ppt function to replace images

This commit is contained in:
2026-01-29 15:36:34 +01:00
parent bc12df28a5
commit 3ee25f9e33
6 changed files with 326 additions and 96 deletions

View File

@@ -29,18 +29,6 @@ def _():
@app.cell(hide_code=True)
def _(mo):
mo.outline(label="Table of Contents")
return
@app.cell
def _():
# Select Dataset
return
@app.cell
def _(mo):
file_browser = mo.ui.file_browser(
initial_path="./data/exports", multiple=False, restrict_navigation=True, filetypes=[".csv"], label="Select 'Labels' File"
@@ -59,12 +47,6 @@ def _(Path, file_browser, mo):
return QSF_FILE, RESULTS_FILE
@app.cell
def _(RESULTS_FILE, mo):
mo.stop(not RESULTS_FILE.name.lower().endswith('labels.csv'), mo.md("**⚠️ Make sure you select a `_Labels.csv` file above**"))
return
@app.cell
def _(JPMCSurvey, QSF_FILE, RESULTS_FILE, mo):
S = JPMCSurvey(RESULTS_FILE, QSF_FILE)
@@ -76,12 +58,6 @@ def _(JPMCSurvey, QSF_FILE, RESULTS_FILE, mo):
@app.cell
def _():
# check_straight_liners(S.get_ss_green_blue(data_all)[0])
return
@app.cell(hide_code=True)
def _(Path, RESULTS_FILE, mo):
mo.md(f"""
@@ -112,6 +88,26 @@ def _(check_progress, data_all, duration_validation, mo):
return
@app.cell
def _(mo):
mo.md(r"""
### ⚠️ ToDo: "straight-liner" detection and removal
""")
return
@app.cell
def _(mo):
mo.md(r"""
---
# Data Filter
Use to select a subset of the data for the following analysis
""")
return
@app.cell(hide_code=True)
def _(S, mo):
filter_form = mo.md('''
@@ -147,7 +143,7 @@ def _(S, mo):
return (filter_form,)
@app.cell(hide_code=True)
@app.cell
def _(S, data_all, filter_form, mo):
mo.stop(filter_form.value is None, mo.md("**Please submit filter above to proceed**"))
_d = S.filter_data(data_all, age=filter_form.value['age'], gender=filter_form.value['gender'], income=filter_form.value['income'], ethnicity=filter_form.value['ethnicity'], consumer=filter_form.value['consumer'])
@@ -173,6 +169,20 @@ def _(S, data, mo):
return (char_rank,)
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
""")
return
@app.cell(hide_code=True)
def _():
# char_rank = S.get_character_ranking(data)[0]
return
@app.cell
def _(S, char_rank, mo):
mo.md(f"""
@@ -218,6 +228,13 @@ def _(S, data, mo):
return (v_18_8_3,)
@app.cell(hide_code=True)
def _():
# print(v_18_8_3.head())
return
@app.cell(hide_code=True)
def _(S, mo, v_18_8_3):
mo.md(f"""
@@ -240,7 +257,7 @@ def _(S, mo, v_18_8_3):
return
@app.cell(hide_code=True)
@app.cell
def _(S, calculate_weighted_ranking_scores, data):
top3_voices = S.get_top_3_voices(data)[0]
top3_voices_weighted = calculate_weighted_ranking_scores(top3_voices)
@@ -284,6 +301,11 @@ def _(S, mo, top3_voices):
@app.cell
def _():
return
@app.cell(hide_code=True)
def _(S, data, mo, utils):
ss_or, choice_map_or = S.get_ss_orange_red(data)
ss_gb, choice_map_gb = S.get_ss_green_blue(data)
@@ -322,12 +344,12 @@ def _(S, mo, pl, ss_long):
return
@app.cell(hide_code=True)
@app.cell
def _():
return
@app.cell
@app.cell(hide_code=True)
def _(S, data, mo):
vscales = S.get_voice_scale_1_10(data)[0]
# plot_average_scores_with_counts(vscales, x_label='Voice', width=1000)
@@ -337,7 +359,7 @@ def _(S, data, mo):
return (vscales,)
@app.cell
@app.cell(hide_code=True)
def _(S, mo, vscales):
mo.md(f"""
### How does each voice score on a scale from 1-10?
@@ -352,7 +374,7 @@ def _():
return
@app.cell(hide_code=True)
@app.cell
def _(mo):
mo.md(r"""
@@ -360,7 +382,7 @@ def _(mo):
return
@app.cell(hide_code=True)
@app.cell
def _(mo):
mo.md(r"""
@@ -400,7 +422,7 @@ def _(choice_map, mo, ss_all, utils, vscales):
return df_style, joined_df
@app.cell
@app.cell(hide_code=True)
def _(S, SPEAKING_STYLES, joined_df, mo):
_content = """### Total Results
@@ -431,34 +453,18 @@ def _(mo):
- [ ] 4 correlation diagrams considering each speaking style (4) and all female voice results.
- [ ] 4 correlation diagrams considering each speaking style (4) and all male voice results.
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
## Correlations Voice Speaking Styles <-> Voice Ranking Points
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
Lets show how scoring better on these speaking styles correlates (or not) with better Vocie Ranking results. For each speaking style we show how the traits in these speaking styles correlate with voice ranking points. This gives us a total of 4 correlation diagrams.
Example for speaking style green:
- Trait 1: Friendly | Conversational | Down-to-earth
- Trait 2: Approachable | Familiar | Warm
- Trait 3: Optimistic | Benevolent | Positive | Appreciative
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
### Total Results
- [ ] 4 correlation diagrams
@@ -466,7 +472,31 @@ def _(mo):
return
@app.cell
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
""")
return
@app.cell(hide_code=True)
def _(S, SPEAKING_STYLES, df_style, mo, top3_voices, utils):
df_ranking = utils.process_voice_ranking_data(top3_voices)
joined = df_style.join(df_ranking, on=['_recordId', 'Voice'], how='inner')
@@ -490,7 +520,7 @@ def _(S, SPEAKING_STYLES, df_style, mo, top3_voices, utils):
return
@app.cell(hide_code=True)
@app.cell
def _(mo):
mo.md(r"""
### Female / Male Voices considered seperately
@@ -501,49 +531,5 @@ def _(mo):
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
## Correlation Heatmap all evaluations <-> voice acoustic data
- [ ] Heatmap for male voices
- [ ] Heatmap for female voices
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
## Most Prominent Character Personality Traits
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
The last question of the survey is about traits for the described character's personality. For each Character personality, we want to display the 8 most chosen character personality traits. This will give us a total of 4 diagrams, one for each character personality included in the test.
- [ ] Bank Teller
- [ ] Familiar Friend
- [ ] The Coach
- [ ] Personal Assistant
""")
return
@app.cell
def _(mo):
mo.md(r"""
---
# Results per subgroup
Use the dropdown selector at the top to filter the data and generate all the plots again
""")
return
if __name__ == "__main__":
app.run()

