\n \n \n \n\n",[18,97],"fa258fa9-6cee-4f88-b8c8-243440ee545d",["D",1783365647101],"\n\n",[18,91],"00384e56-ce05-4f2d-a399-667e9a5785ba",["D",1783365438135],[18,79],"pulls","branches","forks","envVars",{"_98":26,"_255":26},"connections",{"_98":26,"_254":26},"unfilledCount","emptyCount",{"_36":220,"_13":18,"_77":130,"_46":-5,"_38":39,"_221":43,"_222":24,"_40":361,"_42":26,"_80":81,"_224":-5,"_82":30,"_225":-5,"_278":362,"_17":363},{"_36":244,"_13":79,"_77":78,"_46":130,"_38":39,"_221":43,"_222":24,"_40":358,"_42":26,"_80":81,"_224":-5,"_82":30,"_225":-5,"_278":359,"_17":360},{"_36":350,"_13":351,"_77":352,"_46":78,"_38":39,"_221":353,"_222":24,"_40":354,"_42":275,"_80":103,"_224":355,"_82":30,"_225":-5,"_278":356,"_17":357},{"_36":342,"_13":343,"_77":344,"_46":78,"_38":39,"_221":345,"_222":24,"_40":346,"_42":43,"_80":103,"_224":347,"_82":30,"_225":-5,"_278":348,"_17":349},{"_36":334,"_13":335,"_77":336,"_46":78,"_38":39,"_221":337,"_222":24,"_40":338,"_42":43,"_80":103,"_224":339,"_82":30,"_225":-5,"_278":340,"_17":341},{"_36":326,"_13":327,"_77":328,"_46":78,"_38":39,"_221":329,"_222":24,"_40":330,"_42":43,"_80":103,"_224":331,"_82":30,"_225":-5,"_278":332,"_17":333},{"_36":318,"_13":319,"_77":320,"_46":78,"_38":39,"_221":321,"_222":24,"_40":322,"_42":43,"_80":103,"_224":323,"_82":30,"_225":-5,"_278":324,"_17":325},{"_36":240,"_13":91,"_77":90,"_46":130,"_38":39,"_221":115,"_222":24,"_40":315,"_42":43,"_80":21,"_224":242,"_82":30,"_225":-5,"_278":316,"_17":317},{"_36":235,"_13":97,"_77":96,"_46":130,"_38":39,"_221":123,"_222":24,"_40":312,"_42":237,"_80":21,"_224":238,"_82":30,"_225":-5,"_278":313,"_17":314},{"_36":231,"_13":102,"_77":101,"_46":130,"_38":39,"_221":43,"_222":24,"_40":309,"_42":26,"_80":103,"_224":233,"_82":30,"_225":-5,"_278":310,"_17":311},{"_36":305,"_13":297,"_77":291,"_46":-5,"_38":39,"_221":237,"_222":24,"_40":306,"_42":26,"_80":81,"_224":-5,"_82":30,"_225":-5,"_278":307,"_17":308},{"_36":298,"_13":299,"_77":300,"_46":291,"_38":39,"_221":275,"_222":24,"_40":301,"_42":26,"_80":103,"_224":302,"_82":30,"_225":-5,"_278":303,"_17":304},{"_36":288,"_13":289,"_77":290,"_46":291,"_38":39,"_221":292,"_222":24,"_40":293,"_42":99,"_80":103,"_224":294,"_82":30,"_225":-5,"_278":295,"_17":296},{"_36":281,"_13":282,"_77":283,"_46":-5,"_38":39,"_221":170,"_222":24,"_40":284,"_42":237,"_80":21,"_224":285,"_82":30,"_225":-5,"_278":286,"_17":287},{"_36":271,"_13":272,"_77":143,"_46":-5,"_38":39,"_221":273,"_222":24,"_40":274,"_42":275,"_80":276,"_224":277,"_82":30,"_225":-5,"_278":279,"_17":280},"53a8880a-1d76-46ab-9a08-d03ffb838031","index.ts",20,["D",1783366271264],3,"http","import { parseVal, serveFile } from \"https://esm.town/v/std/utils/index.ts\";\nimport { Hono } from \"npm:hono\";\nimport {\n initDB,\n createMeal,\n getMeals,\n createSymptom,\n getSymptoms,\n getAnalysis,\n} from \"./lib/db.ts\";\nimport { extractIngredients } from \"./lib/ai.ts\";\n\nconst app = new Hono();\n\n// Initialize database on first request\nlet dbReady = false;\nasync function ensureDB() {\n if (!dbReady) {\n await initDB();\n dbReady = true;\n }\n}\n\n// Serve index.html at the root /\napp.get(\"/\", () => serveFile(\"/frontend/index.html\"));\n\n// Serve all /frontend files\napp.get(\"/frontend/**/*\", (c) => serveFile(c.req.path));\n\n// View source redirect\napp.get(\"/source\", (c) => c.redirect(parseVal().links.self.val));\n\n// ─── API Routes ───\n\n// Get all meals\napp.get(\"/api/meals\", async (c) => {\n await ensureDB();\n const meals = await getMeals();\n return c.json(meals);\n});\n\n// Create a meal (with AI ingredient extraction)\napp.post(\"/api/meals\", async (c) => {\n await ensureDB();\n const body = await c.req.json();\n const { description, ingredients, created_at } = body;\n\n if (!description?.trim()) {\n return c.json({ error: \"Description is required\" }, 400);\n }\n\n // If ingredients provided, use them; otherwise extract via AI\n const finalIngredients = Array.isArray(ingredients) && ingredients.length > 0\n ? ingredients.map((i: string) => i.toLowerCase().trim()).filter(Boolean)\n : await extractIngredients(description.trim());\n\n const meal = await createMeal(description.trim(), finalIngredients, created_at);\n return c.json(meal, 201);\n});\n\n// Extract ingredients from text (preview before saving)\napp.post(\"/api/extract-ingredients\", async (c) => {\n const body = await c.req.json();\n const { description } = body;\n if (!description?.trim()) {\n return c.json({ error: \"Description is required\" }, 400);\n }\n const ingredients = await extractIngredients(description.trim());\n return c.json({ ingredients });\n});\n\n// Get all symptoms\napp.get(\"/api/symptoms\", async (c) => {\n await ensureDB();\n const symptoms = await getSymptoms();\n return c.json(symptoms);\n});\n\n// Log a symptom\napp.post(\"/api/symptoms\", async (c) => {\n await ensureDB();\n const body = await c.req.json();\n const { meal_id, bloating, gas, pain, bowel_movement, notes, created_at } = body;\n\n const symptom = await createSymptom({\n meal_id: meal_id ?? null,\n