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fixed plot alt-text-tag function

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This commit is contained in:
Luigi Maiorano
2026-02-02 17:07:44 +01:00
parent 45dd121d90
commit 02a0214539
5 changed files with 244 additions and 15 deletions
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13
04_PPTX_Update_Images.py
View File

@@ -33,6 +33,19 @@ def _(TAG_IMAGE_DIR, TAG_SOURCE, TAG_TARGET):
return return
@app.cell
def _():
utils._calculate_file_sha1('figures/OneDrive_2026-01-28/All_Respondents/most_prominent_personality_traits.png')
return
@app.cell
def _():
utils._calculate_perceptual_hash('figures/Picture.png')
return
@app.cell(hide_code=True) @app.cell(hide_code=True)
def _(): def _():
mo.md(r""" mo.md(r"""

8
plots.py
View File

@@ -952,6 +952,7 @@ class JPMCPlotsMixin:
width: int = 1600, width: int = 1600,
height: int = 800, height: int = 800,
background_color: str = 'white', background_color: str = 'white',
random_state: int = 23,
): ):
"""Create a word cloud visualization of personality traits from survey data. """Create a word cloud visualization of personality traits from survey data.
@@ -962,6 +963,7 @@ class JPMCPlotsMixin:
width: Width of the word cloud image in pixels width: Width of the word cloud image in pixels
height: Height of the word cloud image in pixels height: Height of the word cloud image in pixels
background_color: Background color for the word cloud background_color: Background color for the word cloud
random_state: Random seed for reproducible word cloud generation (default: 23)
Returns: Returns:
matplotlib.figure.Figure: The word cloud figure for display in notebooks matplotlib.figure.Figure: The word cloud figure for display in notebooks
@@ -983,6 +985,9 @@ class JPMCPlotsMixin:
# Create frequency dictionary # Create frequency dictionary
trait_freq = Counter(traits_list) trait_freq = Counter(traits_list)
# Set random seed for color selection
random.seed(random_state)
# Color function using JPMC colors # Color function using JPMC colors
def color_func(word, font_size, position, orientation, random_state=None, **kwargs): def color_func(word, font_size, position, orientation, random_state=None, **kwargs):
colors = [ colors = [
@@ -1002,7 +1007,8 @@ class JPMCPlotsMixin:
relative_scaling=0.5, relative_scaling=0.5,
min_font_size=10, min_font_size=10,
prefer_horizontal=0.7, prefer_horizontal=0.7,
collocations=False # Treat each word independently collocations=False, # Treat each word independently
random_state=random_state # Seed for reproducible layout
).generate_from_frequencies(trait_freq) ).generate_from_frequencies(trait_freq)
# Create matplotlib figure # Create matplotlib figure

2
pyproject.toml
View File

@@ -6,6 +6,7 @@ readme = "README.md"
requires-python = ">=3.12" requires-python = ">=3.12"
dependencies = [ dependencies = [
"altair>=6.0.0", "altair>=6.0.0",
"imagehash>=4.3.1",
"marimo>=0.18.0", "marimo>=0.18.0",
"matplotlib>=3.10.8", "matplotlib>=3.10.8",
"modin[dask]>=0.37.1", "modin[dask]>=0.37.1",
@@ -14,6 +15,7 @@ dependencies = [
"openai>=2.9.0", "openai>=2.9.0",
"openpyxl>=3.1.5", "openpyxl>=3.1.5",
"pandas>=2.3.3", "pandas>=2.3.3",
"pillow>=11.0.0",
"polars>=1.37.1", "polars>=1.37.1",
"pyarrow>=23.0.0", "pyarrow>=23.0.0",
"pysqlite3>=0.6.0", "pysqlite3>=0.6.0",

