Initial commit

.gitattributes

@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/bertopic_model_tokyo_olympics_tweets filter=lfs diff=lfs merge=lfs -text
+models/bertopic_model_tokyo_olympics_tweets_unclean filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
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+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
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+
+  12. No Surrender of Others' Freedom.
+
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+                     END OF TERMS AND CONDITIONS
+
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+
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+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
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+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
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+    Copyright (C) <year>  <name of author>
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+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
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+
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+    <program>  Copyright (C) <year>  <name of author>
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+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
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+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

data/bfi_sample_questions.csv

@@ -0,0 +1,26 @@
+question,description
+Q1,Am indifferent to the feelings of others. 
+Q2,Inquire about others' well-being. 
+Q3,Know how to comfort others. 
+Q4,Love children. 
+Q5,Make people feel at ease. 
+Q6,Am exacting in my work. 
+Q7,Continue until everything is perfect. 
+Q8,Do things according to a plan. 
+Q9,Do things in a half-way manner. 
+Q10,Waste my time. 
+Q11,Don't talk a lot. 
+Q12,Find it difficult to approach others. 
+Q13,Know how to captivate people. 
+Q14,Make friends easily. 
+Q15,Take charge. 
+Q16,Get angry easily. 
+Q17,Get irritated easily. 
+Q18,Have frequent mood swings. 
+Q19,Often feel blue. 
+Q20,Panic easily. 
+Q21,Am full of ideas. 
+Q22,Avoid difficult reading material.
+Q23,Carry the conversation to a higher level. 
+Q24,Spend time reflecting on things. 
+Q25,Will not probe deeply into a subject. 

requirements.txt

@@ -0,0 +1,13 @@
+streamlit==1.10.0
+bertopic==0.11.0
+cffi==1.15.0
+factor_analyzer==0.4.0
+matplotlib==3.5.1
+nltk==3.7
+numpy==1.21.5
+pandas==1.4.2
+plotly==5.6.0
+scikit_learn==1.1.1
+scipy==1.7.3
+seaborn==0.11.2
+tqdm==4.64.0

survey_analytics_library.py

@@ -0,0 +1,383 @@
+
+# imports
+import pandas as pd
+import numpy as np
+import streamlit as st
+from tqdm.notebook import tqdm
+import matplotlib.pyplot as plt
+import plotly.express as px
+
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score
+
+import zipfile
+from xml.etree.cElementTree import XML
+
+import re
+from nltk.corpus import stopwords
+
+
+
+# # create elbow plot with kmeans to find optimal number of clusters
+# def create_elbow_plot_kmeans(df, num_clusters, init_method='k-means++', n_init=10, random_state=42, plot=True, template='simple_white', save=False):
+#     '''
+#     create elbow plot with kmeans to find optimal number of clusters based on inertia
+#     where the clusters strikes a balance between being not segmented enough and being too fragmented
+    
+#     we look for the point of diminishing returns (also known as the 'elbow') in terms of the inertia,
+#     where inertia is how close the data points are to their respective centers or centroids
+    
+#     arguments:
+#     df (df): a dataframe of data to cluster
+#     num_clusters (int): number of clusters to plot
+#     init_method (str): default to 'k-means++', other option is 'random'
+#     n_init (int): default to 10, number of times to run model, cost from the best run will be used
+#     random_state (int): default to 42, random seed used to initialise the model
+#     plot (bool): default to True, option to turn off plots
+#     template (str): default to 'simple_white', change as desired
+#     save (bool): default to False, if True save plot as .html file
+
+#     returns:
+#     a list of inertia for each run
+#     '''
+
+#     # create empty list to store inertia for each run
+#     inertia = []
+#     # define range of clusters to try
+#     k = range(2, num_clusters+1)
+
+#     # loop through number of clusters
+#     for num_clusters in tqdm(k):
+#         # define model
+#         kmeans = KMeans(n_clusters=num_clusters, init=init_method, n_init=n_init, random_state=random_state)
+#         # fit and predict data
+#         kmeans.fit_predict(df)