bloating: Math.max(0, Math.min(5, Number(bloating) || 0)),\n gas: Math.max(0, Math.min(5, Number(gas) || 0)),\n pain: Math.max(0, Math.min(5, Number(pain) || 0)),\n bowel_movement: bowel_movement ?? \"none\",\n notes: notes ?? \"\",\n created_at: created_at ?? undefined,\n });\n return c.json(symptom, 201);\n});\n\n// Get ingredient correlation analysis\napp.get(\"/api/analysis\", async (c) => {\n await ensureDB();\n const analysis = await getAnalysis();\n return c.json(analysis);\n});\n\n// Unwrap and rethrow Hono errors as the original error\napp.onError((err) => Promise.reject(err));\n\n// HTTP vals expect an exported \"fetch handler\"\nexport default app.fetch;\n","children",[],[272],"ff7a3c52-f454-4ef8-8ccb-bd7ef514bc7f","README.md","019f38dd-0b14-74df-87f3-f0f4f14b861d",["D",1783366482955],"# 🫃 Gut Tracker\n\nTrack what you eat and how your digestion responds — over time, identify which\ningredients are correlated with digestive issues.\n\n## Features\n\n- **AI-powered meal logging** — describe your meal in plain text, and GPT-4o-mini\n automatically extracts individual ingredients (powered by Val Town's built-in\n OpenAI integration — no API key needed!)\n- **Bristol Stool Scale** — track bowel movements using the medically-recognized\n 7-type scale (Type 1 = hard lumps/constipation → Type 7 = watery/diarrhea),\n with visual color indicators and category labels\n- **Digestion symptom tracking** — log bloating, gas, and pain/cramps on a 0–5\n scale with color-coded severity buttons\n- **Date & time backfilling** — pick any date/time when logging a meal or symptom,\n so you can add entries retroactively. On iOS, this uses the native wheel picker.\n- **Smart correlation** — ingredients are automatically linked to symptoms logged\n within 24 hours, or you can manually link a symptom to a specific meal\n- **Insights dashboard** — see which ingredients correlate with worse-than-average\n or better-than-average digestion, ranked by deviation from your baseline\n\n## Design\n\nBuilt to look and feel like a native iOS app:\n- SF Pro font stack via `-apple-system`\n- iOS system colors (`#007AFF` blue, `#34C759` green, `#F2F2F7` grouped background)\n- Frosted glass tab bar with `backdrop-filter: blur()`\n- Large title navigation pattern\n- Inset grouped table views with 12px rounded corners\n- Safe area insets for notch/Dynamic Island devices\n- PWA meta tags for \"Add to Home Screen\"\n\n## How it works\n\n```mermaid\ngraph LR\n User -->|\"describe meal\"| LogMeal[\"Log Meal\"]\n LogMeal -->|\"POST /api/meals\"| AI[\"OpenAI gpt-4o-mini\"]\n AI -->|extracts| Ingredients[\"ingredient list\"]\n Ingredients -->|stored in| SQLite[(SQLite)]\n\n User -->|\"rate symptoms + Bristol Scale\"| LogSymptom[\"Log Symptom\"]\n LogSymptom -->|\"POST /api/symptoms\"| SQLite\n\n Insights -->|\"GET /api/analysis\"| Analysis[\"Correlation Engine\"]\n Analysis -->|reads| SQLite\n Analysis -->|ranks| Output[\"ingredient ↔ severity deltas\"]\n```\n\n## File structure\n\n```\nindex.ts ← Hono server (API routes + frontend serving)\nlib/\n db.ts ← SQLite schema, queries, correlation analysis\n ai.ts ← OpenAI ingredient extraction via std/openai\nfrontend/\n index.html ← HTML shell (iOS fonts, Twind, safe areas)\n index.tsx ← React entrypoint\n favicon.svg ← App icon\n components/\n App.tsx ← Root layout + iOS frosted tab bar\n LogMeal.tsx ← Meal entry + AI extraction + date picker\n LogSymptom.tsx ← Symptom ratings + Bristol Stool Scale + date picker\n History.tsx ← iOS grouped list with date section headers\n Insights.tsx ← Ingredient correlation cards with delta bars\n```\n\n## Tech\n\n- **Frontend**: React 18 + Twind (Tailwind at runtime, no build step)\n- **Backend**: Hono on Val Town\n- **Database**: Val Town built-in SQLite (`std/sqlite`)\n- **AI**: Val Town's built-in OpenAI proxy (`std/openai`) — free tier covers\n `gpt-4o-mini`, no API key required\n\n## Usage\n\n1. Open the app on your phone (it's mobile-first!)\n2. **Meal** tab → type what you ate → tap \"Extract Ingredients with AI\" →\n review/edit the ingredient chips → pick a date if backfilling → save\n3. **Symptoms** tab → rate bloating/gas/pain → select Bristol Stool Scale type →\n optionally link to a meal → pick a date if backfilling → save\n4. **History** tab → see all entries grouped by date with Bristol color indicators\n5. **Insights** tab → see which ingredients correlate with bad digestion\n\nThe more meals and symptoms you log, the more reliable the correlations become.\nAt least 2 meals per ingredient are needed before it appears in insights.