105
utils.py
View File

@@ -6,6 +6,11 @@ import json
import re import re
import hashlib import hashlib
import os import os
from io import BytesIO
import imagehash
from PIL import Image
from plots import JPMCPlotsMixin from plots import JPMCPlotsMixin
@@ -124,16 +129,54 @@ def _calculate_file_sha1(file_path: Union[str, Path]) -> str:
return sha1.hexdigest() return sha1.hexdigest()
def _build_image_hash_map(root_dir: Union[str, Path]) -> dict: def _calculate_perceptual_hash(image_source: Union[str, Path, bytes]) -> str:
""" """
Recursively walk the directory and build a map of SHA1 hashes to file paths. Calculate perceptual hash of an image based on visual content.
Uses pHash (perceptual hash) which is robust against:
- Metadata differences
- Minor compression differences
- Small color/contrast variations
Args:
image_source: File path to image or raw image bytes.
Returns:
str: Hexadecimal string representation of the perceptual hash.
"""
if isinstance(image_source, bytes):
img = Image.open(BytesIO(image_source))
else:
img = Image.open(image_source)
# Convert to RGB if necessary (handles RGBA, P mode, etc.)
if img.mode not in ('RGB', 'L'):
img = img.convert('RGB')
# Use pHash (perceptual hash) - robust against minor differences
phash = imagehash.phash(img)
return str(phash)
def _build_image_hash_map(root_dir: Union[str, Path], use_perceptual_hash: bool = True) -> dict:
"""
Recursively walk the directory and build a map of image hashes to file paths.
Only includes common image extensions. Only includes common image extensions.
Args:
root_dir: Root directory to scan for images.
use_perceptual_hash: If True, uses perceptual hashing (robust against metadata
differences). If False, uses SHA1 byte hashing (exact match only).
Returns:
dict: Mapping of hash strings to file paths.
""" """
hash_map = {} hash_map = {}
valid_extensions = {'.png', '.jpg', '.jpeg', '.tiff', '.bmp', '.gif'} valid_extensions = {'.png', '.jpg', '.jpeg', '.tiff', '.bmp', '.gif'}
root = Path(root_dir) root = Path(root_dir)
print(f"Building image hash map from {root}...") hash_type = "perceptual" if use_perceptual_hash else "SHA1"
print(f"Building image hash map from {root} using {hash_type} hashing...")
count = 0 count = 0
for root_path, dirs, files in os.walk(root): for root_path, dirs, files in os.walk(root):
@@ -141,9 +184,12 @@ def _build_image_hash_map(root_dir: Union[str, Path]) -> dict:
file_path = Path(root_path) / file file_path = Path(root_path) / file
if file_path.suffix.lower() in valid_extensions: if file_path.suffix.lower() in valid_extensions:
try: try:
file_sha1 = _calculate_file_sha1(file_path) if use_perceptual_hash:
file_hash = _calculate_perceptual_hash(file_path)
else:
file_hash = _calculate_file_sha1(file_path)
# We store the absolute path for reference, but we might just need the path relative to project for alt text # We store the absolute path for reference, but we might just need the path relative to project for alt text
hash_map[file_sha1] = file_path hash_map[file_hash] = file_path
count += 1 count += 1
except Exception as e: except Exception as e:
print(f"Error hashing {file_path}: {e}") print(f"Error hashing {file_path}: {e}")
@@ -168,22 +214,25 @@ def _iter_picture_shapes(shapes):
yield shape yield shape
def update_ppt_alt_text(ppt_path: Union[str, Path], image_source_dir: Union[str, Path], output_path: Union[str, Path] = None): def update_ppt_alt_text(ppt_path: Union[str, Path], image_source_dir: Union[str, Path], output_path: Union[str, Path] = None, use_perceptual_hash: bool = True):
""" """
Updates the alt text of images in a PowerPoint presentation by matching Updates the alt text of images in a PowerPoint presentation by matching
their content (SHA1 hash) with images in a source directory. their content with images in a source directory.
Args: Args:
ppt_path (str/Path): Path to the PowerPoint file. ppt_path (str/Path): Path to the PowerPoint file.
image_source_dir (str/Path): Directory containing source images to match against. image_source_dir (str/Path): Directory containing source images to match against.
output_path (str/Path, optional): Path to save the updated presentation. output_path (str/Path, optional): Path to save the updated presentation.
If None, overwrites the input file. If None, overwrites the input file.
use_perceptual_hash (bool): If True (default), uses perceptual hashing which
matches images based on visual content (robust against metadata differences,
re-compression, etc.). If False, uses SHA1 byte hashing (exact file match only).
""" """
if output_path is None: if output_path is None:
output_path = ppt_path output_path = ppt_path
# 1. Build lookup map of {sha1: file_path} from the source directory # 1. Build lookup map of {hash: file_path} from the source directory
image_hash_map = _build_image_hash_map(image_source_dir) image_hash_map = _build_image_hash_map(image_source_dir, use_perceptual_hash=use_perceptual_hash)
# 2. Open Presentation # 2. Open Presentation
try: try:
@@ -193,6 +242,7 @@ def update_ppt_alt_text(ppt_path: Union[str, Path], image_source_dir: Union[str,
return return
updates_count = 0 updates_count = 0
unmatched_images = [] # Collect unmatched images to report at the end
slides = list(prs.slides) slides = list(prs.slides)
total_slides = len(slides) total_slides = len(slides)
@@ -204,11 +254,16 @@ def update_ppt_alt_text(ppt_path: Union[str, Path], image_source_dir: Union[str,
for shape in picture_shapes: for shape in picture_shapes:
try: try:
# shape.image.sha1 returns the SHA1 hash of the image blob # Get image hash based on selected method
current_sha1 = shape.image.sha1 if use_perceptual_hash:
# Use perceptual hash of the image blob for visual content matching
current_hash = _calculate_perceptual_hash(shape.image.blob)
else:
# Use SHA1 hash from python-pptx (exact byte match)
current_hash = shape.image.sha1
if current_sha1 in image_hash_map: if current_hash in image_hash_map:
original_path = image_hash_map[current_sha1] original_path = image_hash_map[current_hash]
# Generate Alt Text # Generate Alt Text
try: try:
@@ -253,17 +308,39 @@ def update_ppt_alt_text(ppt_path: Union[str, Path], image_source_dir: Union[str,
except Exception as e: except Exception as e:
print(f"Error updating alt text for {original_path}: {e}") print(f"Error updating alt text for {original_path}: {e}")
else:
shape_id = getattr(shape, 'shape_id', getattr(shape, 'id', 'Unknown ID'))
shape_name = shape.name if shape.name else f"Unnamed Shape (ID: {shape_id})"
hash_type = "pHash" if use_perceptual_hash else "SHA1"
unmatched_images.append({
'slide': i+1,
'shape_name': shape_name,
'hash_type': hash_type,
'hash': current_hash
})
except AttributeError: except AttributeError:
continue continue
except Exception as e: except Exception as e:
print(f"Error processing shape on slide {i+1}: {e}") print(f"Error processing shape on slide {i+1}: {e}")
# Print summary
print("\n" + "="*80)
if updates_count > 0: if updates_count > 0:
prs.save(output_path) prs.save(output_path)
print(f"Saved updated presentation to {output_path} with {updates_count} updates.") print(f"✓ Saved updated presentation to {output_path} with {updates_count} updates.")
else: else:
print("No images matched or required updates.") print("No images matched or required updates.")
# List unmatched images at the end
if unmatched_images:
print(f"\n⚠ {len(unmatched_images)} image(s) not found in source directory:")
for img in unmatched_images:
print(f" • Slide {img['slide']}: '{img['shape_name']}' ({img['hash_type']}: {img['hash']})")
else:
print("\n✓ All images matched successfully!")
print("="*80)
def extract_voice_label(html_str: str) -> str: def extract_voice_label(html_str: str) -> str:
""" """

