app.py

import os

# Dendrograms will not work with the latest version of scipy (1.12.0), so installing the version prior to be safe
os.system("pip install scipy==1.11.4")

import gradio as gr
from datetime import datetime
import pandas as pd
import numpy as np
import time

from sentence_transformers import SentenceTransformer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import make_pipeline
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
import funcs.anonymiser as anon
from umap import UMAP

from torch import cuda, backends, version

# Default seed, can be changed in number selection on options page
random_seed = 42

# Check for torch cuda
# If you want to disable cuda for testing purposes
#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

print("Is CUDA enabled? ", cuda.is_available())
print("Is a CUDA device available on this computer?", backends.cudnn.enabled)
if cuda.is_available():
    torch_device = "gpu"
    print("Cuda version installed is: ", version.cuda)
    low_resource_mode = "No"
    #os.system("nvidia-smi")
else: 
    torch_device =  "cpu"
    low_resource_mode = "Yes"

print("Device used is: ", torch_device)



from bertopic import BERTopic


today = datetime.now().strftime("%d%m%Y")
today_rev = datetime.now().strftime("%Y%m%d")

from funcs.helper_functions import dummy_function, initial_file_load, read_file, zip_folder, delete_files_in_folder, save_topic_outputs
#from funcs.representation_model import representation_model
from funcs.embeddings import make_or_load_embeddings

# Log terminal output: https://github.com/gradio-app/gradio/issues/2362
import sys

class Logger:
    def __init__(self, filename):
        self.terminal = sys.stdout
        self.log = open(filename, "w")

    def write(self, message):
        self.terminal.write(message)
        self.log.write(message)
        
    def flush(self):
        self.terminal.flush()
        self.log.flush()
        
    def isatty(self):
        return False    

sys.stdout = Logger("output.log")

def read_logs():
    sys.stdout.flush()
    with open("output.log", "r") as f:
        return f.read()

# Load embeddings
embeddings_name = "BAAI/bge-small-en-v1.5" #"jinaai/jina-embeddings-v2-base-en"

# Use of Jina deprecated - kept here for posterity
# Pinning a Jina revision for security purposes: https://www.baseten.co/blog/pinning-ml-model-revisions-for-compatibility-and-security/
# Save Jina model locally as described here: https://huggingface.co/jinaai/jina-embeddings-v2-base-en/discussions/29
# local_embeddings_location = "model/jina/"
#revision_choice = "b811f03af3d4d7ea72a7c25c802b21fc675a5d99"
#revision_choice = "69d43700292701b06c24f43b96560566a4e5ad1f"

# Model used for representing topics
hf_model_name =  'second-state/stablelm-2-zephyr-1.6b-GGUF' #'TheBloke/phi-2-orange-GGUF' #'NousResearch/Nous-Capybara-7B-V1.9-GGUF'
hf_model_file =   'stablelm-2-zephyr-1_6b-Q5_K_M.gguf' # 'phi-2-orange.Q5_K_M.gguf' #'Capybara-7B-V1.9-Q5_K_M.gguf'

def extract_topics(data, in_files, min_docs_slider, in_colnames, max_topics_slider, candidate_topics, data_file_name_no_ext, custom_labels_df, anonymise_drop, return_intermediate_files, embeddings_super_compress, low_resource_mode, save_topic_model, embeddings_out, zero_shot_similarity, random_seed, calc_probs, progress=gr.Progress(track_tqdm=True)):

    progress(0, desc= "Loading data")

    if calc_probs == "No":
        calc_probs = False
    elif calc_probs == "Yes":
        print("Calculating all probabilities.")
        calc_probs == True

    if not in_colnames:
        error_message = "Please enter one column name to use to find topics."
        print(error_message)
        return error_message, None, embeddings_out, data_file_name_no_ext, None, None

    all_tic = time.perf_counter()

    output_list = []
    file_list = [string.name for string in in_files]

    in_colnames_list_first = in_colnames[0]

    docs = list(data[in_colnames_list_first].str.lower())
    
    if anonymise_drop == "Yes":
        progress(0.1, desc= "Anonymising data")
        anon_tic = time.perf_counter()
        
        data_anon_col, anonymisation_success = anon.anonymise_script(data, in_colnames_list_first, anon_strat="replace")
        data[in_colnames_list_first] = data_anon_col[in_colnames_list_first]
        anonymise_data_name = data_file_name_no_ext + "_anonymised_" + today_rev +  ".csv"
        data.to_csv(anonymise_data_name)
        output_list.append(anonymise_data_name)

        print(anonymisation_success)

        anon_toc = time.perf_counter()
        time_out = f"Anonymising text took {anon_toc - anon_tic:0.1f} seconds"