\n",[],[282],"b5a86bf5-6507-42e4-8afe-b0d3a461ff62","db.ts","6288c69a-796f-11f1-9f42-1607ee4eb77e","37dc5f98-ac69-41c7-a46f-f4585189e848",18,["D",1783366263235],"import { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport type Meal = {\n id: number;\n description: string;\n ingredients: string[];\n created_at: string;\n};\n\nexport type Symptom = {\n id: number;\n meal_id: number | null;\n created_at: string;\n bloating: number;\n gas: number;\n pain: number;\n bowel_movement: string;\n notes: string;\n};\n\nexport type MealWithSymptoms = Meal & {\n symptoms: Symptom[];\n};\n\nexport type IngredientAnalysis = {\n ingredient: string;\n meal_count: number;\n symptom_count: number;\n avg_severity: number;\n overall_avg_severity: number;\n delta: number;\n};\n\nconst SYMPTOM_WINDOW_HOURS = 24;\n\nexport async function initDB() {\n await sqlite.batch([\n {\n sql: `CREATE TABLE IF NOT EXISTS meals (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n description TEXT NOT NULL,\n ingredients TEXT NOT NULL DEFAULT '[]',\n created_at TEXT NOT NULL DEFAULT (datetime('now'))\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS symptoms (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n meal_id INTEGER,\n created_at TEXT NOT NULL DEFAULT (datetime('now')),\n bloating INTEGER DEFAULT 0,\n gas INTEGER DEFAULT 0,\n pain INTEGER DEFAULT 0,\n bowel_movement TEXT DEFAULT 'none',\n notes TEXT DEFAULT '',\n FOREIGN KEY (meal_id) REFERENCES meals(id)\n )`,\n },\n ]);\n}\n\nexport async function createMeal(\n description: string,\n ingredients: string[],\n createdAt?: string,\n): Promise {\n await sqlite.execute({\n sql: \"INSERT INTO meals (description, ingredients, created_at) VALUES (?, ?, COALESCE(?, datetime('now')))\",\n args: [description, JSON.stringify(ingredients), createdAt ?? null],\n });\n const result = await sqlite.execute(\n \"SELECT * FROM meals ORDER BY id DESC LIMIT 1\",\n );\n const row = result.rows[0] as unknown as Meal;\n return {\n ...row,\n ingredients: JSON.parse(row.ingredients as unknown as string),\n };\n}\n\nexport async function getMeals(limit = 50): Promise {\n const result = await sqlite.execute({\n sql: \"SELECT * FROM meals ORDER BY created_at DESC LIMIT ?\",\n args: [limit],\n });\n return result.rows.map((row) => ({\n ...(row as unknown as Meal),\n ingredients: JSON.parse((row as unknown as Meal).ingredients as unknown as string),\n }));\n}\n\nexport async function createSymptom(\n data: Omit & { created_at?: string },\n): Promise {\n await sqlite.execute({\n sql: `INSERT INTO symptoms (meal_id, bloating, gas, pain, bowel_movement, notes, created_at)\n VALUES (?, ?, ?, ?, ?, ?, COALESCE(?, datetime('now')))`,\n args: [\n data.meal_id,\n data.bloating,\n data.gas,\n data.pain,\n data.bowel_movement,\n data.notes,\n data.created_at ?? null,\n ],\n });\n const result = await sqlite.execute(\n \"SELECT * FROM symptoms ORDER BY id DESC LIMIT 1\",\n );\n return result.rows[0] as unknown as Symptom;\n}\n\nexport async function getSymptoms(limit = 50): Promise {\n const result = await sqlite.execute({\n sql: \"SELECT * FROM symptoms ORDER BY created_at DESC LIMIT ?\",\n args: [limit],\n });\n return result.rows as unknown as Symptom[];\n}\n\nexport async function getMealsWithSymptoms(\n limit = 50,\n): Promise {\n const meals = await getMeals(limit);\n const allSymptoms = await sqlite.execute(\n \"SELECT * FROM symptoms ORDER BY created_at DESC\",\n );\n const symptoms = allSymptoms.rows as unknown as Symptom[];\n\n return meals.map((meal) => {\n const mealTime = new Date(meal.created_at + \"Z\").getTime();\n const windowMs = SYMPTOM_WINDOW_HOURS * 60 * 60 * 1000;\n const linked = symptoms.filter((s) => {\n if (s.meal_id === meal.id) return true;\n // Also match symptoms within the time window that aren't linked to another meal\n if (s.meal_id === null) {\n const symTime = new Date(s.created_at + \"Z\").getTime();\n return symTime >= mealTime && symTime <= mealTime + windowMs;\n }\n return false;\n });\n return { ...meal, symptoms: linked };\n });\n}\n\nfunction severityScore(s: Symptom): number {\n // Bristol Stool Scale penalties\n const bmPenalty: Record = {\n none: 0,\n type1: 2, // separate hard lumps — severe constipation\n type2: 1, // lumpy sausage — mild constipation\n type3: 0, // cracked sausage — normal\n type4: 0, // smooth snake — ideal\n type5: 0, // soft blobs — slightly soft\n type6: 2, // mushy — mild diarrhea\n type7: 3, // watery — severe diarrhea\n };\n return s.bloating + s.gas + s.pain + (bmPenalty[s.bowel_movement] ?? 0);\n}\n\nexport async function getAnalysis(): Promise<{\n ingredients: IngredientAnalysis[];\n total_meals: number;\n total_symptoms: number;\n}> {\n const mealsWithSymptoms = await getMealsWithSymptoms(200);\n\n // Build per-ingredient stats\n const stats: Record<\n string,\n { meal_count: number; total_severity: number; symptom_count: number }\n > = {};\n\n // Compute overall average per meal-symptom pair (not per ingredient-symptom pair)\n let overallTotalSeverity = 0;\n let overallSymptomCount = 0;\n const seenSymptomIds = new Set();\n\n for (const meal of mealsWithSymptoms) {\n for (const s of meal.symptoms) {\n // Count each unique symptom once for the overall average\n if (!seenSymptomIds.has(s.id)) {\n seenSymptomIds.add(s.id);\n const score = severityScore(s);\n overallTotalSeverity += score;\n overallSymptomCount++;\n }\n }\n\n for (const ingredient of meal.ingredients) {\n const key = ingredient.toLowerCase().trim();\n if (!key) continue;\n if (!stats[key]) {\n