+#         # get predicted labels
+#         predicted_labels = kmeans.labels_
+#         # append score to list of scores
+#         inertia.append(kmeans.inertia_)
+
+#     # plot elbow plot
+#     if plot:
+#         fig = px.line(
+#             pd.DataFrame({'num_clusters':list(k), 'inertia':inertia}), 
+#             x='num_clusters', 
+#             y='inertia',
+#             title='Elbow Plot for Optimal Number of Clusters with '+init_method,
+#             markers=True,
+#             template=template,
+#             width=800,
+#             height=500,
+#             )
+#         st.plotly_chart(fig, use_container_width=True)
+#         if save:
+#             fig.write_html('Elbow Plot for Optimal Number of Clusters with '+init_method+'.html')
+    
+#     # return
+#     return inertia
+
+
+
+# # create plot of silhouette scores with sklearn model to find optimal number of clusters
+# def silhouette_score_plot_kmeans(df, num_clusters, init_method='k-means++', n_init=10, random_state=42, plot=True, template='simple_white', save=False):
+#     '''
+#     create plot of silhouette score with kmeans to find optimal number of clusters
+#     where the clusters strikes a balance between being not segmented enough and being too fragmented
+#     the closer the score is to 1, the more easily distinguishable are the clusters from each other
+    
+#     arguments:
+#     df (df): a dataframe of data to cluster
+#     num_clusters (int): number of clusters to plot
+#     init_method (str): default to 'k-means++', other option is 'random'
+#     n_init (int): default to 10, number of times to run model, cost from the best run will be used
+#     random_state (int): default to 42, random seed used to initialise the model
+#     plot (bool): default to True, option to turn off plots
+#     template (str): default to 'simple_white', change as desired
+#     save (bool): default to False, if True save plot as .html file
+
+#     returns:
+#     a list of silhouette scores for each run
+#     '''
+
+#     # create empty list to store silhoutte scores for each run
+#     silhouette_scores = []
+#     # define range of clusters to try
+#     k = range(2, num_clusters+1)
+
+#     # loop through number of clusters
+#     for num_clusters in tqdm(k):
+#         # define model
+#         kmeans = KMeans(n_clusters=num_clusters, init=init_method, n_init=n_init, random_state=random_state)
+#         # fit and predict data
+#         kmeans.fit_predict(df)
+#         # get predicted labels
+#         predicted_labels = kmeans.labels_
+#         # get silhoutte score
+#         score = silhouette_score(df, predicted_labels)
+#         # append score to list of scores
+#         silhouette_scores.append(score)
+        
+#     # plot silhouette scores
+#     if plot:
+#         fig = px.line(
+#             pd.DataFrame({'num_clusters':list(k), 'silhouette_scores':silhouette_scores}), 
+#             x='num_clusters', 
+#             y='silhouette_scores',
+#             title='Silhouette Scores for Optimal Number of Clusters with '+init_method,
+#             markers=True,
+#             template=template,
+#             width=800,
+#             height=500,
+#             )
+#         st.plotly_chart(fig, use_container_width=True)
+#         if save:
+#             fig.write_html('Silhouette Scores for Optimal Number of Clusters with '+init_method+'.html')
+    
+#     # return
+#     return silhouette_scores
+
+
+
+# replace text with multiple replacements
+def replace_text(string, dict_of_replacements):
+    '''
+    replace multiple substrings in a string with a dictionary of replacements
+    to be used if replacements are fixed and do not require regex as replace() is faster than re.sub()
+    for regex replacements use clean_text()
+    arguments:
+    string (str): string for replacement
+    dict_of_replacements (dict): dictionary of substring to replace and replacement
+        e.g. {'to replace this':'with this',...}
+    returns:
+    a string with substrings replaced
+    '''
+    # loop through dict
+    for key, value in dict_of_replacements.items():
+        # perform replacement
+        string = string.replace(key, value)
+    # return
+    return string
+
+
+
+# clean text string
+def clean_text(text_string, list_of_replacements, lowercase=True, ignorecase=False):
+    '''
+    clean text string
+        lower case string
+        regex sub user defined patterns with user defined replacements
+    
+    arguments:
+    text_string (str): text string to clean
+    list_of_replacements (list): a list of tuples consisting of regex pattern and replacement value
+        e.g. [('[^a-z\s]+', ''), ...]
