final tweaks

.gitignore

@@ -13,4 +13,5 @@ __pycache__/
 
 data/
 docker-volumes/
-logs/
+logs/
+

02-B_WordClouds.py

@@ -22,24 +22,27 @@ def _():
     tqdm.pandas()
 
 
-    client, _models = connect_qumo_ollama(OLLAMA_LOCATION, print_models=False)
-
     TAGUETTE_EXPORT_DIR = Path('./data/processing/02_taguette_export')
     WORKING_DIR = Path('./data/processing/02-b_WordClouds')
+    VOICE_EXCLUDE_KEYWORDS_FILE = WORKING_DIR / 'voice_excl_keywords.txt'
 
     if not WORKING_DIR.exists():
         WORKING_DIR.mkdir(parents=True)
     if not TAGUETTE_EXPORT_DIR.exists():
         TAGUETTE_EXPORT_DIR.mkdir(parents=True)
 
-    model_select = mo.ui.dropdown(
-        options=_models,
-        value=_models[0],
-        label="Select Ollama Model to use",
-        searchable=True,
+    if not VOICE_EXCLUDE_KEYWORDS_FILE.exists():
+        VOICE_EXCLUDE_KEYWORDS_FILE.touch()
+
+    return (
+        OLLAMA_LOCATION,
+        TAGUETTE_EXPORT_DIR,
+        VOICE_EXCLUDE_KEYWORDS_FILE,
+        WORKING_DIR,
+        connect_qumo_ollama,
+        mo,
+        pd,
     )
-    model_select
-    return TAGUETTE_EXPORT_DIR, WORKING_DIR, client, mo, model_select, pd
 
 
 @app.cell(hide_code=True)
@@ -116,7 +119,7 @@ def _(all_tags_df, mo):
     return (tag_select,)
 
 
-@app.cell
+@app.cell(hide_code=True)
 def _(WORKING_DIR, all_tags_df, mo, tag_select):
     mo.stop(not tag_select.value, mo.md("Select tag to continue"))
 
@@ -139,7 +142,7 @@ def _(WORKING_DIR, all_tags_df, mo, tag_select):
 
     # filter all_tags_df to only the document = file_dropdown.value
     tags_df = all_tags_df.loc[all_tags_df['tag'] == tag_select.value].copy()
-    tags_df
+    tags_df.head()
     return (
         KEYWORDS_FPATH,
         KEYWORD_FREQ_FPATH,
@@ -151,44 +154,65 @@ def _(WORKING_DIR, all_tags_df, mo, tag_select):
 
 
 @app.cell(hide_code=True)
-def _(mo):
-    mo.md(r"""
-    # 4) Keyword extraction
+def _(KEYWORD_FREQ_FPATH, mo):
+    mo.md(rf"""
+    # 4) Keyword extraction {'(skippable, see 4b)' if KEYWORD_FREQ_FPATH.exists() else '(Required)'}
     """)
     return
 
 
+@app.cell(hide_code=True)
+def _(OLLAMA_LOCATION, connect_qumo_ollama, mo):
+    try:
+        client, _models = connect_qumo_ollama(OLLAMA_LOCATION, print_models=False)
+        model_select = mo.ui.dropdown(
+            options=_models,
+            value=_models[0],
+            label="Select Ollama Model to use",
+            searchable=True,
+        )
+    except Exception as e:
+        mo.md(f"Error connecting to Ollama server at `{OLLAMA_LOCATION}`: {e}")
+        model_select = None
+        client = None
+
+    model_select
+    return client, model_select
+
+
 @app.cell
-def _(mo, start_processing_btn, tag_select):
-    mo.stop(not tag_select.value, mo.md("Select tag to continue"))
+def _(mo, model_select, start_processing_btn, tag_select):
+    mo.stop(not tag_select.value or model_select is None, mo.md("Select tag to continue"))
 
     start_processing_btn
     return
 
 
-@app.cell
+@app.cell(hide_code=True)
 def _(client, mo, model_select, pd, start_processing_btn, tags_df):
     from utils import ollama_keyword_extraction, worker_extraction
     # Wait for start processing button
     mo.stop(not start_processing_btn.value, "Click button above to start processing")
 