63
03_ppt_replace_images.py Normal file
View File

@@ -0,0 +1,63 @@
import marimo
__generated_with = "0.19.2"
app = marimo.App(width="medium")
with app.setup:
import marimo as mo
from pathlib import Path
import utils
@app.cell
def _():
PPT_FILE = Path('data/Presentation.pptx')
UPDATED_PPT_FILE = Path('data/Updated_Presentation.pptx')
return PPT_FILE, UPDATED_PPT_FILE
@app.cell
def _():
IMAGE_FILE = Path('figures/OneDrive_2026-01-28/Cons-Early_Professional/cold_distant_approachable_familiar_warm.png')
return (IMAGE_FILE,)
@app.function
def image_alt_text_converter(fpath):
"""convert image file path to alt text
"""
if not isinstance(fpath, Path):
fpath = Path(fpath)
fparts = fpath.parts
assert fparts[0] == 'figures', "Image file path must start with 'figures'"
return Path('/'.join(fparts[2:])).as_posix()
@app.cell
def _(IMAGE_FILE):
img_alt_txt = image_alt_text_converter(IMAGE_FILE)
img_alt_txt
return (img_alt_txt,)
@app.cell
def _(IMAGE_FILE, PPT_FILE, UPDATED_PPT_FILE, img_alt_txt):
utils.pptx_replace_named_image(
presentation_path=PPT_FILE,
target_tag=img_alt_txt,
new_image_path=IMAGE_FILE,
save_path=UPDATED_PPT_FILE)
return
@app.cell
def _(P):
print(P.slides[10])
return
if __name__ == "__main__":
app.run()