+    lowercase (bool): default to True, if True, convert text to lowercase
+    ignorecase (bool): default to False, if True, ignore case when applying re.sub()
+        
+    returns:
+    a cleaned text string 
+    '''
+    
+    # check lowercase argument
+    if lowercase:
+        # lower case text string 
+        clean_string = text_string.lower()
+    else:
+        # keep text as is
+        clean_string = text_string
+    
+    if ignorecase:
+        # loop through each pattern and replacement
+        for pattern, replacement in list_of_replacements:
+            # replace defined pattern with defined replacement value
+            clean_string = re.sub(pattern, replacement, clean_string, flags=re.IGNORECASE)
+    else:
+        # loop through each pattern and replacement
+        for pattern, replacement in list_of_replacements:
+            # replace defined pattern with defined replacement value
+            clean_string = re.sub(pattern, replacement, clean_string)        
+        
+    # return
+    return clean_string
+
+
+
+# remove stopwords from tokens
+def remove_stopwords(tokens, language='english'):
+    '''
+    remove stopwords from tokens using list comprehension
+    default to using english stopwords
+    arguments:
+    tokens (list): list of token#s, output of word_tokenize()
+    language (str): default to english
+    returns:
+    a list of tokens without stopwords
+    '''
+    # define stopwords and store as a set
+    stopwords_set = set(stopwords.words(language))
+    # check if word is in list of stopwords
+    # returns a list of words not found in list of stopwords
+    stopwords_removed = [word for word in tokens if word not in stopwords_set]
+    # return
+    return stopwords_removed
+
+
+
+import itertools
+from typing import List
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+def visualize_barchart_titles(topic_model,
+                       topics: List[int] = None,
+                       subplot_titles: List[str] = None,
+                       top_n_topics: int = 8,
+                       n_words: int = 5,
+                       width: int = 250,
+                       height: int = 250) -> go.Figure:
+    """ Visualize a barchart of selected topics
+
+    Arguments:
+        topic_model: A fitted BERTopic instance.
+        topics: A selection of topics to visualize.
+        top_n_topics: Only select the top n most frequent topics.
+        n_words: Number of words to show in a topic
+        width: The width of each figure.
+        height: The height of each figure.
+
+    Returns:
+        fig: A plotly figure
+
+    Usage:
+
+    To visualize the barchart of selected topics
+    simply run:
+
+    ```python
+    topic_model.visualize_barchart()
+    ```
+
+    Or if you want to save the resulting figure:
+
+    ```python
+    fig = topic_model.visualize_barchart()
+    fig.write_html("path/to/file.html")
+    ```
+    <iframe src="../../getting_started/visualization/bar_chart.html"
+    style="width:1100px; height: 660px; border: 0px;""></iframe>
+    """
+    colors = itertools.cycle(["#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#009E73", "#F0E442"])
+
+    # Select topics based on top_n and topics args
+    freq_df = topic_model.get_topic_freq()
+    freq_df = freq_df.loc[freq_df.Topic != -1, :]
+    if topics is not None:
+        topics = list(topics)
+    elif top_n_topics is not None:
+        topics = sorted(freq_df.Topic.to_list()[:top_n_topics])
+    else:
+        topics = sorted(freq_df.Topic.to_list()[0:6])
+
+    # Initialize figure
+    if subplot_titles is None:
+        subplot_titles = [f"Topic {topic}" for topic in topics]
+    else:
+        subplot_titles = subplot_titles
+    columns = 4
+    rows = int(np.ceil(len(topics) / columns))
+    fig = make_subplots(rows=rows,
+                        cols=columns,
+                        shared_xaxes=False,
+                        horizontal_spacing=.1,
+                        vertical_spacing=.4 / rows if rows > 1 else 0,
+                        subplot_titles=subplot_titles)
+
+    # Add barchart for each topic
+    row = 1
+    column = 1
+    for topic in topics:
+        words = [word + "  " for word, _ in topic_model.get_topic(topic)][:n_words][::-1]
+        scores = [score for _, score in topic_model.get_topic(topic)][:n_words][::-1]
+
+        fig.add_trace(
+            go.Bar(x=scores,
+                   y=words,
+                   orientation='h',
+                   marker_color=next(colors)),
+            row=row, col=column)
