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name: inference description: "Use pretrained easy12306 artifacts for end-to-end 12306 captcha inference, single image-tile prediction, and prerequisite validation." disable-model-invocation: true metadata: disco-role: operating root-skill-id: easy12306 sub-skill-id: inference license: Artistic 2.0

easy12306 inference

This sub-skill is a router and operating guide for using existing easy12306 inference artifacts. It distills the script behavior needed to run or diagnose inference without reopening the source checkout.

Use this sub-skill when

• The user wants to reproduce the legacy README command behavior for end-to-end captcha inference or single image-tile prediction from user-supplied artifacts.
• The user has pretrained artifacts and needs to validate them before model execution.
• The user needs to interpret the printed captcha grid: row col label for eight tiles.
• The user needs to rebuild or adapt script-based inference logic around the original artifact names.

Route elsewhere

• Dataset collection, captcha downloading, crop extraction, perceptual hashing, or .npz construction: use data-preparation.
• Text/OCR model architecture, training, evaluation, or model.h5 regeneration: use text-modeling.
• Image classifier architecture, VGG16 training, augmentation, evaluation, or 12306.image.model.h5 regeneration: use image-modeling.

Required runtime assets

A runnable inference directory needs these files beside the public scripts, or equivalent paths supplied by an adapter:

• model.h5 — text prompt classifier.
• 12306.image.model.h5 — 80-class image-tile classifier.
• texts.txt — UTF-8 label vocabulary with exactly 80 non-empty rows.
• A captcha-like image whose geometry yields exactly eight 67x67 tiles under the easy12306 crop loop.

The root integration may provide more artifact context in ../../references/model-artifacts.md and label semantics in ../../references/label-vocabulary.md.

Start with preflight validation

Use the bundled checker before loading TensorFlow/Keras models:

python3 scripts/check_inference_assets.py \
  --captcha-image <img.jpg> \
  --text-model model.h5 \
  --image-model 12306.image.model.h5 \
  --labels-file texts.txt

The checker is safe by default: it verifies paths, labels, image readability, text-crop geometry, and eight tile crops without loading models. Add --load-models only when the user explicitly wants to validate Keras/TensorFlow loading.

When artifacts pass validation and the user wants actual predictions, use the self-contained adapter instead of depending on a source checkout:

python3 scripts/run_inference.py captcha \
  --captcha-image <img.jpg> \
  --text-model model.h5 \
  --image-model 12306.image.model.h5 \
  --labels-file texts.txt

Operating references

• Inference workflows — command recipes and end-to-end control flow.
• API and data contracts — crop coordinates, tensor shapes, preprocessing, labels, and print formats.
• Troubleshooting — common failures and fixes for missing files, bad labels, geometry, Keras 3, and output interpretation.
• scripts/check_inference_assets.py — safe preflight for image/model/label assets.
• scripts/run_inference.py — self-contained adapter for actual captcha or single-tile prediction when model artifacts are available.

Safety notes

• Do not import or run baidu.py; it performs token acquisition at import time with placeholder credentials.
• Do not depend on the original repository checkout at runtime. Use this sub-skill's references and scripts, plus user-supplied artifacts.
• The verified inspection environment used Python 3.11 with TensorFlow/Keras 2.15. Keras 3 can break the original script import path because mlearn_for_image.py imports keras.preprocessing.image.ImageDataGenerator at module import time.