diff --git a/02-B_Thematic-Processing.py b/02-B_Thematic-Processing.py new file mode 100644 index 0000000..ac687b0 --- /dev/null +++ b/02-B_Thematic-Processing.py @@ -0,0 +1,320 @@ +import marimo + +__generated_with = "0.18.3" +app = marimo.App(width="medium") + + +@app.cell +def _(): + import marimo as mo + # import pandas as pd + import modin.pandas as pd + from tqdm import tqdm + from pathlib import Path + from datetime import datetime + + from utils import connect_qumo_ollama + + OLLAMA_LOCATION= 'localhost' + # VM_NAME = 'ollama-lite' + + # initialize tqdm for pandas + tqdm.pandas() + from modin.config import ProgressBar + ProgressBar.enable() + + 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_taguette_postprocess') + + 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, + ) + model_select + return TAGUETTE_EXPORT_DIR, WORKING_DIR, client, mo, model_select, pd + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + # 1) Export Data out of Taguette + + **Highlights** + 1. Go to: https://taguette.qumo.io/project/1 + 2. Select 'Highlights' (left side) > 'See all hightlights' > 'Export this view' (top right) > 'CSV' + 3. Save to '{TAGUETTE_EXPORT_DIR}/all_tags.csv' + + **Tags Codebook** + 1. Select 'Project Info' (left side) > 'Export codebook' > 'CSV' + 2. Save to '{TAGUETTE_EXPORT_DIR}/codebook.csv' + + _NOTE: Sometimes you need to explicitly allow 'Unsafe Download' in the browser's download manager_ + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + # 2) Import Data + """) + return + + +@app.cell +def _(TAGUETTE_EXPORT_DIR, pd): + all_tags_df = pd.read_csv(f'{TAGUETTE_EXPORT_DIR}/all_tags.csv') + all_tags_df['_seq_id'] = range(len(all_tags_df)) + all_tags_df + return (all_tags_df,) + + +@app.cell +def _(all_tags_df): + # get count of rows per tag + tag_counts = all_tags_df['tag'].value_counts().reset_index() + tag_counts + return + + +@app.cell +def _(TAGUETTE_EXPORT_DIR, pd): + codebook_df = pd.read_csv(f'{TAGUETTE_EXPORT_DIR}/codebook.csv') + codebook_df.rename(columns={'description': 'theme_description'}, inplace=True) + codebook_df + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + # 3) Select Tag for processing + """) + return + + +@app.cell +def _(all_tags_df, mo): + tag_select = mo.ui.dropdown( + options=all_tags_df['tag'].unique().tolist(), + label="Select Tag to Process", + value="Chase as a brand", + full_width=True + ) + tag_select + return (tag_select,) + + +@app.cell +def _(all_tags_df, mo, tag_select): + mo.stop(not tag_select.value, mo.md("Select tag to continue")) + # filter all_tags_df to only the document = file_dropdown.value + df = all_tags_df.loc[all_tags_df['tag'] == tag_select.value].copy() + df + return (df,) + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + # 4) Keyword extraction + """) + return + + +@app.cell +def _(mo, tag_select): + mo.stop(not tag_select.value, mo.md("Select tag to continue")) + start_processing_btn = mo.ui.button( + label="Start Keyword Extraction", + kind="warn", + on_click=lambda val: True + ) + start_processing_btn + return (start_processing_btn,) + + +@app.cell +def _(client, df, mo, model_select, pd, start_processing_btn): + from utils import ollama_keyword_extraction + # Wait for start processing button + mo.stop(not start_processing_btn.value, "Click button above to start processing") + + # Run keyword extraction + df['keywords'] = df.apply( + lambda row: pd.Series(ollama_keyword_extraction( + content=row['content'], + tag=row['tag'], + client=client, + model=model_select.value + )), + axis=1 + ) + return + + +@app.cell +def _(df): + df['keywords_txt'] = df['keywords'].progress_apply(lambda kws: ', '.join(kws)) + return + + +@app.cell +def _(WORKING_DIR, df, tag_select): + df[['id', 'tag', 'content', 'keywords_txt']].to_csv( + WORKING_DIR / f'keywords_{tag_select.value.replace(" ", "-")}.csv', + index=False + ) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + # 5) Wordcloud generation + """) + return + + +@app.cell +def _(): + # Start with loading all necessary libraries + import numpy as np + from os import path + from PIL import Image + from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator + import matplotlib.pyplot as plt + + import warnings + warnings.filterwarnings("ignore") + return Image, WordCloud, np, plt + + +@app.cell +def _(df): + MIN_FREQ = 2 + + all_keywords_list = df['keywords'].tolist() + all_keywords_flat = [item for sublist in all_keywords_list for item in sublist] + + + keyword_freq = {} + for kw in all_keywords_flat: + if kw in keyword_freq: + keyword_freq[kw] += 1 + else: + keyword_freq[kw] = 1 + + 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 = [kw for kw, freq in sorted_keywords] + sorted_keywords_list + return (keyword_freq_filtered,) + + +@app.cell +def _(plt): + import random + import matplotlib.colors as mcolors + + def blue_color_func(word, font_size, position, orientation, random_state=None, **kwargs): + # Use the provided random_state for reproducibility if available, else use random module + r = random_state if random_state else random + + # Sample from the darker end of the 'Blues' colormap (e.g., 0.4 to 1.0) + # 0.0 is white/light, 1.0 is dark blue + min_val, max_val = 0.4, 1.0 + color_val = r.uniform(min_val, max_val) + + # Get color from matplotlib colormap + rgba = plt.cm.Blues(color_val) + return mcolors.to_hex(rgba) + return (blue_color_func,) + + +@app.cell +def _(Image, np): + chase_mask = np.array(Image.open("./data/assets/Chase-National-Bank-Logo.png")) + + def transform_format(val): + if val == 0: + return 255 + else: + return 1 + + transformed_chase_mask = np.ndarray((chase_mask.shape[0], chase_mask.shape[1]), np.int32) + for i in range(len(chase_mask)): + transformed_chase_mask[i] = list(map(transform_format, chase_mask[i])) + return + + +@app.cell +def _(WordCloud, blue_color_func, keyword_freq_filtered, mo, plt): + wordcloud = WordCloud( + background_color='white', + width=800, + max_font_size=60, + max_words=20, + # colormap='Blues', + # relative_scaling=0.5, # Use rank in sorted frequency list instead of pure frequency + color_func=blue_color_func, + # mask=chase_mask + # random_state=42 + ).generate_from_frequencies(keyword_freq_filtered) + + # Display the generated image: + plt.imshow(wordcloud, interpolation='bilinear') + plt.axis("off") + plt.show() + + save_wordcloud_btn = None + save_wordcloud_btn = mo.ui.button( + label="Save_wordcloud_button", + kind="warn", + on_click=lambda val: True + ) + save_wordcloud_btn + return save_wordcloud_btn, wordcloud + + +@app.cell +def _(WORKING_DIR, mo, save_wordcloud_btn, tag_select, wordcloud): + # Wait for start processing button + mo.stop(not save_wordcloud_btn.value, "Click button above to save wordcloud image") + + + filename = f'wordcloud_{tag_select.value.replace(" ", "-")}.png' + fpath = WORKING_DIR / filename + + # add a (increasing) number to the filename so we can save multiple. find the latest in the directory first + existing_files = list(WORKING_DIR.glob(f'wordcloud_{tag_select.value.replace(" ", "-")}*.png')) + if existing_files: + existing_numbers = [] + for ef in existing_files: + parts = ef.stem.split('_') + if len(parts) > 2 and parts[-1].isdigit(): + existing_numbers.append(int(parts[-1])) + if existing_numbers: + next_number = max(existing_numbers) + 1 + else: + next_number = 1 + fpath = WORKING_DIR / f'wordcloud_{tag_select.value.replace(" ", "-")}_{next_number}.png' + + wordcloud.to_file(fpath) + mo.md(f"Wordcloud saved to: {fpath}") + return + + +if __name__ == "__main__": + app.run() diff --git a/02_Taguette_Post-Process.py b/02_Taguette_Post-Process.py index 082ea59..052695a 100644 --- a/02_Taguette_Post-Process.py +++ b/02_Taguette_Post-Process.py @@ -355,7 +355,6 @@ def _(mo, sentiment_df): @app.cell def _(split_rows_editor): split_rows_editor - return diff --git a/ollama/docker-compose.yml b/ollama/docker-compose.yml index b34364b..9d8c0b9 100644 --- a/ollama/docker-compose.yml +++ b/ollama/docker-compose.yml @@ -19,13 +19,13 @@ services: # 'devices' section further down as a fallback (less portable). ## UNCOMMENT THE FOLLOWING BLOCK FOR NVIDIA GPU SUPPORT ### - deploy: - resources: - reservations: - devices: - - driver: nvidia - count: all - capabilities: [gpu] + # deploy: + # resources: + # reservations: + # devices: + # - driver: nvidia + # count: all + # capabilities: [gpu] environment: # Visible devices / capabilities for the NVIDIA container runtime diff --git a/pyproject.toml b/pyproject.toml index 5381f33..3d0dc4c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,6 +6,7 @@ readme = "README.md" requires-python = ">=3.12" dependencies = [ "marimo>=0.18.0", + "modin>=0.37.1", "numpy>=2.3.5", "ollama>=0.6.1", "openai>=2.9.0", @@ -13,4 +14,5 @@ dependencies = [ "pyzmq>=27.1.0", "requests>=2.32.5", "taguette>=1.5.1", + "wordcloud>=1.9.4", ] diff --git a/utils/__init__.py b/utils/__init__.py index cd88752..c36fa01 100644 --- a/utils/__init__.py +++ b/utils/__init__.py @@ -2,3 +2,4 @@ from .ollama_utils import connect_qumo_ollama from .data_utils import create_sentiment_matrix, extract_theme from .transcript_utils import load_srt, csv_to_markdown, cpc_smb_to_markdown from .sentiment_analysis import dummy_sentiment_analysis, ollama_sentiment_analysis +from .keyword_analysis import ollama_keyword_extraction diff --git a/utils/keyword_analysis.py b/utils/keyword_analysis.py new file mode 100644 index 0000000..27f5485 --- /dev/null +++ b/utils/keyword_analysis.py @@ -0,0 +1,78 @@ +import pandas as pd + +from ollama import Client +import json + + + +def ollama_keyword_extraction(content, tag, client: Client, model) -> list: + """ + Perform sentiment analysis using Ollama model. + + Parameters: + - content: Text content to analyze + - tag: Tag indicating the type of sentiment analysis (e.g., 'VT - Positive') + + Returns: + - sentiment score and reason + """ + + # Construct prompt for Ollama model + prompt = f""" +### Role +You are a qualitative data analyst. Your task is to extract keywords from a user quote to build a semantic word cluster. + +### Guidelines +1. **Quantity:** Extract **1-5** high-value keywords. If the quote only contains 1 valid insight, return only 1 keyword. Do not force extra words. +2. **Specificity:** Avoid vague, single nouns (e.g., "tech", "choice", "system"). Instead, capture the descriptor (e.g., "tech-forward", "payment choice", "legacy system"). +3. **Adjectives:** Standalone adjectives are acceptable if they are strong descriptors (e.g., "reliable", "trustworthy", "professional"). +4. **Normalize:** Convert verbs to present tense and nouns to singular. +5. **Output Format:** Return a single JSON object with the key "keywords" containing a list of strings. + +### Examples + +**Input Context:** Chase as a Brand +**Input Quote:** "I would describe it as, you know, like the next big thing, like, you know, tech forward, you know, customer service forward, and just hating that availability." +**Output:** {{ "keywords": ["tech forward", "customer service focused", "availability"] }} + +**Input Context:** App Usability +**Input Quote:** "There are so many options when I try to pay, it's confusing." +**Output:** {{ "keywords": ["confusing", "payment options"] }} + +**Input Context:** Investment Tools +**Input Quote:** "It is just really reliable." +**Output:** {{ "keywords": ["reliable"] }} + +### Input Data +**Context/Theme:** {tag} +**Quote:** "{content}" + +### Output +```json +""" + + max_retries = 3 + for attempt in range(max_retries): + try: + resp = client.generate( + model=model, + prompt=prompt, + format='json', + ) + + response_text = resp.response.strip() + + # Extract JSON from response + start_index = response_text.find('{') + + if start_index == -1: + raise ValueError("No JSON found") + + response_json, _ = json.JSONDecoder().raw_decode(response_text[start_index:]) + keywords = response_json.get('keywords', []) + return [keywords] + + except Exception as e: + print(f"Attempt {attempt + 1}/{max_retries} failed: {e}. Output was: {response_text}") + if attempt == max_retries - 1: + return [[]] \ No newline at end of file diff --git a/uv.lock b/uv.lock index a5b1122..3f5e70a 100644 --- a/uv.lock +++ b/uv.lock @@ -217,6 +217,81 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, ] +[[package]] +name = "contourpy" +version = "1.3.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" } +wheels = [ + { url = 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