Aggregation step

01_Taguette-Pre-Process.py

@@ -1,6 +1,6 @@
 import marimo
 
-__generated_with = "0.18.0"
+__generated_with = "0.18.3"
 app = marimo.App(width="medium")
 
 

02_Taguette_Post-Process.py

@@ -0,0 +1,353 @@
+import marimo
+
+__generated_with = "0.18.3"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import marimo as mo
+    import pandas as pd
+    from pathlib import Path
+    from datetime import datetime
+
+    TAGUETTE_EXPORT_DIR = Path('./data/transcripts/taguette_results')
+    WORKING_DIR = Path('./data/processing/02_taguette_postprocess')
+
+    if not WORKING_DIR.exists():
+        WORKING_DIR.mkdir(parents=True)
+    if not TAGUETTE_EXPORT_DIR.exists():
+        TAGUETTE_EXPORT_DIR.mkdir(parents=True)
+    return TAGUETTE_EXPORT_DIR, WORKING_DIR, datetime, mo, pd
+
+
+@app.cell(hide_code=True)
+def _(TAGUETTE_EXPORT_DIR, mo):
+    mo.md(rf"""
+    # Step 1: Export All Highlights out of Taguette
+
+    1. Go to: http://taguette.tail44fa00.ts.net/project/1
+    2. Select 'Highlights' on left
+    3. Select 'See all hightlights'
+    4. Top right 'Export this view' > 'CSV'
+    5. Save to '{TAGUETTE_EXPORT_DIR}/all_tags.csv'
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    # Step 2: Import here for processing
+    """)
+    return
+
+
+@app.cell
+def _(pd):
+    all_tags_df = pd.read_csv('data/transcripts/taguette_results/all_tags.csv')
+    all_tags_df['_seq_id'] = range(len(all_tags_df))
+    all_tags_df.head(20)
+    return (all_tags_df,)
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    # Step 3: Process each 'Interview'
+    """)
+    return
+
+
+@app.cell
+def _(all_tags_df, mo):
+
+    interview_select = mo.ui.dropdown(
+        options=all_tags_df['document'].unique().tolist(),
+        label="Select Interview to Process",
+        full_width=True
+    )
+    interview_select
+    return (interview_select,)
+
+
+@app.cell
+def _(all_tags_df, interview_select):
+    # filter all_tags_df to only the document = file_dropdown.value
+    df = all_tags_df.loc[all_tags_df['document'] == interview_select.value].copy()
+    return (df,)
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ### Add `_context` column to track Voice / Character is being referred to per highlight
+    Create a new column 'context', which is defined by the last '_V-' or '_C-' tag seen in the 'tags' column', when moving row by row from top to bottom.
+
+    1. Iterates through the dataframe in document order (row by row)
+    2. Uses a set to track which highlight IDs we've already processed
+    3. When we encounter a new highlight ID for the first time, we process all its rows
+    4. Collects all _V- or _C- tags within that highlight
+    5. Assigns the context to all rows with that ID
+    6. This preserves document order and handles multi-tag highlights correctly
+
+
+    Example of challenging case:
+
+    | id  | document | tag                                | content | _seq_id | _context         |
+    |-----|-------------|------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|----------------------|
+    | 88  | P2 - Done   | _V-54                              | They I feel like they're like twins in that sense. Like, they both had this calming, like, calming voice that was smooth. It felt, like, but articulated and helpful, and, like, I felt reassured listening to them.                                                                                                                                                    | 117        | _V-54, _V-41         |
+    | 88  | P2 - Done   | _V-41                              | They I feel like they're like twins in that sense. Like, they both had this calming, like, calming voice that was smooth. It felt, like, but articulated and helpful, and, like, I felt reassured listening to them.                                                                                                                                                    | 118        | _V-54, _V-41         |