    # Check if embeddings are being loaded in 
    progress(0.2, desc= "Loading/creating embeddings")

    print("Low resource mode: ", low_resource_mode)

    if low_resource_mode == "No":
        print("Using high resource BGE transformer model")

        embedding_model = SentenceTransformer(embeddings_name)

        # Use of Jina now superseded by BGE, keeping this code just in case I consider reverting one day
        #try:
        #embedding_model = AutoModel.from_pretrained(embeddings_name, revision = revision_choice, trust_remote_code=True,device_map="auto") # For Jina
        #except:
        #     embedding_model = AutoModel.from_pretrained(embeddings_name)#, revision = revision_choice, trust_remote_code=True, device_map="auto", use_auth_token=os.environ["HF_TOKEN"])
        #tokenizer = AutoTokenizer.from_pretrained(embeddings_name)
        #embedding_model_pipe = pipeline("feature-extraction", model=embedding_model, tokenizer=tokenizer)

        # UMAP model uses Bertopic defaults
        umap_model = UMAP(n_neighbors=15, n_components=5, min_dist=0.0, metric='cosine', low_memory=False, random_state=random_seed)

    elif low_resource_mode == "Yes":
        print("Choosing low resource TF-IDF model.")

        embedding_model_pipe = make_pipeline(
                TfidfVectorizer(),
                TruncatedSVD(100) # 100 # To be compatible with zero shot, this needs to be lower than number of suggested topics
                )
        embedding_model = embedding_model_pipe

        umap_model = TruncatedSVD(n_components=5, random_state=random_seed)

    embeddings_out = make_or_load_embeddings(docs, file_list, embeddings_out, embedding_model, embeddings_super_compress, low_resource_mode)

    vectoriser_model = CountVectorizer(stop_words="english", ngram_range=(1, 2), min_df=0.1)

    # Representation model not currently used in this function
    #print("Create Keybert-like topic representations by default")
    #from funcs.representation_model import create_representation_model, llm_config, chosen_start_tag
    #representation_model = create_representation_model("No", llm_config, hf_model_name, hf_model_file, chosen_start_tag, low_resource_mode)  

 
    progress(0.3, desc= "Embeddings loaded. Creating BERTopic model")

    if not candidate_topics:
        
        topic_model = BERTopic( embedding_model=embedding_model, #embedding_model_pipe, #for Jina
                                vectorizer_model=vectoriser_model,
                                umap_model=umap_model,
                                min_topic_size = min_docs_slider,
                                nr_topics = max_topics_slider,
                                calculate_probabilities=calc_probs,
                                #representation_model=representation_model,
                                verbose = True)

        assigned_topics, probs = topic_model.fit_transform(docs, embeddings_out)

        #print(assigned_topics)

        # Replace original labels with Keybert labels
        #if "KeyBERT" in topic_model.get_topic_info().columns:
        #    keybert_labels = [f"{i+1}: {', '.join(entry[:5])}" for i, entry in enumerate(topic_model.get_topics(full=True)["KeyBERT"].values())]
        #    topic_model.set_topic_labels(keybert_labels)


    # Do this if you have pre-defined topics
    else:
        if low_resource_mode == "Yes":
            error_message = "Zero shot topic modelling currently not compatible with low-resource embeddings. Please change this option to 'No' on the options tab and retry."
            print(error_message)

            return error_message, output_list, embeddings_out, data_file_name_no_ext, None, docs

        zero_shot_topics = read_file(candidate_topics.name)
        zero_shot_topics_lower = list(zero_shot_topics.iloc[:, 0].str.lower())

