Previously the coach required running simulations first to generate a
deck-reports/*.json file. Now if no simulation report is found, the
endpoint builds a lightweight DeckReport on the fly from the deck_cards
table — pulling card names, types, CMC, and color identity directly from
the DB. The LLM can still analyze composition and give advice, just
without simulation-derived performance metrics (impactScore, etc.).
Also removes the '[no report]' tag from the dropdown since all decks
can now be coached.
The decks table has 'commander_name' not 'commander'. The SQL query
was throwing OperationalError which the frontend silently caught,
resulting in an empty dropdown.
Root cause: Perplexity deck generation hit the 8192 max_tokens limit,
truncating the JSON response mid-string at card ~70-80. json.loads()
fails with 'Unterminated string' and the fallback also couldn't recover.
Fixes:
- Bump DECK_GEN_MAX_TOKENS from 8192 to 16384 (primary fix)
- Add _repair_truncated_json() to PerplexityClient — if token limit is
still hit, salvages all complete card objects from the truncated JSON
instead of returning a 422 error
- Add OUTPUT EFFICIENCY instructions to system prompt to reduce per-card
verbosity (shorter reasons, fewer synergy_with entries)
- Add 'truncated' flag to response so UI can warn if deck is incomplete
- Log warning when recovered deck has <90 cards
Backend returns [{deck_id, deck_name, commander, has_report, ...}] directly,
not {decks: [...]}. Frontend was reading data.decks which was always undefined.
Now properly unwraps the array, shows deck name + commander, slugifies name
for coach session deck_id, and marks decks without reports.
- Fixed all ML types to match actual backend response shapes:
MLStatus (logging state + decision files), MLDataStatus (datasets,
checkpoints, eval results), MLTrainStatus (phases, metrics, result),
PPOTrainStatus (metrics with win_rate/reward/losses), TournamentStatus
- Updated training API client with full parameter support for all endpoints
- Rewrote TrainingPage with 5 clear pipeline phases:
Phase 1: Data Collection (toggle logging, view decision files)
Phase 2: Dataset & Model Artifacts (splits, checkpoints, eval, model info)
Phase 3: Supervised Training (epochs/lr/batch/patience, live progress,
phase badges, real-time metrics, final eval results)
Phase 4: PPO Reinforcement Learning (full config: iterations, episodes,
clip epsilon, entropy coeff, opponent selection, live win rate)
Phase 5: Tournament Evaluation (episodes, playstyle, leaderboard table)
- All sections are collapsible with live phase badges and progress bars
- Advanced config toggles for supervised and PPO training
- Proper mutex: can't start PPO while supervised is running, etc.
- Auto-refresh model info and data status when training completes
- Redesigned Recommendations tab with EDHREC / From Collection sub-tabs
- EDHREC sub-tab: fetches average deck data, groups by role, shows owned/missing status
- Collection sub-tab: shows cards from collection grouped by type, shortfall indicators
- Both sub-tabs support: checkbox selection, bulk add to deck, single add buttons
- Role badges with color-coding (Ramp, Draw, Removal, BoardWipe, etc.)
- Card hover preview for EDHREC recommendations
- Show only owned filter for EDHREC results
- Fixed API client types: EdhRecsResponse, CollectionRecsResponse wrappers
- Added EdhRecCard and CollectionRecCard interfaces
KoalaTrapLord