stats[key] = { meal_count: 0, total_severity: 0, symptom_count: 0 };\n }\n stats[key].meal_count++;\n\n for (const s of meal.symptoms) {\n const score = severityScore(s);\n stats[key].total_severity += score;\n stats[key].symptom_count++;\n }\n }\n }\n\n const overallAvg =\n overallSymptomCount > 0\n ? overallTotalSeverity / overallSymptomCount\n : 0;\n\n const ingredients: IngredientAnalysis[] = Object.entries(stats)\n .map(([ingredient, s]) => ({\n ingredient,\n meal_count: s.meal_count,\n symptom_count: s.symptom_count,\n avg_severity: s.symptom_count > 0\n ? s.total_severity / s.symptom_count\n : 0,\n overall_avg_severity: overallAvg,\n delta: s.symptom_count > 0\n ? (s.total_severity / s.symptom_count) - overallAvg\n : 0,\n }))\n .filter((i) => i.meal_count >= 2) // need at least 2 meals for meaningful data\n .sort((a, b) => Math.abs(b.delta) - Math.abs(a.delta));\n\n return {\n ingredients,\n total_meals: mealsWithSymptoms.length,\n total_symptoms: seenSymptomIds.size,\n };\n}\n",[],[297,289],"lib","57410bf6-8f01-40ea-b071-be1ca56c9ec5","ai.ts","67a34e98-796f-11f1-95b9-1607ee4eb77e",["D",1783365481219],"import { OpenAI } from \"https://esm.town/v/std/openai\";\n\n/**\n * Uses AI to extract a list of ingredients from a meal description.\n * Falls back to simple parsing if the AI call fails.\n */\nexport async function extractIngredients(description: string): Promise {\n const openai = new OpenAI();\n\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n max_completion_tokens: 500,\n messages: [\n {\n role: \"system\",\n content: `You are a nutrition assistant. Given a meal description, extract a list of individual food ingredients.\nReturn ONLY a JSON array of strings, each being a single ingredient in lowercase.\nBe specific (e.g. \"garlic\", \"onion\", \"wheat flour\", \"cow's milk\", \"black pepper\").\nInclude spices, sauces, and cooking oils if mentioned or strongly implied.\nDo not include generic terms like \"food\" or \"meal\".\nExample input: \"pasta with tomato sauce and garlic bread\"\nExample output: [\"wheat flour pasta\", \"tomato\", \"garlic\", \"wheat flour bread\", \"olive oil\"]`,\n },\n {\n role: \"user\",\n content: description,\n },\n ],\n });\n\n const content = completion.choices[0]?.message?.content?.trim() ?? \"[]\";\n\n // Strip markdown code fences if present\n const cleaned = content.replace(/^```(?:json)?\\s*/i, \"\").replace(/\\s*```$/i, \"\");\n\n try {\n const parsed = JSON.parse(cleaned);\n if (Array.isArray(parsed)) {\n return parsed\n .filter((x) => typeof x === \"string\")\n .map((x) => x.toLowerCase().trim())\n .filter(Boolean);\n }\n } catch {\n // fall through to simple parse\n }\n\n // Fallback: simple comma/space splitting\n return description\n .split(/[,;]/)\n .map((s) => s.trim().toLowerCase())\n .filter(Boolean);\n}\n",[],[297,299],"751301e0-2950-4c10-9582-d5389757d596",["D",1783365472652],[267,268],[297],["D",1783365438135],[],[18,102],["D",1783366274595],[],[18,97],["D",1783365647101],[],[18,91],"5ee7e1ec-5185-4440-ba84-b1e4db8db4ef","LogSymptom.tsx","87c734be-796f-11f1-822a-1607ee4eb77e",24,["D",1783366385200],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState, useEffect } from \"https://esm.sh/react@18.2.0\";\n\ntype Meal = {\n id: number;\n description: string;\n ingredients: string[];\n created_at: string;\n};\n\nconst BRISTOL_TYPES = [\n { value: \"type1\", label: \"Hard lumps\", desc: \"Separate hard lumps, like nuts\", category: \"constipation\", color: \"#8B4513\" },\n { value: \"type2\", label: \"Lumpy sausage\", desc: \"Sausage-shaped but lumpy\", category: \"constipation\", color: \"#A0522D\" },\n { value: \"type3\", label: \"Cracked sausage\", desc: \"Sausage with cracks on surface\", category: \"normal\", color: \"#BC8F5F\" },\n { value: \"type4\", label: \"Smooth snake\", desc: \"Smooth, soft sausage or snake\", category: \"ideal\", color: \"#D2691E\" },\n { value: \"type5\", label: \"Soft blobs\", desc: \"Soft blobs with clear edges\", category: \"soft\", color: \"#DAA520\" },\n { value: \"type6\", label: \"Mushy\", desc: \"Mushy with ragged edges\", category: \"diarrhea\", color: \"#CD853F\" },\n { value: \"type7\", label: \"Watery\", desc: \"Watery, no solid pieces\", category: \"diarrhea\", color: \"#DEB887\" },\n];\n\nconst CATEGORY_COLORS: Record = {\n constipation: \"#FF9500\",\n normal: \"#34C759\",\n ideal: \"#34C759\",\n soft: \"#FFCC00\",\n diarrhea: \"#FF3B30\",\n};\n\nfunction nowLocalInput(): string {\n const now = new Date();\n const offset = now.getTimezoneOffset();\n const local = new Date(now.getTime() - offset * 60000);\n return local.toISOString().slice(0, 16);\n}\n\nfunction localToSQLiteUTC(localStr: string): string {\n const date = new Date(localStr);\n return date.toISOString().slice(0, 19).replace(\"T\", \" \");\n}\n\nfunction IOSRating({\n label,\n emoji,\n value,\n onChange,\n}: {\n label: string;\n emoji: string;\n value: number;\n onChange: (v: number) => void;\n}) {\n const colors = [\"#E5E5EA\", \"#34C759\", \"#FFCC00\", \"#FF9500\", \"#FF3B30\", \"#FF2D55\"];\n return (\n