+    if client is not None:
+        df = tags_df
+        # Run keyword extraction
 
-    df = tags_df
-    # Run keyword extraction
-
-    df['keywords'] = df.progress_apply(
-        lambda row: pd.Series(ollama_keyword_extraction(
-            content=row['content'], 
-            tag=row['tag'], 
-            client=client, 
-            model=model_select.value
-        )),
-        axis=1
-    )
+        df['keywords'] = df.progress_apply(
+            lambda row: pd.Series(ollama_keyword_extraction(
+                content=row['content'], 
+                tag=row['tag'], 
+                client=client, 
+                model=model_select.value
+            )),
+            axis=1
+        )
+    else:
+        mo.md("Ollama client not available, See 4b) for loading data from xlsx.")
     return (df,)
 
 
-@app.cell
+@app.cell(hide_code=True)
 def _(KEYWORDS_FPATH, KEYWORD_FREQ_FPATH, df, mo, pd, start_processing_btn):
     mo.stop(not start_processing_btn.value, "Click button above to process first")
 
@@ -232,26 +256,37 @@ def _(KEYWORDS_FPATH, KEYWORD_FREQ_FPATH, df, mo, pd, start_processing_btn):
 
 
 @app.cell(hide_code=True)
-def _(mo):
-    mo.md(r"""
-    # 4b) [optional] Load data from `keyword_frequencies_*.xlsx`
+def _(KEYWORD_FREQ_FPATH, mo):
+    mo.md(rf"""
+    # 4b) [optional] Load data from `keyword_frequencies_{KEYWORD_FREQ_FPATH.name}`
     """)
     return
 
 
 @app.cell(hide_code=True)
-def _(KEYWORD_FREQ_FPATH, mo):
+def _(KEYWORD_FREQ_FPATH, mo, start_processing_btn):
+    if start_processing_btn is not None: # Triggers re-execution of this cell when keyword extraction completes
+        pass
+
+    
     load_existing_btn = None
     if KEYWORD_FREQ_FPATH.exists():
-        load_existing_btn = mo.ui.run_button(label=f"Load keywords from `{KEYWORD_FREQ_FPATH.name}`")
+        load_existing_btn = mo.ui.run_button(label=f"Load `{KEYWORD_FREQ_FPATH.name}`", kind='warn')
 
     load_existing_btn
     return (load_existing_btn,)
 
 
 @app.cell(hide_code=True)
-def _(KEYWORD_FREQ_FPATH, freq_df, load_existing_btn, pd):
-    if load_existing_btn.value:
+def _(
+    KEYWORD_FREQ_FPATH,
+    VOICE_EXCLUDE_KEYWORDS_FILE,
+    freq_df,
+    load_existing_btn,
+    pd,
+    tag_select,
+):
+    if load_existing_btn is not None and load_existing_btn.value:
         _fdf = pd.read_excel(KEYWORD_FREQ_FPATH, engine='openpyxl')
 
         # Drop nan rows if any
@@ -259,11 +294,23 @@ def _(KEYWORD_FREQ_FPATH, freq_df, load_existing_btn, pd):
         _fdf.sort_values(by='frequency', ascending=False, inplace=True)
         _fdf.reset_index(drop=True, inplace=True)
         print(f"Loaded `{KEYWORD_FREQ_FPATH}` successfully.")
-
+        
         frequency_df = _fdf
 
     else:
         frequency_df = freq_df
+
+    if tag_select.value.startswith('V'):
+        # Read exclusion list
+        excl_kw = []
+        with VOICE_EXCLUDE_KEYWORDS_FILE.open('r') as _f:
+            for line in _f:
+                excl_kw.append(line.strip())
+
+        _drop_idx = frequency_df[frequency_df['keyword'].isin(excl_kw)].index
+
+        frequency_df.drop(index=_drop_idx, inplace=True, axis=0)
+        print(f"Dropped {len(_drop_idx)} keywords automatically")
     return (frequency_df,)
 
