File size: 10,493 Bytes
8499c35
 
 
 
 
 
 
 
8ad45db
8499c35
 
 
35a0403
8499c35
 
 
 
 
 
 
 
 
 
 
 
 
 
b7ef881
8499c35
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
edd60a3
 
8499c35
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
edd60a3
 
8499c35
 
 
edd60a3
 
8499c35
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
edd60a3
8499c35
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
edd60a3
 
8499c35
 
 
 
 
 
 
 
 
 
 
edd60a3
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
import whisper
import os
from pytube import YouTube
import pandas as pd
import plotly_express as px
import nltk
import plotly.graph_objects as go
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
from sentence_transformers import SentenceTransformer, CrossEncoder, util
import streamlit as st
import en_core_web_lg
import validators

nltk.download('punkt')

from nltk import sent_tokenize

@st.experimental_singleton(suppress_st_warning=True)
def load_models():
    asr_model = whisper.load_model("small")
    q_model = ORTModelForSequenceClassification.from_pretrained("nickmuchi/quantized-optimum-finbert-tone")
    ner_model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
    q_tokenizer = AutoTokenizer.from_pretrained("nickmuchi/quantized-optimum-finbert-tone")
    ner_tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
    sent_pipe = pipeline("text-classification",model=q_model, tokenizer=q_tokenizer)
    sum_pipe = pipeline("summarization",model="facebook/bart-large-cnn", tokenizer="facebook/bart-large-cnn")
    ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, grouped_entities=True)
    sbert = SentenceTransformer("all-mpnet-base-v2")
    cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')
    
    return asr_model, sent_pipe, sum_pipe, ner_pipe, sbert, cross_encoder

@st.experimental_singleton(suppress_st_warning=True)
def get_spacy():
    nlp = en_core_web_lg.load()
    return nlp
    
@st.experimental_memo(suppress_st_warning=True)
def inference(link, upload):
    '''Convert Youtube video or Audio upload to text'''
    
    if validators.url(link):
    
      yt = YouTube(link)
      title = yt.title
      path = yt.streams.filter(only_audio=True)[0].download(filename="audio.mp4")
      options = whisper.DecodingOptions(without_timestamps=True)
      results = asr_model.transcribe(path)
      
      return results, yt.title
      
    elif upload:
      results = asr_model.transcribe(upload)
      
      return results, "Transcribed Earnings Audio"
      
@st.experimental_memo(suppress_st_warning=True)
def sentiment_pipe(earnings_text):
    '''Determine the sentiment of the text'''
    
    earnings_sentences = sent_tokenize(earnings_text)
    earnings_sentiment = sent_pipe(earnings_sentences)
    
    return earnings_sentiment, earnings_sentences    
    
@st.experimental_memo(suppress_st_warning=True)
def preprocess_plain_text(text,window_size=3):
    '''Preprocess text for semantic search'''
    
    text = text.encode("ascii", "ignore").decode()  # unicode
    text = re.sub(r"https*\S+", " ", text)  # url
    text = re.sub(r"@\S+", " ", text)  # mentions
    text = re.sub(r"#\S+", " ", text)  # hastags
    text = re.sub(r"\s{2,}", " ", text)  # over spaces
    #text = re.sub("[^.,!?%$A-Za-z0-9]+", " ", text)  # special characters except .,!?
    
    #break into lines and remove leading and trailing space on each
    lines = [line.strip() for line in text.splitlines()]
    
    # #break multi-headlines into a line each
    chunks = [phrase.strip() for line in lines for phrase in line.split("  ")]
    
    # # drop blank lines
    text = '\n'.join(chunk for chunk in chunks if chunk)
    
    ## We split this article into paragraphs and then every paragraph into sentences
    paragraphs = []
    for paragraph in text.replace('\n',' ').split("\n\n"):
        if len(paragraph.strip()) > 0:
            paragraphs.append(sent_tokenize(paragraph.strip()))

    #We combine up to 3 sentences into a passage. You can choose smaller or larger values for window_size
    #Smaller value: Context from other sentences might get lost
    #Lager values: More context from the paragraph remains, but results are longer
    window_size = window_size
    passages = []
    for paragraph in paragraphs:
        for start_idx in range(0, len(paragraph), window_size):
            end_idx = min(start_idx+window_size, len(paragraph))
            passages.append(" ".join(paragraph[start_idx:end_idx]))
        
    print(f"Sentences: {sum([len(p) for p in paragraphs])}")
    print(f"Passages: {len(passages)}")

    return passages
 
@st.experimental_memo(suppress_st_warning=True)    
def chunk_clean_text(text):
    
    """Chunk text longer than 500 tokens"""
    
    article = nlp(text)
    sentences = [i.text for i in list(article.sents)]
    
    current_chunk = 0
    chunks = []
    
    for sentence in sentences:
        if len(chunks) == current_chunk + 1:
            if len(chunks[current_chunk]) + len(sentence.split(" ")) <= 500:
                chunks[current_chunk].extend(sentence.split(" "))
            else:
                current_chunk += 1
                chunks.append(sentence.split(" "))
        else:
            chunks.append(sentence.split(" "))

    for chunk_id in range(len(chunks)):
        chunks[chunk_id] = " ".join(chunks[chunk_id])
    
    return chunks
    
def summary_downloader(raw_text):
    
	b64 = base64.b64encode(raw_text.encode()).decode()
	new_filename = "new_text_file_{}_.txt".format(time_str)
	st.markdown("#### Download Summary as a File ###")
	href = f'<a href="data:file/txt;base64,{b64}" download="{new_filename}">Click to Download!!</a>'
	st.markdown(href,unsafe_allow_html=True)