131
uv.lock generated
View File

@@ -556,6 +556,21 @@ wheels = [
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[[package]] [[package]]
name = "importlib-resources" name = "importlib-resources"
version = "6.5.2" version = "6.5.2"
@@ -1404,6 +1419,7 @@ version = "0.1.0"
source = { virtual = "." } source = { virtual = "." }
dependencies = [ dependencies = [
{ name = "altair" }, { name = "altair" },
{ name = "imagehash" },
{ name = "marimo" }, { name = "marimo" },
{ name = "matplotlib" }, { name = "matplotlib" },
{ name = "modin", extra = ["dask"] }, { name = "modin", extra = ["dask"] },
@@ -1412,6 +1428,7 @@ dependencies = [
{ name = "openai" }, { name = "openai" },
{ name = "openpyxl" }, { name = "openpyxl" },
{ name = "pandas" }, { name = "pandas" },
{ name = "pillow" },
{ name = "polars" }, { name = "polars" },
{ name = "pyarrow" }, { name = "pyarrow" },
{ name = "pysqlite3" }, { name = "pysqlite3" },
@@ -1426,6 +1443,7 @@ dependencies = [
[package.metadata] [package.metadata]
requires-dist = [ requires-dist = [
{ name = "altair", specifier = ">=6.0.0" }, { name = "altair", specifier = ">=6.0.0" },
{ name = "imagehash", specifier = ">=4.3.1" },
{ name = "marimo", specifier = ">=0.18.0" }, { name = "marimo", specifier = ">=0.18.0" },
{ name = "matplotlib", specifier = ">=3.10.8" }, { name = "matplotlib", specifier = ">=3.10.8" },
{ name = "modin", extras = ["dask"], specifier = ">=0.37.1" }, { name = "modin", extras = ["dask"], specifier = ">=0.37.1" },
@@ -1434,6 +1452,7 @@ requires-dist = [
{ name = "openai", specifier = ">=2.9.0" }, { name = "openai", specifier = ">=2.9.0" },
{ name = "openpyxl", specifier = ">=3.1.5" }, { name = "openpyxl", specifier = ">=3.1.5" },
{ name = "pandas", specifier = ">=2.3.3" }, { name = "pandas", specifier = ">=2.3.3" },
{ name = "pillow", specifier = ">=11.0.0" },
{ name = "polars", specifier = ">=1.37.1" }, { name = "polars", specifier = ">=1.37.1" },
{ name = "pyarrow", specifier = ">=23.0.0" }, { name = "pyarrow", specifier = ">=23.0.0" },
{ name = "pysqlite3", specifier = ">=0.6.0" }, { name = "pysqlite3", specifier = ">=0.6.0" },
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