 
@@ -305,30 +352,6 @@ def _(mo):
     return (min_freq_select,)
 
 
-@app.cell(hide_code=True)
-def _(freq_df, frequency_df, min_freq_select, mo):
-    mo.stop('keyword' not in freq_df.columns, "Waiting for keyword extraction to finish")
-
-    MIN_FREQ = min_freq_select.value
-
-    freq_df_filtered = frequency_df.loc[freq_df['frequency'] >= MIN_FREQ]
-
-    freq_df_filtered.reset_index(drop=True, inplace=True)
-
-    keyword_freq_filtered = freq_df_filtered.set_index('keyword')['frequency'].to_dict()
-
-    table_selection = mo.ui.table(freq_df_filtered, page_size=50)
-    table_selection
-
-    # keyword_freq_filtered = {kw: freq for kw, freq in keyword_freq.items() if freq >= MIN_FREQ}
-
-    # # create list of keywords sorted by their frequencies. only store the keyword
-    # sorted_keywords = sorted(keyword_freq_filtered.items(), key=lambda x: x[1], reverse=True)
-    # sorted_keywords_list = [f"{kw}:{freq}" for kw, freq in sorted_keywords]
-    # sorted_keywords_list
-    return (keyword_freq_filtered,)
-
-
 @app.cell(hide_code=True)
 def _(mo, tag_select):
     mo.md(rf"""
@@ -349,7 +372,80 @@ def _(mo, tag_select):
     return
 
 
-@app.cell
+@app.cell(hide_code=True)
+def _(frequency_df, min_freq_select, mo):
+    mo.stop('keyword' not in frequency_df.columns, "Waiting for keyword extraction to finish")
+
+    MIN_FREQ = min_freq_select.value
+
+    _freq_df_filtered = frequency_df.loc[frequency_df['frequency'] >= MIN_FREQ].copy()
+
+    table_selection = mo.ui.table(_freq_df_filtered, page_size=50)
+    table_selection
+
+    return MIN_FREQ, table_selection
+
+
+@app.cell(hide_code=True)
+def _(mo, table_selection):
+    remove_rows_btn = None
+    if len(table_selection.value) >0 :
+        remove_rows_btn = mo.ui.run_button(label="Click to remove selected keywords and update xlsx")
+
+    remove_rows_btn
+    return (remove_rows_btn,)
+
+
+@app.cell(hide_code=True)
+def _(
+    KEYWORD_FREQ_FPATH,
+    VOICE_EXCLUDE_KEYWORDS_FILE,
+    frequency_df,
+    mo,
+    remove_rows_btn,
+    table_selection,
+    tag_select,
+):
+    _s = None
+    if remove_rows_btn is not None and remove_rows_btn.value:
+        # get selected rows
+        selected_rows = table_selection.value
+        if len(selected_rows) >0 :
+            rows_to_drop = table_selection.value.index.tolist()
+            try:
+                if tag_select.value.startswith('V'):
+                    # append values to an VoiceKeywordsExclusion file (txt file just a list of keywords)
+                    exclude_keywords = frequency_df.loc[rows_to_drop, 'keyword'].to_list()
+
+                    with VOICE_EXCLUDE_KEYWORDS_FILE.open('w') as f:
+                        for _kw in exclude_keywords:
+                            f.write(_kw + '\n')
+                
+
+                
+                frequency_df.drop(index=rows_to_drop, inplace=True, axis=0)
+
+                
+            
+            except KeyError:
+                _s = mo.callout("GO BACK TO STEP 4b) and reload data to continue refining the dataset.", kind='warn')
+            else:
+                # Save updated frequencies back to xlsx
+                frequency_df.to_excel(
+                    KEYWORD_FREQ_FPATH,
+                    index=False
+                )
+            
+                print(f"Updated keyword frequencies saved to: `{KEYWORD_FREQ_FPATH}`")
+
+            # mo.callout(f"Updated keyword frequencies saved to: `{KEYWORD_FREQ_FPATH}`", kind="success")
+            _s = mo.callout("GO BACK TO STEP 4b) and reload data before continuing.", kind='warn')
+
+    _s
+    return
+
+
+@app.cell(hide_code=True)
 def _():
     IGNORE_WORDS = {
         'chase as a brand': [
@@ -384,11 +480,13 @@ def _(mo):
     canvas_size = (1200, 800)
 
