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chore: comment out debug step in similarity cache workflow

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The debug step was helpful for diagnosing numpy array issues but is no longer needed for normal operation. Commented out rather than removed so it's available if needed for future troubleshooting.
This commit is contained in:
matt 2025-10-19 08:37:07 -07:00
parent 33791c93e8
commit b2ccbbd664
1 changed files with 39 additions and 38 deletions
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77
.github/workflows/build-similarity-cache.yml vendored
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@ -89,44 +89,45 @@ jobs:
exit 1 exit 1
fi fi
- name: Debug - Inspect Parquet file after tagging # Debug step - uncomment if needed to inspect Parquet file contents
if: steps.check_cache.outputs.needs_build == 'true' # - name: Debug - Inspect Parquet file after tagging
run: | # if: steps.check_cache.outputs.needs_build == 'true'
python -c " # run: |
import pandas as pd # python -c "
from pathlib import Path # import pandas as pd
from code.path_util import get_processed_cards_path # from pathlib import Path
# from code.path_util import get_processed_cards_path
parquet_path = Path(get_processed_cards_path()) #
print(f'Reading Parquet file: {parquet_path}') # parquet_path = Path(get_processed_cards_path())
print(f'File exists: {parquet_path.exists()}') # print(f'Reading Parquet file: {parquet_path}')
# print(f'File exists: {parquet_path.exists()}')
if not parquet_path.exists(): #
raise FileNotFoundError(f'Parquet file not found: {parquet_path}') # if not parquet_path.exists():
# raise FileNotFoundError(f'Parquet file not found: {parquet_path}')
df = pd.read_parquet(parquet_path) #
print(f'Loaded {len(df)} rows from Parquet file') # df = pd.read_parquet(parquet_path)
print(f'Columns: {list(df.columns)}') # print(f'Loaded {len(df)} rows from Parquet file')
print('') # print(f'Columns: {list(df.columns)}')
# print('')
# Show first 5 rows completely #
print('First 5 complete rows:') # # Show first 5 rows completely
print('=' * 100) # print('First 5 complete rows:')
for idx, row in df.head(5).iterrows(): # print('=' * 100)
print(f'Row {idx}:') # for idx, row in df.head(5).iterrows():
for col in df.columns: # print(f'Row {idx}:')
value = row[col] # for col in df.columns:
if isinstance(value, (list, tuple)) or hasattr(value, '__array__'): # value = row[col]
# For array-like, show type and length # if isinstance(value, (list, tuple)) or hasattr(value, '__array__'):
try: # # For array-like, show type and length
length = len(value) # try:
print(f' {col}: {type(value).__name__}[{length}] = {value}') # length = len(value)
except: # print(f' {col}: {type(value).__name__}[{length}] = {value}')
print(f' {col}: {type(value).__name__} = {value}') # except:
else: # print(f' {col}: {type(value).__name__} = {value}')
print(f' {col}: {value}') # else:
print('-' * 100) # print(f' {col}: {value}')
" # print('-' * 100)
# "
- name: Generate theme catalog - name: Generate theme catalog
if: steps.check_cache.outputs.needs_build == 'true' if: steps.check_cache.outputs.needs_build == 'true'