+
+        if column == columns:
+            column = 1
+            row += 1
+        else:
+            column += 1
+
+    # Stylize graph
+    fig.update_layout(
+        template="plotly_white",
+        showlegend=False,
+        title={
+            'text': "<b>Topic Word Scores",
+            'x': .5,
+            'xanchor': 'center',
+            'yanchor': 'top',
+            'font': dict(
+                size=22,
+                color="Black")
+        },
+        width=width*4,
+        height=height*rows if rows > 1 else height * 1.3,
+        hoverlabel=dict(
+            bgcolor="white",
+            font_size=16,
+            font_family="Rockwell"
+        ),
+    )
+
+    fig.update_xaxes(showgrid=True)
+    fig.update_yaxes(showgrid=True)
+
+    return fig
+
+
+
+# convert transformer model zero shot classification prediction into dataframe
+def convert_zero_shot_classification_output_to_dataframe(model_output):
+    '''
+    convert zero shot classification output to dataframe
+    model's prediction is a list dictionaries
+    e.g. each prediction consists of the sequence being predicted, the user defined labels,
+        and the respective scores.
+    [
+    {'sequence': 'the organisation is generally...',
+     'labels': ['rewards', 'resourcing', 'leadership'],
+     'scores': [0.905086100101471, 0.06712279468774796, 0.027791114524006844]}, 
+    ...
+        ]
+    the function pairs the label and scores and stores it as a dataframe
+    it also identifies the label with the highest score
+    
+    arguments:
+    model_output (list): output from transformer.pipeline(task='zero-shot-classification')
+    
+    returns:
+    a dataframe of label and scores for each prediction
+    
+    '''
+    
+    # store results as dataframe
+    results = pd.DataFrame(model_output)
+    # zip labels and scores as dictionary
+    results['labels_scores'] = results.apply(lambda x: dict(zip(x['labels'], x['scores'])), axis=1) 
+    # convert labels_scores to dataframe
+    labels_scores = pd.json_normalize(results['labels_scores'])
+    # get label of maximum score as new column
+    labels_scores['label'] = labels_scores.idxmax(axis=1)
+    # get score of maximum score as new column
+    labels_scores['score'] = labels_scores.max(axis=1)
+    # concat labels_scores to results
+    results = pd.concat([results, labels_scores], axis=1)
+    # drop unused columns
+    results = results.drop(['labels', 'scores'], axis=1)
+
+    # return
+    return results

survey_analytics_streamlit.py

@@ -0,0 +1,333 @@
+# launch app
+# streamlit run "survey_analytics_streamlit.py"
+
+# imports
+import streamlit as st
+import pandas as pd
+import numpy as np
+import os
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+
+# factor analysis
+from factor_analyzer import FactorAnalyzer
+from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity
+from factor_analyzer.factor_analyzer import calculate_kmo
+from scipy.stats import zscore
+
+# nlp
+from bertopic import BERTopic
+
+# custom
+import survey_analytics_library as LIB
+
+st.set_page_config(layout='wide')
+
+# define data file path
+data_path = 'data' + os.sep
+# define model file path
+model_path = 'models' + os.sep
+
+# load all data and models
+@st.cache
+def read_survey_data():
+    data_survey = pd.read_csv(data_path+'bfi_sample_answers.csv')
+    data_questions = pd.read_csv(data_path+'bfi_sample_questions.csv')
+    return data_survey, data_questions
+data_survey, data_questions = read_survey_data()
+
+@st.cache
+def read_tweet_data():
+    tokyo = pd.read_csv(data_path+'tokyo_olympics_tweets.csv')
+    return tokyo
+tokyo = read_tweet_data()
+
+@st.cache(allow_output_mutation=True)
+def load_bertopic_model_unclean():
+    topic_model = BERTopic.load(model_path+'bertopic_model_tokyo_olympics_tweets_unclean')
+    return topic_model
+topic_model_unclean = load_bertopic_model_unclean()
+
+@st.cache(allow_output_mutation=True)
+def load_bertopic_model():
+    topic_model = BERTopic.load(model_path+'bertopic_model_tokyo_olympics_tweets')
+    return topic_model
+topic_model = load_bertopic_model()
+
+@st.cache
+def read_topic_results():
+    topic_results = pd.read_csv(data_path+'topic_results.csv')
+    return topic_results
+topic_results = read_topic_results()
+
+# write title of app
+st.title('DACoP - Survey Analytics')
+st.markdown('''---''')
+
+
+
+st.header('Clustering Survey Responders')
+st.write('''
+    Having knowledge about different groups of responders can help us to customise our interactions with them.  