View File

@@ -496,7 +496,7 @@ class JPMCPlotsMixin:
# Bar chart
bars = alt.Chart(weighted_df).mark_bar(color=color).encode(
x=alt.X('Character:N', title=x_label),
x=alt.X('Character:N', title=x_label, sort='-y'),
y=alt.Y('Weighted Score:Q', title=y_label),
tooltip=[
alt.Tooltip('Character:N'),

View File

@@ -17,6 +17,7 @@ dependencies = [
"polars>=1.37.1",
"pyarrow>=23.0.0",
"pysqlite3>=0.6.0",
"python-pptx>=1.0.2",
"pyzmq>=27.1.0",
"requests>=2.32.5",
"taguette>=1.5.1",

View File

@@ -8,6 +8,89 @@ from plots import JPMCPlotsMixin
import marimo as mo
from pptx import Presentation
from pptx.enum.shapes import MSO_SHAPE_TYPE
def pptx_replace_named_image(presentation_path, target_tag, new_image_path, save_path):
"""
Finds and replaces specific images in a PowerPoint presentation while
preserving their original position, size, and aspect ratio.
This function performs a 'surgical' replacement: it records the coordinates
of the existing image, removes it from the slide's XML, and inserts a
new image into the exact same bounding box. It identifies the target
image by searching for a specific string within the Shape Name
(Selection Pane) or Alt Text.
Args:
presentation_path (str): The file path to the source .pptx file.
target_tag (str): The unique identifier to look for (e.g., 'HERO_IMAGE').
This is case-sensitive and checks both the shape name and alt text.
new_image_path (str): The file path to the new image (PNG, JPG, etc.).
save_path (str): The file path where the modified presentation will be saved.
Returns:
None: Saves the file directly to the provided save_path.
Raises:
FileNotFoundError: If the source presentation or new image is not found.
PermissionError: If the save_path is currently open or locked.
"""
prs = Presentation(presentation_path)
for i, slide in enumerate(prs.slides):
# Iterate over a list copy of shapes to safely modify the slide during iteration
print(f"Processing Slide {i + 1}...")
print(f"Total Shapes: {len(slide.shapes)} shapes")
for shape in list(slide.shapes):
print(f"Checking shape: {shape.name} of type {shape.shape_type}...")
shape_name = shape.name or ""
alt_text = ""
# More robust strategy: Check for alt text in ANY valid element property
# This allows replacing Pictures, Placeholders, GraphicFrames, etc.
try:
# Check for common property names used by python-pptx elements to store non-visual props
# nvPicPr (Picture), nvSpPr (Shape/Placeholder), nvGrpSpPr (Group),
# nvGraphicFramePr (GraphicFrame), nvCxnSpPr (Connector)
nvPr = None
for attr in ['nvPicPr', 'nvSpPr', 'nvGrpSpPr', 'nvGraphicFramePr', 'nvCxnSpPr']:
if hasattr(shape._element, attr):
nvPr = getattr(shape._element, attr)
break
if nvPr is not None and hasattr(nvPr, 'cNvPr'):
alt_text = nvPr.cNvPr.get("descr", "")
except Exception:
pass
print(f"Alt Text for shape '{shape_name}': {alt_text}")
if target_tag in shape_name or target_tag in alt_text:
print(f"Found it! Replacing {shape_name}...")
try:
# Record coordinates
left, top, width, height = shape.left, shape.top, shape.width, shape.height
# Remove old shape
old_element = shape._element
old_element.getparent().remove(old_element)
# Add new image at the same spot
slide.shapes.add_picture(str(new_image_path), left, top, width, height)
except AttributeError:
print(f"Could not replace {shape_name} - might be missing dimensions.")
else:
print(f"Skipping shape '{shape_name}' with alt text '{alt_text}'")
prs.save(save_path)
print(f"Successfully saved to {save_path}")
def extract_voice_label(html_str: str) -> str:
"""
Extract voice label from HTML string and convert to short format.

97
uv.lock generated
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