Upload app.py

app.py

@@ -0,0 +1,155 @@
+# TODO: Visualizations for topic, text
+# TODO: BERT model switch
+# TODO: Default input
+# TODO: Pre segmented sentences
+# TODO: Progress bar
+
+import streamlit as st
+import pandas as pd
+import re
+import json
+
+from utils import window, get_depths, get_local_maxima, compute_threshold, get_threshold_segments
+
+st.write("loading ...")
+
+import spacy
+nlp = spacy.load('en_core_web_sm')
+
+def print_list(lst):
+    for e in lst:
+        st.markdown("- " + e)
+
+# Demo start
+
+st.subheader("Topic Segmentation Demo")
+
+uploaded_file = st.file_uploader("choose a text file", type=["txt"])
+
+if uploaded_file is not None: 
+    st.session_state["text"] = uploaded_file.getvalue().decode('utf-8')
+
+st.write("OR")
+
+input_text = st.text_area(
+    label="Enter text separated by newlines",
+    value="",
+    key="text",
+    height=150
+
+)
+
+button=st.button('Get Segments')
+
+# Radio bar
+# BERT or TOPIC
+select_names = ["LDA Topic", "BERT"]
+model = st.radio(label='Select model', options=select_names, index=0)
+
+if (button==True) and input_text != "":
+
+    # Parse sample document and break it into sentences
+    texts = input_text.split('\n')
+    sents = []
+    for text in texts:
+        doc = nlp(text)
+        for sent in doc.sents:
+            sents.append(sent)
+
+    # Select tokens while ignoring punctuations and stopwords, and lowercase them
+    MIN_LENGTH = 3
+    tokenized_sents = [[token.lemma_.lower() for token in sent if 
+                        not token.is_stop and not token.is_punct and token.text.strip() and len(token) >= MIN_LENGTH] 
+                        for sent in sents]
+
+
+    st.write("building topic model ...")
+
+    # Build gensim dictionary and topic model
+    from gensim import corpora, models
+    import numpy as np
+
+    np.random.seed(123)
+
+    N_TOPICS = 5
+    N_PASSES = 5
+
+    dictionary = corpora.Dictionary(tokenized_sents)
+    bow = [dictionary.doc2bow(sent) for sent in tokenized_sents]
+    topic_model = models.LdaModel(corpus=bow, id2word=dictionary, num_topics=N_TOPICS, passes=N_PASSES)
+
+    ###st.write(topic_model.show_topics())
+
+
+    st.write("inferring topics ...")
+    # Infer topics with minimum threshold
+    THRESHOLD = 0.05
+    doc_topics = list(topic_model.get_document_topics(bow, minimum_probability=THRESHOLD))
+
+    # st.write(doc_topics)
+
+    # get top k topics for each sentence
+    k = 3
+    top_k_topics = [[t[0] for t in sorted(sent_topics, key=lambda x: x[1], reverse=True)][:k] 
+                    for sent_topics in doc_topics]
+    # st.write(top_k_topics)
+
+    ###st.write("apply window")
+
+    from itertools import chain
+
+    WINDOW_SIZE = 3
+
+    window_topics = window(top_k_topics, n=WINDOW_SIZE)
+    # assert(len(window_topics) == (len(tokenized_sents) - WINDOW_SIZE + 1))
+    window_topics = [list(set(chain.from_iterable(window))) for window in window_topics]
+
+    # Encode topics for similarity computation
+
+    from sklearn.preprocessing import MultiLabelBinarizer
+
+    binarizer = MultiLabelBinarizer(classes=range(N_TOPICS))
+
+    encoded_topic = binarizer.fit_transform(window_topics)
+
+    # Get similarities
+
+    st.write("generating segments ...")
+
+    from sklearn.metrics.pairwise import cosine_similarity
+
+    sims_topic = [cosine_similarity([pair[0]], [pair[1]])[0][0] for pair in zip(encoded_topic, encoded_topic[1:])]
+    # plot
+
+    # Compute depth scores
+    depths_topic = get_depths(sims_topic)
+    # plot
+
+    # Get local maxima
+    filtered_topic = get_local_maxima(depths_topic, order=1)
+    # plot
+
+    ###st.write("compute threshold")
+    # Automatic threshold computation
+    # threshold_topic = compute_threshold(depths_topic)
+    threshold_topic = compute_threshold(filtered_topic)
+
+    # topk_segments = get_topk_segments(filtered_topic, k=5)
+    # Select segments based on threshold
+    threshold_segments_topic = get_threshold_segments(filtered_topic, threshold_topic)
+
+    # st.write(threshold_topic)
+
+    ###st.write("compute segments")
+
+    segment_ids = threshold_segments_topic + WINDOW_SIZE
+
+    segment_ids = [0] + segment_ids.tolist() + [len(sents)]
+    slices = list(zip(segment_ids[:-1], segment_ids[1:]))
+
+    segmented = [sents[s[0]: s[1]] for s in slices]
+
+    for segment in segmented[:-1]:
+        print_list([s.text for s in segment])
+        st.markdown("""---""")
+    print_list([s.text for s in segmented[-1]])