+    E.g. Within the Financial Institutions we have banks, insurers, and payment services.   
+    We want to be able to cluster survey reponders into various groups based on how their answers.  
+    This can be achieved though **Factor Analysis**.   
+    ''')
+st.write('\n')
+st.write('\n')
+
+# copy daya
+df_factor_analysis = data_survey.copy()
+
+st.subheader('Sample Survey Data')
+st.write('''
+    Here we have a sample survey dataset where responders answer questions about their personality traits on a scale from 1 (Very Inaccurate) to 6 (Very Accurate).  
+    Factor Analysis gives us \'factors\' or groups of responders into groups can provide us insights about the different personalities of the responders.  
+    ''')
+
+# split page into two columns
+# display survey questions and responses as dataframes
+col1, col2 = st.columns(2)
+with col1:
+    st.write('Survey Questions')
+    st.dataframe(data_questions)
+with col2:
+    st.write('Survey Answers')
+    st.dataframe(df_factor_analysis)
+st.write('\n')
+st.write('\n')
+
+st.subheader('Factor Analysis Suitability')
+st.write('''
+    Before performing Factor Analysis on the data, we need to evaluate if it is suitable to do so.  
+    We apply two statistical tests (Bartlett's and KMO test) the data. 
+    ''')
+
+# interactive button to run statistical test to determine suitability for factor analysis
+if st.button('Run Tests'):
+    # Test with the null hypothesis that the correlation matrix is an identity matrix
+    bartlett_sphericity_stat, p_value = calculate_bartlett_sphericity(x=df_factor_analysis)
+    # Test how predictable of a variable by others
+    kmo_per_variable, kmo_total = calculate_kmo(x=df_factor_analysis)
+    st.write(f'''
+        The P Value from Bartlett\'s Test (suitability is less than 0.05): **{round(p_value, 2)}**  
+        The Value from KMO Test (suitability is more than 0.60): **{round(kmo_total, 2)}**  
+        ''')
+    fa_stat_test = 'Failed'
+
+    if p_value < 0.05 and kmo_total >= 0.6:
+        fa_stat_test = 'Passed'
+
+    st.success(f'Our data has **{fa_stat_test}** the two statistical tests!')
+
+st.write('\n')
+st.write('\n')
+
+# define factor analyser model
+fa = FactorAnalyzer()
+# fit data
+fa.fit(X=df_factor_analysis)
+
+# get eigenvalues
+eigenvalues, _ = fa.get_eigenvalues()
+# get number of eigenvalues more than or equal to 1
+optimal_factors = len([value for value in eigenvalues if value >= 1])
+# store eigenvalues and number of clusters into a df for plotly
+scree_df = pd.DataFrame({'Eigenvalues':eigenvalues, 'Number of Factors':list(range(1, len(eigenvalues)+1))}) 
+
+st.subheader('Number of Clusters?')
+st.write(f'''
+    How many clusters or factors are appropriate for our data?  