+    | 88  | P2 - Done   | VT - Human / Artificial            | They I feel like they're like twins in that sense. Like, they both had this calming, like, calming voice that was smooth. It felt, like, but articulated and helpful, and, like, I felt reassured listening to them.                                                                                                                                                    | 119        | _V-54, _V-41         |
+    | 88  | P2 - Done   | VT - Friendliness / Empathy        | They I feel like they're like twins in that sense. Like, they both had this calming, like, calming voice that was smooth. It felt, like, but articulated and helpful, and, like, I felt reassured listening to them.                                                                                                                                                    | 120        | _V-54, _V-41         |
+    """)
+    return
+
+
+@app.cell
+def _(df):
+    # First pass: identify context tags within each highlight group
+    df['_context'] = None
+    last_context = None
+    processed_ids = set()
+
+    # Process in document order
+    for idx, row in df.iterrows():
+        highlight_id = row['id']
+
+        # If we haven't processed this highlight yet
+        if highlight_id not in processed_ids:
+            processed_ids.add(highlight_id)
+
+            # Get all rows for this highlight
+            highlight_rows = df[df['id'] == highlight_id]
+
+            # Collect all context tags in this highlight
+            context_tags = []
+            for _, h_row in highlight_rows.iterrows():
+                tag = h_row.get('tag', '')
+                if '_V-' in tag or '_C-' in tag:
+                    context_tags.append(tag)
+
+            # If we found context tags, join them with comma
+            if context_tags:
+                context_tag = ', '.join(context_tags)
+                last_context = context_tag
+            else:
+                # If no context tag in this highlight, use the last context
+                context_tag = last_context
+
+            # Assign the context to all rows in this highlight
+            df.loc[df['id'] == highlight_id, '_context'] = context_tag
+
+    df
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Resolve multi-context rows (only VT- and CT- theme tags)
+
+    For rows that have multiple contexts (e.g., both _V-54 and _V-41)
+    - split these into separate rows for each context.
+    - Then mark these for 'manual_analysis'
+    """)
+    return
+
+
+@app.cell
+def _(df, pd):
+    # Expand rows that contain multiple contexts (comma-separated)
+    expanded_rows = []
+
+    for _, _row in df.iterrows():
+        context_value = _row['_context']
+        has_multiple = pd.notna(context_value) and ',' in str(context_value)
+
+        if has_multiple:
+            contexts = [c.strip() for c in str(context_value).split(',')]
+        else:
+            contexts = [context_value]
+
+        if has_multiple:
+            for ctx in contexts:
+                new_row = _row.copy()
+                new_row['_context'] = ctx
+                new_row['manual_analysis'] = True
+
+                if str(new_row['tag']).startswith(('VT -', 'CT -')):
+                    new_row['sentiment'] = None
+
+                expanded_rows.append(new_row)
+        else:
+            new_row = _row.copy()
+            new_row['_context'] = contexts[0]
+            new_row['manual_analysis'] = False
+            expanded_rows.append(new_row)
+
+    expanded_df_raw = pd.DataFrame(expanded_rows).reset_index(drop=True)
+
+
+    sentiment_df = expanded_df_raw.loc[
+        expanded_df_raw['tag'].str.startswith(('VT -', 'CT -'), na=False)
+    ].copy()
+
+    manual_rows = sentiment_df[sentiment_df['manual_analysis']]
+    if not manual_rows.empty:
+        print(
+            f"⚠️  {len(manual_rows)} rows were created from multi-context splits. "
+            "See next cell for manual review."
+        )
+    else:
+        print("✓ No multi-context rows found")
+    return (sentiment_df,)
+
+
+@app.cell
+def _(mo, sentiment_df):