 

        topic_model = BERTopic( embedding_model=embedding_model, #embedding_model_pipe, # for Jina
                                vectorizer_model=vectoriser_model,
                                umap_model=umap_model,
                                min_topic_size = min_docs_slider,
                                nr_topics = max_topics_slider,
                                zeroshot_topic_list = zero_shot_topics_lower,
                                zeroshot_min_similarity = zero_shot_similarity, # 0.7
                                calculate_probabilities=calc_probs,
                                #representation_model=representation_model,
                                verbose = True)
        
        assigned_topics, probs = topic_model.fit_transform(docs, embeddings_out)

        # For some reason, zero topic modelling exports assigned topics as a np.array instead of a list. Converting it back here.
        if isinstance(assigned_topics, np.ndarray):
            assigned_topics = assigned_topics.tolist()
        #print(assigned_topics.tolist())

         # Zero shot modelling is a model merge, which wipes the c_tf_idf part of the resulting model completely. To get hierarchical modelling to work, we need to recreate this part of the model with the CountVectorizer options used to create the initial model. Since with zero shot, we are merging two models that have exactly the same set of documents, the vocubulary should be the same, and so recreating the cf_tf_idf component in this way shouldn't be a problem. Discussion here, and below based on Maarten's suggested code: https://github.com/MaartenGr/BERTopic/issues/1700

        doc_dets = topic_model.get_document_info(docs)

        documents_per_topic = doc_dets.groupby(['Topic'], as_index=False).agg({'Document': ' '.join})

        # Assign CountVectorizer to merged model

        topic_model.vectorizer_model = vectoriser_model

        # Re-calculate c-TF-IDF
        c_tf_idf, _ = topic_model._c_tf_idf(documents_per_topic)
        topic_model.c_tf_idf_ = c_tf_idf

        # Replace original labels with Keybert labels
        #if "KeyBERT" in topic_model.get_topic_info().columns:
        #    print(topic_model.get_topics(full=True)["KeyBERT"].values())
        #    keybert_labels = [f"{i+1}: {', '.join(entry[:5])}" for i, entry in enumerate(topic_model.get_topics(full=True)["KeyBERT"].values())]
        #    topic_model.set_topic_labels(keybert_labels) 

    if not assigned_topics:
    # Handle the empty array case
        return "No topics found.", output_list, embeddings_out, data_file_name_no_ext, topic_model, docs
    
    else: 
        print("Topic model created.")

    if not custom_labels_df.empty:
        #print(custom_labels_df.shape)

        #topic_dets = topic_model.get_topic_info()
        #print(topic_dets.shape)

        topic_model.set_topic_labels(list(custom_labels_df.iloc[:,0]))

    # Outputs
    output_list, output_text = save_topic_outputs(topic_model, data_file_name_no_ext, output_list, docs, save_topic_model)

     # If you want to save your embedding files
    if return_intermediate_files == "Yes":
        print("Saving embeddings to file")
        if low_resource_mode == "Yes":
            embeddings_file_name = data_file_name_no_ext + '_' + 'tfidf_embeddings.npz'
        else:
            if embeddings_super_compress == "No":
                embeddings_file_name = data_file_name_no_ext + '_' + 'bge_embeddings.npz'
            else:
                embeddings_file_name = data_file_name_no_ext + '_' + 'bge_embeddings_compress.npz'

        np.savez_compressed(embeddings_file_name, embeddings_out)

        output_list.append(embeddings_file_name)

    all_toc = time.perf_counter()
    time_out = f"All processes took {all_toc - all_tic:0.1f} seconds."
    print(time_out)

    return output_text, output_list, embeddings_out, data_file_name_no_ext, topic_model, docs

def reduce_outliers(topic_model, docs, embeddings_out, data_file_name_no_ext, save_topic_model, progress=gr.Progress(track_tqdm=True)):

    progress(0, desc= "Preparing data")

    output_list = []

    all_tic = time.perf_counter()

    assigned_topics, probs = topic_model.fit_transform(docs, embeddings_out)

    if isinstance(assigned_topics, np.ndarray):
        assigned_topics = assigned_topics.tolist()

    #progress(0.2, desc= "Loading in representation model")
    #print("Create LLM topic labels:", create_llm_topic_labels)
    #from funcs.representation_model import create_representation_model, llm_config, chosen_start_tag
    #representation_model = create_representation_model(create_llm_topic_labels, llm_config, hf_model_name, hf_model_file, chosen_start_tag, low_resource_mode)  

