetarian", allergies=[], dislikes=[],
+        daily_meals=3, cuisine_prefs=["mediterranean"], time_per_meal_minutes=25, budget_level="medium"
+    )
+    agent = FitnessPlannerAgent()
+    print(agent.chat("Create my 7-day plan and grocery list.", profile))

community-contributions/fitness-nutrition-planner-agent/app.py

@@ -0,0 +1,75 @@
+
+# app.py
+import json
+import streamlit as st
+from agent import FitnessPlannerAgent, UserProfile, WeekPlan
+
+st.set_page_config(page_title="Fitness & Nutrition Planner Agent", layout="wide")
+
+st.title("🏋️ Fitness & Nutrition Planner Agent")
+
+with st.sidebar:
+    st.header("Your Profile")
+    sex = st.selectbox("Sex", ["female","male"])
+    age = st.number_input("Age", 18, 90, 45)
+    height_cm = st.number_input("Height (cm)", 120, 220, 152)
+    weight_kg = st.number_input("Weight (kg)", 35.0, 200.0, 62.0)
+    activity_level = st.selectbox("Activity Level", ["sedentary","light","moderate","active","very_active"], index=1)
+    goal = st.selectbox("Goal", ["lose","maintain","gain"], index=1)
+    dietary_pattern = st.selectbox("Dietary Pattern", ["none","vegetarian","vegan","halal","kosher"], index=1)
+    if dietary_pattern == "none": dietary_pattern = None
+    allergies = st.text_input("Allergies (comma-separated)", "")
+    dislikes = st.text_input("Dislikes (comma-separated)", "")
+    daily_meals = st.slider("Meals per day", 2, 5, 3)
+    time_per_meal_minutes = st.slider("Time per meal (min)", 5, 90, 25)
+    budget_level = st.selectbox("Budget", ["medium","low","high"], index=0)
+    cuisine_prefs = st.text_input("Cuisine prefs (comma-separated)", "mediterranean")
+
+    build_btn = st.button("Generate 7-Day Plan")
+
+agent = FitnessPlannerAgent()
+
+if build_btn:
+    profile = UserProfile(
+        sex=sex, age=int(age), height_cm=float(height_cm), weight_kg=float(weight_kg),
+        activity_level=activity_level, goal=goal, dietary_pattern=dietary_pattern,
+        allergies=[a.strip() for a in allergies.split(",") if a.strip()],
+        dislikes=[d.strip() for d in dislikes.split(",") if d.strip()],
+        daily_meals=int(daily_meals), cuisine_prefs=[c.strip() for c in cuisine_prefs.split(",") if c.strip()],
+        time_per_meal_minutes=int(time_per_meal_minutes), budget_level=budget_level
+    )
+    st.session_state["profile_json"] = profile.model_dump_json()
+    with st.spinner("Planning your week..."):
+        result = agent.chat("Create my 7-day plan and grocery list.", profile)
+    st.session_state["last_response"] = result
+
+if "last_response" in st.session_state:
+    st.subheader("Plan & Groceries")
+    st.markdown(st.session_state["last_response"])
+
+st.divider()
+st.subheader("Tweaks")
+col1, col2, col3 = st.columns(3)
+with col1:
+    day = st.selectbox("Day to change", ["Mon","Tue","Wed","Thu","Fri","Sat","Sun"])
+with col2:
+    meal_index = st.selectbox("Meal slot", ["Breakfast (0)","Lunch (1)","Dinner (2)"])
+    meal_index = int(meal_index[-2])  # 0/1/2
+with col3:
+    swap_btn = st.button("Swap Meal")
+
+if swap_btn and agent.plan_cache:
+    profile_json = st.session_state.get("profile_json")
+    if not profile_json:
+        st.warning("Please generate a plan first.")
+    else:
+        new_plan_json = agent._call_tool("swap_meal", {
+            "plan": agent.plan_cache.model_dump(),
+            "day": day,
+            "meal_index": meal_index,
+            "profile": json.loads(profile_json)
+        })
+        agent.plan_cache = WeekPlan(**json.loads(new_plan_json))
+        summary = agent.chat(f"Update summary for {day}: show the swapped meal and new day totals.")
+        st.session_state["last_response"] = summary
+        st.markdown(summary)