     logo_switch = mo.ui.switch(label="Include Chase Logo", value=False)
-    return buffer, canvas_size, logo_switch
+
+    n_words = mo.ui.slider(start=10, stop=200, step=1, value=100, debounce=True, show_value=True, label="Max number of words in WordCloud")
+    return buffer, canvas_size, logo_switch, n_words
 
 
 @app.cell(hide_code=True)
-def _(logo_switch, mo):
+def _(logo_switch, mo, n_words):
     run_wordcloud_btn = mo.ui.run_button(label="Generate WordCloud")
 
     mo.vstack([
@@ -399,7 +497,7 @@ def _(logo_switch, mo):
 
         When satisfied with the result, click 'Save WordCloud to File' to save the image."""),
         mo.md('---'),
-        mo.hstack([logo_switch, run_wordcloud_btn], align='center', justify='space-around')]
+        mo.hstack([logo_switch, n_words, run_wordcloud_btn], align='center', justify='space-around')]
     )
     return (run_wordcloud_btn,)
 
@@ -409,13 +507,15 @@ def _(
     IGNORE_WORDS,
     Image,
     ImageDraw,
+    MIN_FREQ,
     WordCloud,
     blue_color_func,
     buffer,
     canvas_size,
-    keyword_freq_filtered,
+    frequency_df,
     logo_switch,
     mo,
+    n_words,
     np,
     plt,
     run_wordcloud_btn,
@@ -424,6 +524,12 @@ def _(
     if run_wordcloud_btn.value:
         pass
 
+    freq_df_filtered = frequency_df.loc[frequency_df['frequency'] >= MIN_FREQ].copy()
+
+    # freq_df_filtered.reset_index(drop=True, inplace=True)
+
+    keyword_freq_filtered = freq_df_filtered.set_index('keyword')['frequency'].to_dict()
+
     # remove specific keywords depending on selected tag
     if IGNORE_WORDS.get(tag_select.value.lower()):
         for word in IGNORE_WORDS[tag_select.value.lower()]:
@@ -433,7 +539,7 @@ def _(
     if logo_switch.value:
         # 1. Load the logo
         # Make sure this path points to your uploaded file
-        logo_path = "./data/assets/JP-Morgan-Chase-Symbol.png" 
+        logo_path = "./assets/JP-Morgan-Chase-Symbol.png" 
         logo = Image.open(logo_path).convert("RGBA")
 
         # Optional: Resize logo if it's too large or small for the canvas
@@ -473,7 +579,7 @@ def _(
             width=canvas_size[0],
             height=canvas_size[1],
             max_font_size=100,  # Increased font size for larger canvas
-            max_words=20,      # Increased word count to fill space
+            max_words=n_words.value,      # Increased word count to fill space
             color_func=blue_color_func,
             mask=chase_mask,    # Apply the circular mask
             contour_width=0,
@@ -487,7 +593,7 @@ def _(
             width=canvas_size[0],
             height=canvas_size[1],
             max_font_size=150,  # Increased font size for larger canvas
-            max_words=20,      # Increased word count to fill space
+            max_words=n_words.value,      # Increased word count to fill space
             color_func=blue_color_func,
             # mask=chase_mask,    # Apply the circular mask
             # contour_width=0,