+    # Filter for rows that need review. Manual analysis and the tag starts with 'VT -' or 'CT -'
+    rows_to_edit = sentiment_df[
+        (sentiment_df['manual_analysis'])
+    ]
+
+    # Create data editor for split rows
+    split_rows_editor = mo.ui.data_editor(
+        rows_to_edit
+    ).form(label="Update Sentiment / Manual Flag")
+    return rows_to_edit, split_rows_editor
+
+
+@app.cell(hide_code=True)
+def _(mo, rows_to_edit, split_rows_editor):
+    mo.vstack([
+        mo.md(f"""
+        ### ⚠️ Manual Review Required
+
+        **{len(rows_to_edit)} rows** were split from multi-context entries.
+        Please review them below:
+        1. Update the `sentiment` column (-1, 0, 1) for each row based on the specific context.
+        2. Uncheck `manual_analysis` when you are done reviewing a row.
+        3. Click **Submit** to apply changes.
+        """),
+        split_rows_editor
+    ])
+    return
+
+
+@app.cell
+def _(mo, split_rows_editor):
+    # Capture the edited manual-analysis rows for validation
+    mo.stop(split_rows_editor.value is None, mo.md("Submit your changes."))
+    reviewed_manual_rows = split_rows_editor.value
+
+    # Ensure all manual-analysis rows include a sentiment of -1, 0, or 1
+    if not reviewed_manual_rows.empty:
+        valid_sentiments = {-1, 0, 1}
+        needs_review = reviewed_manual_rows[
+            reviewed_manual_rows['manual_analysis']
+            & ~reviewed_manual_rows['sentiment'].isin(valid_sentiments)
+        ]
+        assert needs_review.empty, f"{len(needs_review)} manual-analysis rows missing sentiment -1/0/1"
+
+    print("✓ Manual-analysis rows have valid sentiment values")
+    return (reviewed_manual_rows,)
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    # Highlight Sentiment Analysis
+
+    For each row in the dataframe, analyze the sentiment of the 'content' regarding the respective tag. This should be done for all 'VT -' and 'CT -' tags, since these represent the 'VoiceThemes' and 'CharacterThemes' respectively. The results should be stored in a new 'sentiment' column.
+
+    Values to be used:
+    - Positive: +1
+    - Neutral: 0
+    - Negative: -1
+    """)
+    return
+
+
+@app.cell
+def _(sentiment_df):
+    # TODO: Implement sentiment analysis and add 'sentiment' column
+
+    # for now, create an empty sentiment column with randomized dummy values for testing
+    # only for 'VT -' and 'CT -' tags
+    import random
+
+    def dummy_sentiment_analysis(content, tag):
+        if tag.startswith('VT -') or tag.startswith('CT -'):
+            return random.choice([-1, 0, 1])  # Random sentiment for testing
+        return None
+
+    # Only run on rows without manual_analysis
+
+    sentiment_df['sentiment'] = sentiment_df[~sentiment_df['manual_analysis']].apply(lambda row: dummy_sentiment_analysis(row['content'], row['tag']), axis=1)
+
+    sentiment_df[~sentiment_df['manual_analysis']]
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Recombine
+    """)
+    return
+
+
+@app.cell
+def _(pd, reviewed_manual_rows, sentiment_df):
+    _static_analysis_rows = sentiment_df[~sentiment_df['manual_analysis']]
+    recombined_df = pd.concat([_static_analysis_rows, reviewed_manual_rows]).sort_values(by='_seq_id').reset_index(drop=True)
+
+    recombined_df
+    return (recombined_df,)
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    # Step 3: Process 'Other' tags
+
+    These need to be reviewed manually for interesting content
+    """)
+    return
+
+
+@app.cell
+def _(mo):
+    mo.md(r"""
+ 
+    """)
+    return
+
+
+@app.cell
+def _():
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    # Save to CSV
+    """)
+    return
+
+
+@app.cell
+def _(WORKING_DIR, datetime, interview_select, recombined_df):
+    # Save to CSV in working dir
+    timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
+    filename = WORKING_DIR / f"{interview_select.value.split(' ')[0]}_sentiments_{timestamp}.csv"
+    recombined_df.to_csv(filename, index=False)
+
+    print(f"✓ Saved processed data to '{filename}'")
+    return
+
+
+if __name__ == "__main__":
+    app.run()