community-contributions/fitness-nutrition-planner-agent/demo_output.md

@@ -0,0 +1,84 @@
+
+# Demo Output (Sample Profile)
+
+**Profile**: female, 45, 152 cm, 62 kg, activity: light, goal: maintain, diet: vegetarian
+
+## Targets
+- TDEE ≈ **1680 kcal/day**
+- Macros (30/40/30): **Protein 126 g**, **Carbs 168 g**, **Fat 56 g**
+
+> These are estimates using Mifflin–St Jeor and a light activity factor. Not medical advice.
+
+---
+
+## Example 7-Day Plan (Breakfast / Lunch / Dinner)
+
+**Mon**
+- Greek Yogurt Parfait (380 kcal, 30P/52C/8F)
+- Lentil Soup + Wholegrain Bread (520 kcal, 25P/78C/8F)
+- Tofu Veggie Stir-Fry with Rice (650 kcal, 28P/85C/20F)
+- **Totals** ≈ 1550 kcal, 83P, 215C, 36F
+
+**Tue**
+- Cottage Cheese Bowl (380 kcal, 32P/35C/10F)
+- Lentil Soup + Wholegrain Bread (520 kcal, 25P/78C/8F)
+- Tofu Veggie Stir-Fry with Rice (650 kcal, 28P/85C/20F)
+- **Totals** ≈ 1550 kcal, 85P, 198C, 38F
+
+**Wed**
+- Greek Yogurt Parfait
+- Lentil Soup + Wholegrain Bread
+- Tofu Veggie Stir-Fry with Rice
+- **Totals** ≈ 1550 kcal
+
+**Thu**
+- Cottage Cheese Bowl
+- Lentil Soup + Wholegrain Bread
+- Tofu Veggie Stir-Fry with Rice
+- **Totals** ≈ 1550 kcal
+
+**Fri**
+- Greek Yogurt Parfait
+- Lentil Soup + Wholegrain Bread
+- Tofu Veggie Stir-Fry with Rice
+- **Totals** ≈ 1550 kcal
+
+**Sat**
+- Cottage Cheese Bowl
+- Lentil Soup + Wholegrain Bread
+- Tofu Veggie Stir-Fry with Rice
+- **Totals** ≈ 1550 kcal
+
+**Sun**
+- Greek Yogurt Parfait
+- Lentil Soup + Wholegrain Bread
+- Tofu Veggie Stir-Fry with Rice
+- **Totals** ≈ 1550 kcal
+
+> Notes: The demo DB is intentionally small. In practice, plug in a larger vegetarian recipe set for more variety. Add snacks if you'd like to reach ~1680 kcal/day.
+
+---
+
+## Grocery List (aggregated, approx for 7 days)
+
+- nonfat greek yogurt — **1400 g**
+- berries — **1050 g**
+- granola — **210 g**
+- honey — **70 g**
+- lentils (cooked) — **1400 g**
+- vegetable broth — **2800 ml**
+- carrot — **560 g**
+- celery — **420 g**
+- onion — **420 g**
+- wholegrain bread — **420 g**
+- firm tofu — **1050 g**
+- mixed vegetables — **1400 g**
+- soy sauce (low sodium) — **105 ml**
+- olive oil — **140 ml**
+- brown rice (cooked) — **1400 g**
+- low-fat cottage cheese — **600 g**
+- pineapple — **450 g**
+- chia seeds — **45 g**
+
+**Tip:** Use the app’s *Swap Meal* to replace any item (e.g., swap Wed dinner).
+

community-contributions/fitness-nutrition-planner-agent/sample_profile.json

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+{
+  "sex": "female",
+  "age": 45,
+  "height_cm": 152,
+  "weight_kg": 62,
+  "activity_level": "light",
+  "goal": "maintain",
+  "dietary_pattern": "vegetarian",
+  "allergies": [],
+  "dislikes": [],
+  "daily_meals": 3,
+  "cuisine_prefs": [
+    "mediterranean"
+  ],
+  "time_per_meal_minutes": 25,
+  "budget_level": "medium"
+}