+    For Factor Analysis, we can determine the number of factors using the Kaiser criterion and a Scree Plot.  
+    We should include factors with an Eigenvalue of at least 1.0.  
+    ''')
+
+# plot scree plot
+fig = px.line(
+    scree_df,
+    x='Number of Factors', 
+    y='Eigenvalues',
+    markers=True,
+    title='Scree Plot for Kaiser Criterion',
+    template='simple_white',
+    width=800,
+    height=500,
+    )
+fig.add_hline(y=1, line_width=3, line_color='darkgreen')
+st.plotly_chart(fig, use_container_width=True)
+st.write(f'''
+    Kaiser criterion is one of many guides to determine the number of factors, ultimately the decision on the number of factors to use is best decided by the user based on their use case.  
+    ''')
+
+with st.form('num_factor_form'):
+    user_num_factors = st.number_input('Enter desired number of factors:', min_value=1, max_value=10, value=6)
+    optimal_factors = user_num_factors
+    submit = st.form_submit_button('Run Factor Analysis')
+
+st.write('\n')
+st.write('\n')
+
+# define factor analyser model
+fa = FactorAnalyzer(n_factors=optimal_factors, rotation='varimax')
+# fit data
+fa.fit(df_factor_analysis)
+# generate factor loadings
+loads_df = pd.DataFrame(fa.loadings_, index=df_factor_analysis.columns)
+
+transformed_df = fa.fit_transform(df_factor_analysis)
+transformed_df = pd.DataFrame(transformed_df)
+transformed_df.columns = ['factor_'+str(col) for col in list(transformed_df)]
+
+responder_factors = transformed_df.copy()
+responder_factors['cluster'] = responder_factors.apply(lambda s: s.argmax(), axis=1)
+
+# list of factor columns
+list_of_factor_cols = [col for col in responder_factors.columns if 'factor_' in col]
+st.subheader('Fator Analysis Results')
+st.write('''
+    Factor analysis gives us a loading for every factor for each responder.  
+    We assign each responder to a factor or cluster based on their maximum loading across all the factors.  
+    ''')
+# highlight factor with max loadings
+st.dataframe(responder_factors.style.highlight_max(axis=1, subset=list_of_factor_cols, props='color:white; background-color:green;').format(precision=2))
+st.write('\n')
+
+fa_clusters = df_factor_analysis.copy().reset_index(drop=True)
+fa_clusters['cluster'] = responder_factors['cluster']
+fa_z_scores = df_factor_analysis.copy().reset_index(drop=True)
+fa_z_scores = fa_z_scores.apply(zscore)
+fa_z_scores['cluster'] = responder_factors['cluster']
+fa_z_scores = fa_z_scores.groupby('cluster').mean().reset_index()
+fa_z_scores = fa_z_scores.apply(lambda x: round(x, 2))
+
+cm = sns.light_palette('green', as_cmap=True)
+list_of_question_cols = list(fa_z_scores.iloc[:,1:])
+st.write('''
+    Aggregating the scores of the clusters gives us detail insights to the personality traits of the responders.  
+    The scores here have been normalised to Z-scores, a measure of how many standard deviations (SD) is the score away from the mean.  
+    E.g. A Z-score of 0 indicates the score is identical to the mean, while a Z-score of 1 indicates the score is 1 SD away from the mean.  
+    ''')
+st.dataframe(fa_z_scores.style.background_gradient(cmap=cm, subset=list_of_question_cols).format(precision=2))
+st.write('\n')
+
+cluster_counts = fa_clusters['cluster'].value_counts().reset_index()
+cluster_counts = cluster_counts.rename(columns={'index':'Cluster', 'cluster':'Count'})
+
+st.write('''
+    Lastly, we can visualise the distribution of responders in each cluster.  
+    ''')
+fig = px.pie(
+    cluster_counts,
+    values='Count', 
+    names='Cluster', 
+    hole=0.35,
+    title='Percentage of Responders in Each Cluster',
+    template='simple_white',
+    width=1000,
+    height=600,
+    )
+st.plotly_chart(fig, use_container_width=True)
+st.markdown('''---''')
+
+
+
+
+
+
+st.header('Uncovering Topics from Text Responses')
+st.write('''
+    With feedback forms or open-ended survey questions, we want to know what are the responders generally talking about.  