    # Reduce outliers if required, then update representation
    progress(0.2, desc= "Reducing outliers")
    print("Reducing outliers.")
    # Calculate the c-TF-IDF representation for each outlier document and find the best matching c-TF-IDF topic representation using cosine similarity.
    assigned_topics = topic_model.reduce_outliers(docs, assigned_topics, strategy="embeddings")
    # Then, update the topics to the ones that considered the new data

    print("Finished reducing outliers.")

    progress(0.7, desc= "Replacing topic names with LLMs if necessary")
    #print("Create LLM topic labels:", "No")
    #vectoriser_model = CountVectorizer(stop_words="english", ngram_range=(1, 2), min_df=0.1)
    #representation_model = create_representation_model("No", llm_config, hf_model_name, hf_model_file, chosen_start_tag, low_resource_mode) 
    #topic_model.update_topics(docs, topics=assigned_topics, vectorizer_model=vectoriser_model, representation_model=representation_model)

    topic_dets = topic_model.get_topic_info()

    # Replace original labels with LLM labels
    if "LLM" in topic_model.get_topic_info().columns:
        llm_labels = [label[0][0].split("\n")[0] for label in topic_model.get_topics(full=True)["LLM"].values()]
        topic_model.set_topic_labels(llm_labels)
    else:
        topic_model.set_topic_labels(list(topic_dets["Name"]))

    # Outputs   
    progress(0.9, desc= "Saving to file")
    output_list, output_text = save_topic_outputs(topic_model, data_file_name_no_ext, output_list, docs, save_topic_model)

    all_toc = time.perf_counter()
    time_out = f"All processes took {all_toc - all_tic:0.1f} seconds"
    print(time_out)
    
    return output_text, output_list, topic_model

def represent_topics(topic_model, docs, embeddings_out, data_file_name_no_ext, low_resource_mode, save_topic_model, progress=gr.Progress(track_tqdm=True)):
    #from funcs.prompts import capybara_prompt, capybara_start, open_hermes_prompt, open_hermes_start, stablelm_prompt, stablelm_start
    from funcs.representation_model import create_representation_model, llm_config, chosen_start_tag

    output_list = []

    all_tic = time.perf_counter()

    vectoriser_model = CountVectorizer(stop_words="english", ngram_range=(1, 2), min_df=0.1)

    assigned_topics, probs = topic_model.fit_transform(docs, embeddings_out)

    topic_dets = topic_model.get_topic_info()

    progress(0.1, desc= "Loading LLM model")
    print("Create LLM topic labels:", "Yes")
    representation_model = create_representation_model("Yes", llm_config, hf_model_name, hf_model_file, chosen_start_tag, low_resource_mode)  

    topic_model.update_topics(docs, topics=assigned_topics, vectorizer_model=vectoriser_model, representation_model=representation_model)

    # Replace original labels with LLM labels
    if "LLM" in topic_model.get_topic_info().columns:
        llm_labels = [label[0][0].split("\n")[0] for label in topic_model.get_topics(full=True)["LLM"].values()]
        topic_model.set_topic_labels(llm_labels)

        label_list_file_name = data_file_name_no_ext + '_llm_topic_list_' + today_rev + '.csv'

        llm_labels_df = pd.DataFrame(data={"Label":llm_labels})
        llm_labels_df.to_csv(label_list_file_name, index=None)
        #with open(label_list_file_name, 'w') as file:
        #    file.write(f"Label\n")
        #    for item in llm_labels:
        #        file.write(f"{item}\n")
        output_list.append(label_list_file_name)
    else:
        topic_model.set_topic_labels(list(topic_dets["Name"]))