03_Sentiment_Analysis.py

@@ -0,0 +1,195 @@
+import marimo
+
+__generated_with = "0.18.3"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import marimo as mo
+    import pandas as pd
+    from pathlib import Path
+
+    INPUT_DIR = Path("./data/processing/02_taguette_postprocess")
+    WORKING_DIR = Path('./data/processing/03_sentiment_analysis')
+
+    if not WORKING_DIR.exists():
+        WORKING_DIR.mkdir(parents=True)
+
+    return INPUT_DIR, Path, WORKING_DIR, mo, pd
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    # Load Sentiment CSV
+    """)
+    return
+
+
+@app.cell
+def _(INPUT_DIR, mo):
+    csv_files = list(INPUT_DIR.glob("*.csv"))
+    file_options = {f.stem: str(f) for f in csv_files}
+
+    sentiment_csv = mo.ui.dropdown(
+        options=file_options,
+        label="Select Sentiment CSV File",
+        full_width=True
+    )
+    sentiment_csv
+    return (sentiment_csv,)
+
+
+@app.cell
+def _(Path, pd, sentiment_csv):
+    input_csv_name = Path(sentiment_csv.value).stem
+    timestamp = input_csv_name.split('_')[-1]
+    doc = input_csv_name.split('_')[0]
+
+    sentiment_df = pd.read_csv(sentiment_csv.value)
+    sentiment_df
+    return doc, sentiment_df, timestamp
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    # Phase 1: Individual interview analysis
+    - Create sentiment matrices for each interview (document)
+    - Save the intermediate results to file in the `WORKING_DIR`
+    """)
+    return
+
+
+@app.cell
+def _(document_name, pd):
+    import numpy as np
+
+    def create_sentiment_matrix(doc_df, column_prefix='VT - |CT - ', row_prefix='_V-|_C-'):
+        """
+        Create a sentiment matrix for a specific document.
+
+        Parameters:
+        - df: DataFrame with columns ['document', 'tag', '_context', 'sentiment']
+        - document_name: Name of the document to filter by
+
+        Returns:
+        - DataFrame representing the sentiment matrix
+        """
+
+        # Filter for rows where the tag matches the sentiment prefixes (VT-/CT-)
+        sentiment_rows = doc_df[
+            doc_df['tag'].str.contains(column_prefix, na=False)
+        ].copy()
+
+        if sentiment_rows.empty:
+            print(f"No sentiment data found for document: {document_name}")
+            return pd.DataFrame()
+
+        # Filter for rows with valid Voice/Character context
+        valid_rows = sentiment_rows[
+            sentiment_rows['_context'].notna() & 
+            (sentiment_rows['_context'].str.contains(row_prefix, na=False))
+        ].copy()
+
+        if valid_rows.empty:
+            print(f"No Voice/Character context found for document: {document_name}")
+            return pd.DataFrame()
+
+        # Create aggregation: group by Voice/Character (_context) and Theme (tag)
+        # Sum sentiment scores for each combination
+        matrix_data = valid_rows.groupby(['_context', 'tag'])['sentiment'].sum().reset_index()
+
+        # Pivot to create the matrix
+        matrix = matrix_data.pivot(index='_context', columns='tag', values='sentiment')
+
+        # # Convert to integers for cleaner display
+        # matrix = matrix.astype(int)
+
+        return matrix
+    return (create_sentiment_matrix,)
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Step 1.1: Voice Sample vs. Theme Sentiment Matrix
+
+    For each interview (document), create a matrix where:
+    - Rows represent the different Voices (based on '_V-' tags)
+    - Columns represent the different VoiceThemes(based on 'VT -' tags)
+    - Each cell contains the aggregated sentiment score (sum) for that Voice/Theme combination
+    """)
+    return
+
+
+@app.cell
+def _(create_sentiment_matrix, sentiment_df):
+    voice_matrix = create_sentiment_matrix(sentiment_df, column_prefix='VT - ', row_prefix='_V-')
+    voice_matrix
+    return (voice_matrix,)
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    SAVE TO CSV
+    """)
+    return
+
+
+@app.cell
+def _(WORKING_DIR, doc, timestamp, voice_matrix):
+    # Save to CSV
+    voice_filename = WORKING_DIR / f"{doc}_voice_theme_matrix_{timestamp}.csv"
+
+    voice_matrix.to_csv(voice_filename)
+
+    print(f"Saved to '{voice_filename}'")
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Step 1.2: Character Sample vs. Theme Sentiment Matrix
+
+    For each interview (document), create a matrix where:
+    - Rows represent the different Characters (based on  '_C-' tags)
+    - Columns represent the different CharacterThemes (based on 'CT -' tags)
+    - Each cell contains the aggregated sentiment score (sum) for that Character/Theme combination
+    """)
+    return
+
+
+@app.cell
+def _(create_sentiment_matrix, sentiment_df):
+    character_matrix = create_sentiment_matrix(sentiment_df, column_prefix='CT - ', row_prefix='_C-')
+    character_matrix
+    return (character_matrix,)
+
+
+@app.cell
+def _(WORKING_DIR, character_matrix, doc, timestamp):
+    # Save to CSV
+    character_filename = WORKING_DIR / f"{doc}_character_theme_matrix_{timestamp}.csv"
+
+    character_matrix.to_csv(character_filename)
+
+    print(f"Saved to '{character_filename}'")
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Step 1.3: Chase Brand Sentiment
+
+    TODO: not sure we have enough supporting data for this yet
+    """)
+    return
+
+
+if __name__ == "__main__":
+    app.run()