\n {emoji} {label}\n {value}/5\n

\n {[0, 1, 2, 3, 4, 5].map((n) => (\n onChange(n)}\n class=\"flex-1 h-9 rounded-[8px] transition-all cursor-pointer active:scale-90\"\n style={{\n backgroundColor: n <= value ? colors[value] : \"#E5E5EA\",\n boxShadow: n === value ? `0 0 0 2px ${colors[value]}40` : \"none\",\n }}\n >\n {n}\n \n ))}\n

\n );\n}\n\nexport function LogSymptom({ onSaved }: { onSaved?: () => void }) {\n const [meals, setMeals] = useState([]);\n const [mealId, setMealId] = useState(null);\n const [bloating, setBloating] = useState(0);\n const [gas, setGas] = useState(0);\n const [pain, setPain] = useState(0);\n const [bowelMovement, setBowelMovement] = useState(\"none\");\n const [notes, setNotes] = useState(\"\");\n const [saving, setSaving] = useState(false);\n const [message, setMessage] = useState(\"\");\n const [dateTime, setDateTime] = useState(nowLocalInput());\n\n useEffect(() => {\n fetch(\"/api/meals\")\n .then((r) => r.json())\n .then((data: Meal[]) => setMeals(data.slice(0, 10)))\n .catch(() => {});\n }, []);\n\n async function handleSave() {\n setSaving(true);\n setMessage(\"\");\n try {\n const res = await fetch(\"/api/symptoms\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n meal_id: mealId,\n bloating,\n gas,\n pain,\n bowel_movement: bowelMovement,\n notes: notes.trim(),\n created_at: localToSQLiteUTC(dateTime),\n }),\n });\n const data = await res.json();\n if (data.error) {\n setMessage(\"⚠️ \" + data.error);\n } else {\n setMessage(\"Saved ✓\");\n setBloating(0);\n setGas(0);\n setPain(0);\n setBowelMovement(\"none\");\n setNotes(\"\");\n setMealId(null);\n setDateTime(nowLocalInput());\n onSaved?.();\n setTimeout(() => setMessage(\"\"), 2000);\n }\n } catch {\n setMessage(\"⚠️ Failed to save.\");\n } finally {\n setSaving(false);\n }\n }\n\n const hasData = bloating > 0 || gas > 0 || pain > 0 || bowelMovement !== \"none\";\n const selectedBristol = BRISTOL_TYPES.find((t) => t.value === bowelMovement);\n\n return (\n

\n {/* Meal link + Date */}\n

\n \n Linked Meal\n \n

\n setMealId(e.target.value ? Number(e.target.value) : null)}\n >\n \n {meals.map((m) => (\n \n ))}\n \n

\n \n Date & Time\n \n setDateTime(e.target.value)}\n />\n

\n\n {/* Severity ratings */}\n

\n \n Severity\n \n

\n \n \n \n

\n\n {/* Bristol Stool Scale */}\n

\n \n Bristol Stool Scale\n \n

\n {/* None option */}\n setBowelMovement(\"none\")}\n class=\"w-full flex items-center gap-3 px-4 py-3 transition-colors cursor-pointer\"\n style={{ backgroundColor: bowelMovement === \"none\" ? \"#F2F2F7\" : \"transparent\" }}\n >\n