    # Outputs
    progress(0.8, desc= "Saving outputs")
    output_list, output_text = save_topic_outputs(topic_model, data_file_name_no_ext, output_list, docs, save_topic_model)

    all_toc = time.perf_counter()
    time_out = f"All processes took {all_toc - all_tic:0.1f} seconds"
    print(time_out)

    return output_text, output_list, topic_model

def visualise_topics(topic_model, data, data_file_name_no_ext, low_resource_mode,  embeddings_out, in_label, in_colnames, sample_prop, visualisation_type_radio, random_seed, progress=gr.Progress()):

    progress(0, desc= "Preparing data for visualisation")

    output_list = []
    vis_tic = time.perf_counter()

    from funcs.bertopic_vis_documents import visualize_documents_custom, visualize_hierarchical_documents_custom, visualize_barchart_custom

    if not visualisation_type_radio:
        return "Please choose a visualisation type above.", output_list, None, None

    # Get topic labels
    if in_label:
       in_label_list_first = in_label[0]
    else:
       return "Label column not found. Please enter this above.", output_list, None, None
    
    # Get docs
    if in_colnames:
        in_colnames_list_first = in_colnames[0]
    else:
        return "Label column not found. Please enter this on the data load tab.", output_list, None, None
    
    docs = list(data[in_colnames_list_first].str.lower())

    # Make sure format of input series is good
    data[in_label_list_first] = data[in_label_list_first].fillna('').astype(str)
    label_list = list(data[in_label_list_first])

    topic_dets = topic_model.get_topic_info()

    # Replace original labels with LLM labels if they exist, or go with the 'Name' column
    if "LLM" in topic_model.get_topic_info().columns:
        llm_labels = [label[0][0].split("\n")[0] for label in topic_model.get_topics(full=True)["LLM"].values()]
        topic_model.set_topic_labels(llm_labels)
    else:
        topic_model.set_topic_labels(list(topic_dets["Name"]))

    # Pre-reduce embeddings for visualisation purposes
    if low_resource_mode == "No":
        reduced_embeddings = UMAP(n_neighbors=15, n_components=2, min_dist=0.0, metric='cosine', random_state=random_seed).fit_transform(embeddings_out)
    else:
        reduced_embeddings = TruncatedSVD(2, random_state=random_seed).fit_transform(embeddings_out)

    progress(0.5, desc= "Creating visualisation (this can take a while)")
    # Visualise the topics:
    
    print("Creating visualisation")

    # "Topic document graph", "Hierarchical view"

    if visualisation_type_radio == "Topic document graph":
        topics_vis = visualize_documents_custom(topic_model, docs, hover_labels = label_list, reduced_embeddings=reduced_embeddings, hide_annotations=True, hide_document_hover=False, custom_labels=True, sample = sample_prop, width= 1200, height = 750)

        topics_vis_name = data_file_name_no_ext + '_' + 'vis_topic_docs_' + today_rev + '.html'
        topics_vis.write_html(topics_vis_name)
        output_list.append(topics_vis_name)

        topics_vis_2 = visualize_barchart_custom(topic_model, top_n_topics = 12, custom_labels=True, width= 300, height = 250)

        topics_vis_2_name = data_file_name_no_ext + '_' + 'vis_barchart_' + today_rev + '.html'
        topics_vis_2.write_html(topics_vis_2_name)
        output_list.append(topics_vis_2_name)

    elif visualisation_type_radio == "Hierarchical view":
       
        # Check that original topics are retained
        #new_topic_dets = topic_model.get_topic_info()
        #new_topic_dets.to_csv("new_topic_dets.csv")

        #from funcs.bertopic_hierarchical_topics_mod import hierarchical_topics_mod

        hierarchical_topics = topic_model.hierarchical_topics(docs)

        # Save new hierarchical topic model to file
        hierarchical_topics_name = data_file_name_no_ext + '_' + 'vis_hierarchy_topics_' + today_rev + '.csv'
        hierarchical_topics.to_csv(hierarchical_topics_name)
        output_list.append(hierarchical_topics_name)

