v1.1.0:3 — AI upgrades: history context, test connection, cost estimator, streaming preview

Four incremental upgrades to the AI program generator. No schema change, no /data migration.

1. History as context (the killer feature)
   - lib/ai/historyContext.ts builds a 90-day per-exercise rollup:
     frequency, recent weights, estimated 1RM (Epley), avg RPE,
     days-since-last, plus a STAGNANT flag when the heaviest weight in
     the new half doesn't beat the old half.
   - Generate page surfaces an "Include my workout history as context"
     checkbox (default on at >=10 logged workouts). When checked, the
     ~1-3 KB summary is appended to the system prompt so the model can
     recommend things like "you've stalled bench at 245 — try paused reps."
   - We deliberately don't ship raw set logs (privacy + token cost).

2. Test connection
   - POST /api/ai/test sends a tiny "say hi in 3 words" prompt and
     reports latency + first sample, or the error inline.
   - "Test connection" button next to "Save AI config" in
     Settings -> AI integration. Verifies provider/model/key/baseUrl
     without going through full program generation.

3. Cost estimator
   - lib/ai/pricing.ts ships a price table for major models
     (Claude 3.5/3.7/4/4.5, GPT-4o/5/o1/o3/o4-mini, Gemini 1.5/2.0/2.5).
     Ollama always returns 0; openai-compatible returns null.
   - Generation history shows per-row cost + a 30-day rolling total
     at the top of the page.

4. Streaming preview render
   - lib/ai/lenientJson.ts: stack-aware partial-JSON parser that
     auto-closes open strings/brackets/braces in reverse-of-opening
     order, drops dangling key:value pairs and partial keywords.
     Returns a best-effort snapshot of the program-so-far on each chunk.
   - Generate UI now renders a live "Building program..." panel that
     updates as weeks/days/exercises arrive instead of just showing
     raw text and waiting for stream end.

Tests: 26 new (ai-historyContext.test.ts, ai-lenientJson.test.ts,
ai-pricing.test.ts). 161 total pass.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

proof-of-work/app/api/ai/generate/route.ts

@@ -7,6 +7,10 @@ import {
   PROGRAM_OUTPUT_SHAPE,
   parseAIProgram,
 } from '@/lib/ai/programSchema';
+import {
+  buildHistorySummary,
+  formatHistoryContext,
+} from '@/lib/ai/historyContext';
 
 /**
  * POST /api/ai/generate
@@ -33,6 +37,13 @@ import {
 const bodySchema = z.object({
   templateId: z.string().optional().nullable(),
   userInput: z.string().min(1),
+  /**
+   * When true, build + append a compact summary of the user's
+   * recent (90-day) workout history to the system prompt. Lets the
+   * model design around stagnations, current strength levels, and
+   * actual training frequency.
+   */
+  includeHistory: z.boolean().optional().default(false),
 });
 
 export const dynamic = 'force-dynamic';
@@ -135,6 +146,13 @@ export async function POST(request: NextRequest) {
     })),
   );
 
+  // If requested, build the workout-history summary block.
+  let historyBlock = '';
+  if (parsed.data.includeHistory) {
+    const summary = await buildHistorySummary(prisma, user.id);
+    historyBlock = formatHistoryContext(summary);
+  }
+
   // Stitch the final system + user prompts.
   const baseSystem = template?.systemPrompt ?? DEFAULT_SYSTEM_PROMPT;
   const systemPrompt = `${baseSystem}
@@ -143,7 +161,7 @@ OUTPUT SHAPE — emit ONLY a JSON object matching this shape (no commentary, no
 ${PROGRAM_OUTPUT_SHAPE}
 
 LIBRARY — pick exerciseId values from this list when possible. If you need an exercise the user doesn't have, set exerciseId to null and put the proposed name in exerciseName; the user will resolve it during preview.
-${libraryJson}`;
+${libraryJson}${historyBlock}`;
 
   const userPromptBody =
     template?.userPromptTemplate.replace(/{{userInput}}/g, parsed.data.userInput) ??