\n {bowelMovement === \"none\" &&

}\n

\n None\n \n\n {BRISTOL_TYPES.map((t) => {\n const isSelected = bowelMovement === t.value;\n return (\n setBowelMovement(t.value)}\n class=\"w-full flex items-center gap-3 px-4 py-3 transition-colors cursor-pointer\"\n style={{ backgroundColor: isSelected ? \"#F2F2F7\" : \"transparent\" }}\n >\n

\n {isSelected &&

}\n

\n \n

\n Type {t.value.slice(4)}\n \n {t.category}\n \n

{t.desc}

\n \n );\n })}\n

\n\n {/* Notes */}\n

\n \n Notes\n \n

\n setNotes(e.target.value)}\n />\n

\n\n {/* Save */}\n \n {saving ? \"Saving…\" : \"Save Symptom\"}\n \n\n {message && (\n

\n {message}\n

\n )}\n

\n );\n}\n",[],[18,79,319],"3ac3b4ee-2274-4373-8d63-ac523ea350d4","LogMeal.tsx","7e404d0e-796f-11f1-a636-1607ee4eb77e",23,["D",1783366352142],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState } from \"https://esm.sh/react@18.2.0\";\n\nfunction nowLocalInput(): string {\n const now = new Date();\n const offset = now.getTimezoneOffset();\n const local = new Date(now.getTime() - offset * 60000);\n return local.toISOString().slice(0, 16);\n}\n\nfunction localToSQLiteUTC(localStr: string): string {\n const date = new Date(localStr);\n return date.toISOString().slice(0, 19).replace(\"T\", \" \");\n}\n\nexport function LogMeal({ onSaved }: { onSaved?: () => void }) {\n const [description, setDescription] = useState(\"\");\n const [ingredients, setIngredients] = useState([]);\n const [extracting, setExtracting] = useState(false);\n const [saving, setSaving] = useState(false);\n const [newIngredient, setNewIngredient] = useState(\"\");\n const [message, setMessage] = useState(\"\");\n const [dateTime, setDateTime] = useState(nowLocalInput());\n\n async function handleExtract() {\n if (!description.trim()) return;\n setExtracting(true);\n setMessage(\"\");\n try {\n const res = await fetch(\"/api/extract-ingredients\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ description: description.trim() }),\n });\n const data = await res.json();\n if (data.error) {\n setMessage(\"⚠️ \" + data.error);\n } else {\n setIngredients(data.ingredients);\n setMessage(\"✅ Ingredients extracted — review below\");\n setTimeout(() => setMessage(\"\"), 2500);\n }\n } catch {\n setMessage(\"⚠️ Could not extract. Try saving directly.\");\n } finally {\n setExtracting(false);\n }\n }\n\n async function handleSave() {\n if (!description.trim()) return;\n setSaving(true);\n setMessage(\"\");\n try {\n const res = await fetch(\"/api/meals\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n description: description.trim(),\n ingredients: ingredients.length > 0 ? ingredients : undefined,\n created_at: localToSQLiteUTC(dateTime),\n }),\n });\n const data = await res.json();\n if (data.error) {\n setMessage(\"⚠️ \" + data.error);\n } else {\n setMessage(\"Saved ✓\");\n setDescription(\"\");\n setIngredients([]);\n setDateTime(nowLocalInput());\n onSaved?.();\n setTimeout(() => setMessage(\"\"), 2000);\n }\n } catch {\n setMessage(\"⚠️ Failed to save.\");\n } finally {\n setSaving(false);\n }\n }\n\n function removeIngredient(idx: number) {\n setIngredients((prev) => prev.filter((_, i) => i !== idx));\n }\n\n function addIngredient() {\n const trimmed = newIngredient.trim().toLowerCase();\n if (trimmed && !ingredients.includes(trimmed)) {\n setIngredients((prev) => [...prev, trimmed]);\n }\n setNewIngredient(\"\");\n }\n\n return (\n

\n {/* Meal description */}\n

\n \n Description\n \n

\n setDescription(e.target.value)}\n />\n

\n\n {/* Date picker */}\n

\n \n Date & Time\n \n

\n setDateTime(e.target.value)}\n />\n

\n\n {/* AI Extract button */}\n \n {extracting ? \"Extracting…\" : \"✨ Extract Ingredients with AI\"}\n \n\n {/* Ingredients */}\n {ingredients.length > 0 && (\n

\n \n Ingredients\n \n {ingredients.length}\n

\n {ingredients.map((ing, idx) => (\n \n {ing}\n removeIngredient(idx)}\n class=\"text-[#8E8E93] hover:text-[#FF3B30] cursor-pointer text-sm leading-none\"\n >\n ✕\n \n \n ))}\n

\n )}\n\n {/* Manual add */}\n

\n setNewIngredient(e.target.value)}\n onKeyDown={(e) => e.key === \"Enter\" && (e.preventDefault(), addIngredient())}\n />\n \n Add\n \n

\n\n {/* Save */}\n \n {saving ? \"Saving…\" : \"Save Meal\"}\n \n\n {message && (\n

\n {message}\n

\n )}\n

\n );\n}\n",[],[18,79,327],"d6f919e1-2158-40db-9d75-4afff1b1b584","Insights.tsx","9ca23db6-796f-11f1-aa1d-1607ee4eb77e",26,["D",1783366439004],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState, useEffect } from \"https://esm.sh/react@18.2.0\";\n\ntype IngredientAnalysis = {\n ingredient: string;\n meal_count: number;\n symptom_count: number;\n avg_severity: number;\n overall_avg_severity: number;\n delta: number;\n};\n\ntype AnalysisResponse = {\n ingredients: IngredientAnalysis[];\n total_meals: number;\n total_symptoms: number;\n};\n\nexport function Insights() {\n const [data, setData] = useState(null);\n const [loading, setLoading] = useState(true);\n\n useEffect(() => {\n fetch(\"/api/analysis\")\n .then((r) => r.json())\n .then((d: AnalysisResponse) => setData(d))\n .catch(() => {})\n .finally(() => setLoading(false));\n }, []);\n\n if (loading) {\n return (\n