        #hierarchical_topics = hierarchical_topics_mod(topic_model, docs)
        topics_vis = visualize_hierarchical_documents_custom(topic_model, docs, label_list, hierarchical_topics, reduced_embeddings=reduced_embeddings, sample = sample_prop, hide_document_hover= False, custom_labels=True, width= 1200, height = 750)
        #topics_vis = topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, reduced_embeddings=reduced_embeddings, sample = sample_prop, hide_document_hover= False, custom_labels=True, width= 1200, height = 750)
        topics_vis_2 = topic_model.visualize_hierarchy(hierarchical_topics=hierarchical_topics, width= 1200, height = 750)

        topics_vis_name = data_file_name_no_ext + '_' + 'vis_hierarchy_topic_doc_' + today_rev + '.html'
        topics_vis.write_html(topics_vis_name)
        output_list.append(topics_vis_name)

        topics_vis_2_name = data_file_name_no_ext + '_' + 'vis_hierarchy_' + today_rev + '.html'
        topics_vis_2.write_html(topics_vis_2_name)
        output_list.append(topics_vis_2_name)

    all_toc = time.perf_counter()
    time_out = f"Creating visualisation took {all_toc - vis_tic:0.1f} seconds"
    print(time_out)

    return time_out, output_list, topics_vis, topics_vis_2

def save_as_pytorch_model(topic_model, data_file_name_no_ext , progress=gr.Progress()):

    if not topic_model:
        return "No Pytorch model found.", None

    progress(0, desc= "Saving topic model in Pytorch format")

    output_list = []


    topic_model_save_name_folder = "output_model/" + data_file_name_no_ext + "_topics_" + today_rev# + ".safetensors"
    topic_model_save_name_zip = topic_model_save_name_folder + ".zip"

    # Clear folder before replacing files
    delete_files_in_folder(topic_model_save_name_folder)

    topic_model.save(topic_model_save_name_folder, serialization='pytorch', save_embedding_model=True, save_ctfidf=False)

    # Zip file example
    
    zip_folder(topic_model_save_name_folder, topic_model_save_name_zip)
    output_list.append(topic_model_save_name_zip)

    return "Model saved in Pytorch format.", output_list

# Gradio app

block = gr.Blocks(theme = gr.themes.Base())

with block:

    data_state = gr.State(pd.DataFrame())
    embeddings_state = gr.State(np.array([]))
    topic_model_state = gr.State()
    docs_state = gr.State()
    data_file_name_no_ext_state = gr.State()
    label_list_state = gr.State(pd.DataFrame())
 
    gr.Markdown(
    """
    # Topic modeller
    Generate topics from open text in tabular data. Upload a file (csv, xlsx, or parquet), then specify the open text column that you want to use to generate topics, and another for labels in the visualisation. If you have an embeddings .npz file of the text made using the 'BAAI/bge-small-en-v1.5' model, you can load this in at the same time to skip the first modelling step. If you have a pre-defined list of topics, you can upload this as a csv file under 'I have my own list of topics...'. Further configuration options are available under the 'Options' tab.

    Suggested test dataset: https://huggingface.co/datasets/rag-datasets/mini_wikipedia/tree/main/data (passages.parquet)
    """)    
          
    with gr.Tab("Load files and find topics"):
        with gr.Accordion("Load data file", open = True):
            in_files = gr.File(label="Input text from file", file_count="multiple")
            with gr.Row():
                in_colnames = gr.Dropdown(choices=["Choose a column"], multiselect = True, label="Select column to find topics (first will be chosen if multiple selected).")                

        with gr.Accordion("I have my own list of topics (zero shot topic modelling).", open = False):
            candidate_topics = gr.File(label="Input topics from file (csv). File should have at least one column with a header and topic keywords in cells below. Topics will be taken from the first column of the file. Currently not compatible with low-resource embeddings.")
            zero_shot_similarity = gr.Slider(minimum = 0.5, maximum = 1, value = 0.65, step = 0.001, label = "Minimum similarity value for document to be assigned to zero-shot topic.")

        with gr.Row():
            min_docs_slider = gr.Slider(minimum = 2, maximum = 1000, value = 15, step = 1, label = "Minimum number of similar documents needed to make a topic.")
            max_topics_slider = gr.Slider(minimum = 2, maximum = 500, value = 10, step = 1, label = "Maximum number of topics")

        with gr.Row():
            topics_btn = gr.Button("Extract topics")
            
        with gr.Row():
            output_single_text = gr.Textbox(label="Output topics")
            output_file = gr.File(label="Output file")

        with gr.Accordion("Post processing options.", open = True):
            with gr.Row():
                reduce_outliers_btn = gr.Button("Reduce outliers")
                represent_llm_btn = gr.Button("Generate topic labels with LLMs")
                save_pytorch_btn = gr.Button("Save model in Pytorch format")