+    One way would be to manually read all the collected response to get a sense of the topics within, however, this is very manual and subjective.  
+    Using **Topic Modelling**, we can programmatically extract common topics with the help of machine learning.  
+    ''')
+st.write('\n')
+
+st.write('''
+    Here we have 10,000 tweets from the Tokyo Olympics, going through them manually and coming up with topics would not be practical.  
+    ''')
+st.dataframe(tokyo)
+st.write('\n')
+st.write('\n')
+
+st.write('''
+    Lets generate some topics without performing any cleaning to the data.  
+    ''')
+st.write('\n')
+
+fig = LIB.visualize_barchart_titles(
+    topic_model=topic_model_unclean,
+    subplot_titles=None,
+    n_words=5,
+    top_n_topics=8,
+    height=300
+)
+st.plotly_chart(fig, use_container_width=True)
+
+st.write('''
+    From the chart above, we can see that 'Topic 1' and 'Topic 3' have some words that are not as meaningful.  
+    For 'Topic 1', we already know that the tweets are about the Tokyo 2020 Olympics, having a topic for that isn't helpful.  
+    'Tokyo', '2020', etc., we refer to these as *stopwords*, and lets remove them and regenerate the topics.  
+    ''')
+st.write('\n')
+
+labelled_topics = [
+    'Mirabai Chanu (Indian Weightlifter)',
+    'Hockey',
+    'Barbra Banda (Zambian Football Player)',
+    'Sutirtha Mukherjee (Indian Table Tennis Player)',
+    'Vikas Krishan (Indian Boxer)',
+    'Road Race',
+    'Brendon Smith (Australian Swimmer)',
+    'Sam Kerr (Australian Footballer)',
+    ]
+
+fig = LIB.visualize_barchart_titles(
+    topic_model=topic_model,
+    subplot_titles=labelled_topics,
+    n_words=5,
+    top_n_topics=8,
+    height=300
+)
+st.plotly_chart(fig, use_container_width=True)
+st.write('''
+    Now we can see that the topics have improved.  
+    We can make use of the top words in each topic to come up with a meaningful name.  
+    ''')
+st.write('\n')
+st.write('\n')
+
+topics_df = topic_model.get_topic_info()
+
+st.write(f'''
+    Next, we can also review the total number of topics and how many tweets are in each topic, to give us a sense of importance or priority.  
+    There are a total of **{len(topics_df)-1}** topics, and the larget topic contains **{topics_df['Count'][1]}** tweets.  
+    {topics_df['Count'][0]} tweets have also been assigned as Topic -1 or outliers.  
+    These tweets are more unique and there are enough of them to form a topic.   
+    ''')
+st.dataframe(topics_df)
+st.write('\n')
+
+st.write('''
+    As there are many topics generated, we can also visualise how closely related they are to one another.  
+    Depending on the business case, we may want to merge these topics together or keep them separate.  
+    If there are too many or too few topics, there is also the option to tune the parameters of the model to refine the results.  
+    ''')
+fig = topic_model.visualize_topics()
+st.plotly_chart(fig, use_container_width=True)
+st.write('\n')
+
+st.write('''
+    Lastly, we can inspect the individual tweets within each topic.  
+    ''')
+
+with st.form('inspect_tweets'):
+    inspect_topic = st.number_input('Enter Topic (from -1 to 63) to Inspect:', min_value=-1, max_value=63, value=8)
+    submit = st.form_submit_button('Inspect Topic')
+
+inspect_topic_words = [i[0] for i in topic_model.get_topic(inspect_topic)[:5]]
+
+st.write(f'''
+    The top five words for Topic {inspect_topic} are: {inspect_topic_words}
+    ''')
+st.dataframe(topic_results.loc[(topic_results['Topic'] == inspect_topic)])