\n );\n }\n\n if (!data || data.total_meals === 0) {\n return (\n

📊

No data yet

Log meals and symptoms to see insights

\n );\n }\n\n if (data.total_symptoms === 0) {\n return (\n

📊

{data.total_meals} meals logged

Log symptoms after eating to find patterns

\n );\n }\n\n if (data.ingredients.length === 0) {\n return (\n

📊

Not enough data yet

Need at least 2 meals per ingredient with linked symptoms

\n );\n }\n\n return (\n

\n {/* Summary stats */}\n

{data.total_meals}

meals logged

{data.total_symptoms}

symptoms tracked

\n\n {/* Correlations */}\n

\n Ingredient Correlations\n

\n {data.ingredients.map((ing, idx) => {\n const isWorse = ing.delta > 0.3;\n const isBetter = ing.delta < -0.3;\n const indicatorColor = isWorse ? \"#FF3B30\" : isBetter ? \"#34C759\" : \"#8E8E93\";\n const bgColor = isWorse ? \"#FFF5F5\" : isBetter ? \"#F0FBF4\" : \"transparent\";\n\n return (\n \n

\n {ing.ingredient}\n

\n {ing.meal_count} meals · {ing.symptom_count} symptom{ing.symptom_count !== 1 ? \"s\" : \"\"}\n

\n \n \n {ing.avg_severity.toFixed(1)}\n \n

\n avg {ing.overall_avg_severity.toFixed(1)}\n

\n {/* Delta bar */}\n

\n \n

\n \n {ing.delta > 0 ? \"+\" : \"\"}{ing.delta.toFixed(1)}\n \n

\n {isWorse && ing.delta > 1 && (\n

\n ⚠️ Potential trigger — {ing.delta.toFixed(1)} pts above average\n

\n )}\n

\n );\n })}\n

\n\n {/* Disclaimer */}\n

\n Correlations are not medical advice. Patterns become more reliable with more data.\n
\n View source\n

\n );\n}\n",[],[18,79,335],"88be14d7-16f1-45a0-a47d-96bd69546e2b","History.tsx","936df852-796f-11f1-aa1d-1607ee4eb77e",25,["D",1783366420135],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState, useEffect } from \"https://esm.sh/react@18.2.0\";\n\ntype Meal = {\n id: number;\n description: string;\n ingredients: string[];\n created_at: string;\n};\n\ntype Symptom = {\n id: number;\n meal_id: number | null;\n created_at: string;\n bloating: number;\n gas: number;\n pain: number;\n bowel_movement: string;\n notes: string;\n};\n\ntype Entry = {\n type: \"meal\" | \"symptom\";\n data: Meal | Symptom;\n};\n\nconst BRISTOL_LABELS: Record = {\n none: \"None\",\n type1: \"Type 1 · Hard lumps\",\n type2: \"Type 2 · Lumpy\",\n type3: \"Type 3 · Cracked\",\n type4: \"Type 4 · Smooth\",\n type5: \"Type 5 · Soft blobs\",\n type6: \"Type 6 · Mushy\",\n type7: \"Type 7 · Watery\",\n};\n\nconst BRISTOL_COLORS: Record = {\n type1: \"#8B4513\",\n type2: \"#A0522D\",\n type3: \"#BC8F5F\",\n type4: \"#D2691E\",\n type5: \"#DAA520\",\n type6: \"#CD853F\",\n type7: \"#DEB887\",\n};\n\nfunction formatTime(dateStr: string): string {\n const d = new Date(dateStr + \"Z\");\n return d.toLocaleTimeString(undefined, { hour: \"numeric\", minute: \"2-digit\" });\n}\n\nfunction formatDateGroup(dateStr: string): string {\n const d = new Date(dateStr + \"Z\");\n const today = new Date();\n const yesterday = new Date(today);\n yesterday.setDate(yesterday.getDate() - 1);\n\n if (d.toDateString() === today.toDateString()) return \"Today\";\n if (d.toDateString() === yesterday.toDateString()) return \"Yesterday\";\n return d.toLocaleDateString(undefined, { weekday: \"long\", month: \"long\", day: \"numeric\" });\n}\n\nexport function History() {\n const [entries, setEntries] = useState([]);\n const [loading, setLoading] = useState(true);\n\n useEffect(() => {\n Promise.all([\n fetch(\"/api/meals\").then((r) => r.json()),\n fetch(\"/api/symptoms\").then((r) => r.json()),\n ])\n .then(([meals, symptoms]: [Meal[], Symptom[]]) => {\n const combined: Entry[] = [\n ...meals.map((m) => ({ type: \"meal\" as const, data: m })),\n ...symptoms.map((s) => ({ type: \"symptom\" as const, data: s })),\n ];\n combined.sort((a, b) =>\n new Date(b.data.created_at + \"Z\").getTime() -\n new Date(a.data.created_at + \"Z\").getTime()\n );\n setEntries(combined);\n })\n .catch(() => {})\n .finally(() => setLoading(false));\n }, []);\n\n if (loading) {\n return (\n