04_Sentiment_Aggregation.py

@@ -0,0 +1,86 @@
+import marimo
+
+__generated_with = "0.18.3"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import marimo as mo
+    import pandas as pd
+    from pathlib import Path
+
+    INPUT_DIR = Path("./data/processing/03_sentiment_analysis")
+    WORKING_DIR = Path('./data/processing/04_sentiment_aggregation')
+
+    if not WORKING_DIR.exists():
+        WORKING_DIR.mkdir(parents=True)
+    return INPUT_DIR, mo, pd
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    # Voices
+    """)
+    return
+
+
+@app.cell
+def _(INPUT_DIR, mo):
+    voice_csv_files = list(INPUT_DIR.glob("*voice*.csv"))
+    file_options = {f.stem: str(f) for f in voice_csv_files}
+
+    voice_multiselect = mo.ui.multiselect(options=file_options, label="Select Voice CSV Files for Aggregation")
+    voice_multiselect
+    return (voice_multiselect,)
+
+
+@app.cell
+def _(mo, voice_multiselect):
+    mo.hstack([voice_multiselect, mo.md(f"Has value: {voice_multiselect.value}")])
+    return
+
+
+@app.cell
+def _(pd, voice_multiselect):
+    # Load all voice CSV files and aggregate them so that each row-column pair is summed
+    KEY_COL = "_context"
+
+    def _read_voice_csv(path: str) -> pd.DataFrame:
+        df = pd.read_csv(path).set_index(KEY_COL)
+        df = df.apply(pd.to_numeric, errors="coerce")
+        return df
+
+    def aggregate_voice_data(files: list[str]) -> pd.DataFrame:
+        if not files:
+            return pd.DataFrame()
+
+        master = _read_voice_csv(files[0])
+        for path in files[1:]:
+            master = master.add(_read_voice_csv(path), fill_value=0)
+
+        return master.reset_index()
+
+    master_df = aggregate_voice_data(voice_multiselect.value)
+    master_df
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    # Characters
+    """)
+    return
+
+
+@app.cell
+def _(INPUT_DIR):
+    char_csv_files = list(INPUT_DIR.glob("*character*.csv"))
+    char_csv_files
+    return
+
+
+if __name__ == "__main__":
+    app.run()

pyproject.toml

@@ -8,6 +8,7 @@ dependencies = [
     "marimo>=0.18.0",
     "numpy>=2.3.5",
     "ollama>=0.6.1",
+    "openai>=2.9.0",
     "pandas>=2.3.3",
     "pyzmq>=27.1.0",
     "requests>=2.32.5",

uv.lock

@@ -229,6 +229,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/84/d0/205d54408c08b13550c733c4b85429e7ead111c7f0014309637425520a9a/deprecated-1.3.1-py2.py3-none-any.whl", hash = "sha256:597bfef186b6f60181535a29fbe44865ce137a5079f295b479886c82729d5f3f", size = 11298, upload-time = "2025-10-30T08:19:00.758Z" },
 ]
 
+[[package]]
+name = "distro"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fc/f8/98eea607f65de6527f8a2e8885fc8015d3e6f5775df186e443e0964a11c3/distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed", size = 60722, upload-time = "2023-12-24T09:54:32.31Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" },
+]
+
 [[package]]
 name = "docutils"
 version = "0.22.3"
@@ -353,6 +362,7 @@ dependencies = [
     { name = "marimo" },
     { name = "numpy" },
     { name = "ollama" },
+    { name = "openai" },
     { name = "pandas" },
     { name = "pyzmq" },
     { name = "requests" },
@@ -364,6 +374,7 @@ requires-dist = [
     { name = "marimo", specifier = ">=0.18.0" },
     { name = "numpy", specifier = ">=2.3.5" },
     { name = "ollama", specifier = ">=0.6.1" },
+    { name = "openai", specifier = ">=2.9.0" },
     { name = "pandas", specifier = ">=2.3.3" },
     { name = "pyzmq", specifier = ">=27.1.0" },
     { name = "requests", specifier = ">=2.32.5" },
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