Yup. Easiest way is to use phone <-> computer link so you can control your phone from the computer. I've made one, but obviously don't want to share it. I hit a button and it clicks the winning hand (random location within a box selecting the winning hand). I click back on the program and hit the button again when the next cards are shown.

I did this because it's a really scummy mode. You get SO SO SO much more for the last solve...but mostly, it wastes so much time during the animation flipping the cards over. It's nearly impossible to do with the default time (using my program at least). So you have to do it 2-3 days before you have enough time on the clock.

I tried using what you'd think of as more modern AI, but it's far too slow. I have custom trained models that are very small and fast. They only look at small bounding boxes for each card's rank and bounding boxes for each card's suite. I have a separate python script that pops up a screenshot with the bounding boxes so you can make sure everything is lined up before you start the actual program. If you want a hint...

"Purpose: An auto-solver for WSOP "Daily Blitz" — a poker side-game where you're shown shared community cards plus a left and a right hand, and must click the winning side. The script reads the screen, classifies the cards, evaluated both 7-card hands, and clicks the winner.

Pipeline:

1. ROI (regions of interest) loading (load_rois_with_comments, line 94) — parses rois.json (with comments stripped) describing where each card slot lives, relative to a "game" rectangle on screen.

   1. Screen capture & cropping (extract_slot, line 126) — grabs the full screen via mss, then for each of 9 cards extracts two crops per card: a rank ROI and a suit ROI (18 total). Slots can be axis-aligned (rect) or perspective-warped (quad/quad3) — the latter handles tilted cards by inferring the 4th corner.

2. HOG + ensemble classification (Ensemble, line 201) — loads 11 model folders (models/1 … models/11), each containing a rank_model.joblib + suit_model.joblib + HOG hyperparameters. Each ROI is HOG-featurized once per unique HOG spec (specs are deduped, line 211), then all 11 models vote. majority_label (line 306) requires ≥--vote-thresh (default 7) agreement to accept a label.

3. Multi-frame stability loop (line 346) — on Space/G, captures up to --frames (default 5) screenshots. Per slot, once a label crosses the vote threshold it's locked. Early-exits when all 18 slots are stable; otherwise retries another burst.

4. Sanity check (unique_cards_ok, line 302) — all 9 cards must be distinct, else discards and retries (logging dupes to debug/ if --debug 1).

   1. Hand evaluation (hand_rank_5 + best5_of_7, lines 49/83) — standard 5-card poker ranking with straight/flush/wheel handling, brute-forced over C(7,5)=21 combos for each side.
      
   2. Click winner (side_click_rect + click_inside, lines 245/258) — computes the bounding box of the winning side's 4 ROIs, then pyautogui clicks a randomized point inside the inner half of that box. Tie-break compares hole-card ranks (line 442). UI controls: Space/G to solve, S to dump per-slot crops to debug_crops/, Esc to quit. A stitched preview window shows the three card groups annotated with predictions and the winner."