\n );\n }\n\n if (entries.length === 0) {\n return (\n

📋

No entries yet

Log a meal or symptom to get started

\n );\n }\n\n // Group entries by date\n const grouped: { date: string; items: Entry[] }[] = [];\n for (const entry of entries) {\n const dateKey = formatDateGroup(entry.data.created_at);\n const last = grouped[grouped.length - 1];\n if (last && last.date === dateKey) {\n last.items.push(entry);\n } else {\n grouped.push({ date: dateKey, items: [entry] });\n }\n }\n\n return (\n

\n {grouped.map((group) => (\n

\n {/* Date section header */}\n

\n {group.date}\n

\n {/* Grouped list */}\n

\n {group.items.map((entry, idx) => {\n if (entry.type === \"meal\") {\n const meal = entry.data as Meal;\n return (\n

\n 🍽️\n

\n {meal.description}\n

\n \n {formatTime(meal.created_at)}\n \n

\n {meal.ingredients.length > 0 && (\n

\n {meal.ingredients.slice(0, 6).map((ing, i) => (\n \n {ing}\n \n ))}\n {meal.ingredients.length > 6 && (\n \n +{meal.ingredients.length - 6}\n \n )}\n

\n )}\n

\n );\n } else {\n const s = entry.data as Symptom;\n const severity = s.bloating + s.gas + s.pain;\n const severityColor = severity >= 8 ? \"#FF3B30\" : severity >= 4 ? \"#FF9500\" : severity > 0 ? \"#FFCC00\" : \"#8E8E93\";\n const hasBM = s.bowel_movement !== \"none\";\n const bmColor = BRISTOL_COLORS[s.bowel_movement];\n\n return (\n

\n 🚽\n

\n Digestion\n

\n \n {formatTime(s.created_at)}\n \n

\n {s.bloating > 0 && 🎈 {s.bloating}}\n {s.gas > 0 && 💨 {s.gas}}\n {s.pain > 0 && 😣 {s.pain}}\n

\n {hasBM && (\n

\n \n {BRISTOL_LABELS[s.bowel_movement]}\n \n

\n )}\n {s.notes && (\n

\n {s.notes}\n

\n )}\n {severity > 0 && (\n

\n \n

\n \n {severity}/15\n \n

\n )}\n

\n );\n }\n })}\n

\n ))}\n

\n );\n}\n",[],[18,79,343],"b23e9fec-eb0e-492b-92e1-1e968098b380","App.tsx","019f38dd-0b13-778e-abae-dcb1dc5a7703",22,["D",1783366323012],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState } from \"https://esm.sh/react@18.2.0\";\nimport { LogMeal } from \"./LogMeal.tsx\";\nimport { LogSymptom } from \"./LogSymptom.tsx\";\nimport { History } from \"./History.tsx\";\nimport { Insights } from \"./Insights.tsx\";\n\ntype Tab = \"meal\" | \"symptom\" | \"history\" | \"insights\";\n\nconst TABS: { id: Tab; label: string; emoji: string }[] = [\n { id: \"meal\", label: \"Meal\", emoji: \"🍽️\" },\n { id: \"symptom\", label: \"Symptoms\", emoji: \"🚽\" },\n { id: \"history\", label: \"History\", emoji: \"📋\" },\n { id: \"insights\", label: \"Insights\", emoji: \"📊\" },\n];\n\nconst TITLES: Record = {\n meal: \"Log Meal\",\n symptom: \"Log Symptoms\",\n history: \"History\",\n insights: \"Insights\",\n};\n\nexport function App() {\n const [tab, setTab] = useState(\"meal\");\n const [refreshKey, setRefreshKey] = useState(0);\n\n const handleSaved = () => setRefreshKey((k) => k + 1);\n\n return (\n \n {/* Large Title Header */}\n \n

\n {TITLES[tab]}\n

\n \n\n {/* Content */}\n

\n {tab === \"meal\" && }\n {tab === \"symptom\" && }\n {tab === \"history\" && }\n {tab === \"insights\" && }\n

\n\n {/* Frosted Glass Tab Bar */}\n \n

\n {TABS.map((t) => (\n setTab(t.id)}\n class={`flex-1 flex flex-col items-center gap-0.5 pt-2 pb-1.5 transition-colors cursor-pointer ${\n tab === t.id ? \"text-[#007AFF]\" : \"text-[#8E8E93]\"\n }`}\n >\n {t.emoji}\n \n {t.label}\n \n \n ))}\n

\n \n

\n );\n}\n",[],[18,79,351],["D",1783365438135],[258,259,260,261,262],[18,79],["D",1783365438135],[257,263,264,265],[18],["D",1783365438135],["D",1789523429989],{"_11":12,"_186":85},{"_11":12,"_182":-5,"_84":85,"_31":83,"_183":184,"_185":30},{"_42":170,"_13":16,"_36":39,"_171":132,"_172":74,"_173":174,"_175":136,"_176":177,"_178":179,"_180":14,"_181":12},"ctx",{"_55":-5,"_207":402,"_403":404,"_405":406,"_407":30,"_408":30,"_409":30,"_410":24,"_411":-5,"_412":-7,"_413":-7,"_414":26,"_415":30,"_416":30,"_417":-5,"_418":30,"_419":30,"_420":30,"_421":-5},"ENV",{"_373":374,"_375":376,"_377":16,"_378":379,"_380":381,"_382":383,"_384":385,"_386":387,"_388":389,"_390":391,"_392":393,"_394":395,"_396":397,"_398":399,"_400":401},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

History · frontend