        #logs = gr.Textbox(label="Processing logs.")

    with gr.Tab("Visualise"):
        with gr.Row():
            in_label = gr.Dropdown(choices=["Choose a column"], multiselect = True, label="Select column for labelling documents in output visualisations.")
            visualisation_type_radio = gr.Radio(label="Visualisation type", choices=["Topic document graph", "Hierarchical view"])
        sample_slide = gr.Slider(minimum = 0.01, maximum = 1, value = 0.1, step = 0.01, label = "Proportion of data points to show on output visualisations.")
        plot_btn = gr.Button("Visualise topic model")
        with gr.Row():
            vis_output_single_text = gr.Textbox(label="Visualisation output text")
            out_plot_file = gr.File(label="Output plots to file", file_count="multiple")
        plot = gr.Plot(label="Visualise your topics here.")
        plot_2 = gr.Plot(label="Visualise your topics here.")

    
    with gr.Tab("Options"):
        with gr.Accordion("Data load and processing options", open = True):
            with gr.Row():
                anonymise_drop = gr.Dropdown(value = "No", choices=["Yes", "No"], multiselect=False, label="Anonymise data on file load. Names and other details are replaced with tags e.g. '<person>'.")
                embedding_super_compress = gr.Dropdown(label = "Round embeddings to three dp for smaller files with less accuracy.", value="No", choices=["Yes", "No"])
                seed_number = gr.Number(label="Random seed to use for dimensionality reduction.", minimum=0, step=1, value=42, precision=0)
                calc_probs = gr.Dropdown(label="Calculate all topic probabilities (i.e. a separate document prob. value for each topic)", value="No", choices=["Yes", "No"])
            with gr.Row():
                low_resource_mode_opt = gr.Dropdown(label = "Use low resource embeddings and processing.", value="No", choices=["Yes", "No"])
                return_intermediate_files = gr.Dropdown(label = "Return intermediate processing files from file preparation.", value="Yes", choices=["Yes", "No"])
                save_topic_model = gr.Dropdown(label = "Save topic model to file.", value="Yes", choices=["Yes", "No"])

    # Update column names dropdown when file uploaded
    in_files.upload(fn=initial_file_load, inputs=[in_files], outputs=[in_colnames, in_label, data_state, output_single_text, topic_model_state, embeddings_state, data_file_name_no_ext_state, label_list_state])    
    in_colnames.change(dummy_function, in_colnames, None)

    topics_btn.click(fn=extract_topics, inputs=[data_state, in_files, min_docs_slider, in_colnames, max_topics_slider, candidate_topics, data_file_name_no_ext_state, label_list_state, anonymise_drop, return_intermediate_files, embedding_super_compress, low_resource_mode_opt, save_topic_model, embeddings_state, zero_shot_similarity, seed_number, calc_probs], outputs=[output_single_text, output_file, embeddings_state, data_file_name_no_ext_state, topic_model_state, docs_state], api_name="topics")

    reduce_outliers_btn.click(fn=reduce_outliers, inputs=[topic_model_state, docs_state, embeddings_state, data_file_name_no_ext_state, save_topic_model], outputs=[output_single_text, output_file, topic_model_state], api_name="reduce_outliers")

    represent_llm_btn.click(fn=represent_topics, inputs=[topic_model_state, docs_state, embeddings_state, data_file_name_no_ext_state, low_resource_mode_opt, save_topic_model], outputs=[output_single_text, output_file, topic_model_state], api_name="represent_llm")

    save_pytorch_btn.click(fn=save_as_pytorch_model, inputs=[topic_model_state, data_file_name_no_ext_state], outputs=[output_single_text, output_file])

    plot_btn.click(fn=visualise_topics, inputs=[topic_model_state, data_state, data_file_name_no_ext_state, low_resource_mode_opt, embeddings_state, in_label, in_colnames, sample_slide, visualisation_type_radio, seed_number], outputs=[vis_output_single_text, out_plot_file, plot, plot_2], api_name="plot")

    #block.load(read_logs, None, logs, every=5)

block.queue().launch(debug=True)#, server_name="0.0.0.0", ssl_verify=False, server_port=7860)