@st.experimental_memo(suppress_st_warning=True) 	
def get_all_entities_per_sentence(text):
    doc = nlp(''.join(text))

    sentences = list(doc.sents)

    entities_all_sentences = []
    for sentence in sentences:
        entities_this_sentence = []

        # SPACY ENTITIES
        for entity in sentence.ents:
            entities_this_sentence.append(str(entity))

        # FLAIR ENTITIES (CURRENTLY NOT USED)
        # sentence_entities = Sentence(str(sentence))
        # tagger.predict(sentence_entities)
        # for entity in sentence_entities.get_spans('ner'):
        #     entities_this_sentence.append(entity.text)

        # XLM ENTITIES
        entities_xlm = [entity["word"] for entity in ner_model(str(sentence))]
        for entity in entities_xlm:
            entities_this_sentence.append(str(entity))

        entities_all_sentences.append(entities_this_sentence)

    return entities_all_sentences
 
@st.experimental_memo(suppress_st_warning=True)    
def get_all_entities(text):
    all_entities_per_sentence = get_all_entities_per_sentence(text)
    return list(itertools.chain.from_iterable(all_entities_per_sentence))

@st.experimental_memo(suppress_st_warning=True)    
def get_and_compare_entities(article_content,summary_output):
    
    all_entities_per_sentence = get_all_entities_per_sentence(article_content)
    entities_article = list(itertools.chain.from_iterable(all_entities_per_sentence))
   
    all_entities_per_sentence = get_all_entities_per_sentence(summary_output)
    entities_summary = list(itertools.chain.from_iterable(all_entities_per_sentence))
   
    matched_entities = []
    unmatched_entities = []
    for entity in entities_summary:
        if any(entity.lower() in substring_entity.lower() for substring_entity in entities_article):
            matched_entities.append(entity)
        elif any(
                np.inner(sentence_embedding_model.encode(entity, show_progress_bar=False),
                         sentence_embedding_model.encode(art_entity, show_progress_bar=False)) > 0.9 for
                art_entity in entities_article):
            matched_entities.append(entity)
        else:
            unmatched_entities.append(entity)

    matched_entities = list(dict.fromkeys(matched_entities))
    unmatched_entities = list(dict.fromkeys(unmatched_entities))

    matched_entities_to_remove = []
    unmatched_entities_to_remove = []

    for entity in matched_entities:
        for substring_entity in matched_entities:
            if entity != substring_entity and entity.lower() in substring_entity.lower():
                matched_entities_to_remove.append(entity)

    for entity in unmatched_entities:
        for substring_entity in unmatched_entities:
            if entity != substring_entity and entity.lower() in substring_entity.lower():
                unmatched_entities_to_remove.append(entity)

    matched_entities_to_remove = list(dict.fromkeys(matched_entities_to_remove))
    unmatched_entities_to_remove = list(dict.fromkeys(unmatched_entities_to_remove))

    for entity in matched_entities_to_remove:
        matched_entities.remove(entity)
    for entity in unmatched_entities_to_remove:
        unmatched_entities.remove(entity)

    return matched_entities, unmatched_entities

@st.experimental_memo(suppress_st_warning=True) 
def highlight_entities(article_content,summary_output):
   
    markdown_start_red = "<mark class=\"entity\" style=\"background: rgb(238, 135, 135);\">"
    markdown_start_green = "<mark class=\"entity\" style=\"background: rgb(121, 236, 121);\">"
    markdown_end = "</mark>"

    matched_entities, unmatched_entities = get_and_compare_entities(article_content,summary_output)
    
    print(summary_output)

    for entity in matched_entities:
        summary_output = re.sub(f'({entity})(?![^rgb\(]*\))',markdown_start_green + entity + markdown_end,summary_output)

    for entity in unmatched_entities:
        summary_output = re.sub(f'({entity})(?![^rgb\(]*\))',markdown_start_red + entity + markdown_end,summary_output)
    
    print("")
    print(summary_output)
    
    print("")
    print(summary_output)
    
    soup = BeautifulSoup(summary_output, features="html.parser")

    return HTML_WRAPPER.format(soup)
    
    
def display_df_as_table(model,top_k,score='score'):
    '''Display the df with text and scores as a table'''
    
    df = pd.DataFrame([(hit[score],passages[hit['corpus_id']]) for hit in model[0:top_k]],columns=['Score','Text'])
    df['Score'] = round(df['Score'],2)
    
    return df   

      
def make_spans(text,results):
    results_list = []
    for i in range(len(results)):
        results_list.append(results[i]['label'])
    facts_spans = []
    facts_spans = list(zip(sent_tokenizer(text),results_list))
    return facts_spans

##Fiscal Sentiment by Sentence
def fin_ext(text):
    results = remote_clx(sent_tokenizer(text))
    return make_spans(text,results)
    
nlp = get_spacy()    
asr_model, sent_pipe, sum_pipe, ner_pipe, sbert, cross_encoder  = load_models()