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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, AutoModelForSeq2SeqLM
from sentence_transformers import SentenceTransformer, CrossEncoder, util
import streamlit as st
import en_core_web_lg
import validators
import re
import itertools
import numpy as np
from bs4 import BeautifulSoup
import base64, time
from annotated_text import annotated_text
import pickle, math
import wikipedia
from pyvis.network import Network
import torch
from langchain.docstore.document import Document
from langchain.embeddings import HuggingFaceEmbeddings,HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import OpenAI
from langchain import VectorDBQA
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from langchain.prompts.base import RegexParser
nltk.download('punkt')
from nltk import sent_tokenize
OPEN_AI_KEY = os.environ.get('OPEN_AI_KEY')
time_str = time.strftime("%d%m%Y-%H%M%S")
HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem;
margin-bottom: 2.5rem">{}</div> """
#Stuff Chain Type Prompt template
output_parser = RegexParser(
regex=r"(.*?)\nScore: (.*)",
output_keys=["answer", "score"],
)
template = """Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES").
If you don't know the answer, just say that you don't know. Don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer.
In addition to giving an answer, also return a score of how fully it answered the user's question. This should be in the following format:
Question: [question here]
Helpful Answer: [answer here]
Score: [score between 0 and 100]
Begin!
Context:
---------
{summaries}
---------
Question: {question}
Helpful Answer:"""
#Refine Chain Type Prompt Template
refine_prompt_template = (
"The original question is as follows: {question}\n"
"We have provided an existing answer: {existing_answer}\n"
"We have the opportunity to refine the existing answer"
"(only if needed) with some more context below.\n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the new context, refine the original answer to better "
"answer the question. "
"If the context isn't useful, return the original answer."
)
refine_prompt = PromptTemplate(
input_variables=["question", "existing_answer", "context_str"],
template=refine_prompt_template,
)
initial_qa_template = (
"Context information is below. \n"
"---------------------\n"
"{context_str}"
"\n---------------------\n"
"Given the context information and not prior knowledge, "
"answer the question: {question}\n.\n"
)
###################### Functions #######################################################################################
@st.experimental_singleton(suppress_st_warning=True)
def load_models():
'''Load and cache all the models to be used'''
q_model = ORTModelForSequenceClassification.from_pretrained("nickmuchi/quantized-optimum-finbert-tone")
ner_model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
kg_model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/rebel-large")
kg_tokenizer = AutoTokenizer.from_pretrained("Babelscape/rebel-large")
q_tokenizer = AutoTokenizer.from_pretrained("nickmuchi/quantized-optimum-finbert-tone")
ner_tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
emb_tokenizer = AutoTokenizer.from_pretrained('google/flan-t5-xl')
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",clean_up_tokenization_spaces=True)
ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, grouped_entities=True)
cross_encoder = CrossEncoder('cross-encoder/mmarco-mMiniLMv2-L12-H384-v1') #cross-encoder/ms-marco-MiniLM-L-12-v2
sbert = SentenceTransformer(all-MiniLM-L6-v2)
return sent_pipe, sum_pipe, ner_pipe, cross_encoder, kg_model, kg_tokenizer, emb_tokenizer, sbert
@st.experimental_singleton(suppress_st_warning=True)
def load_asr_model(asr_model_name):
asr_model = whisper.load_model(asr_model_name)
return asr_model
@st.experimental_singleton(suppress_st_warning=True)
def process_corpus(corpus, _tokenizer, title, embedding_model, chunk_size=200, overlap=50):
'''Process text for Semantic Search'''
text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(_tokenizer,chunk_size=chunk_size,chunk_overlap=overlap,separator='.')
texts = text_splitter.split_text(corpus)
embeddings = gen_embeddings(embedding_model)
docsearch = FAISS.from_texts(texts, embeddings, metadatas=[{"source": i} for i in range(len(texts))])
return docsearch
@st.experimental_singleton(suppress_st_warning=True)
def chunk_and_preprocess_text(text,thresh=500):
"""Chunk text longer than n tokens for summarization"""
sentences = sent_tokenize(clean_text(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(" ")) <= thresh:
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
@st.experimental_singleton(suppress_st_warning=True)
def gen_embeddings(embedding_model):
'''Generate embeddings for given model'''
if 'hkunlp' in embedding_model:
embeddings = HuggingFaceInstructEmbeddings(model_name=embedding_model,
query_instruction='Represent the Financial question for retrieving supporting paragraphs: ',
embed_instruction='Represent the Financial paragraph for retrieval: ')
else:
embeddings = HuggingFaceEmbeddings(model_name=embedding_model)
return embeddings
@st.experimental_memo(suppress_st_warning=True)
def embed_text(query,title,embedding_model,_emb_tok,docsearch,chain_type):
'''Embed text and generate semantic search scores'''
title = title.split()[0].lower()
docs = docsearch.similarity_search_with_score(query, k=3)
if chain_type == 'Normal':
docs = [d[0] for d in docs]
PROMPT = PromptTemplate(template=template,
input_variables=["summaries", "question"],
output_parser=output_parser)
chain = load_qa_with_sources_chain(OpenAI(temperature=0),
chain_type="stuff",
prompt=PROMPT,
)
answer = chain({"input_documents": docs, "question": query}, return_only_outputs=False)
elif chain_type == 'Refined':
docs = [d[0] for d in docs]
initial_qa_prompt = PromptTemplate(
input_variables=["context_str", "question"], template=initial_qa_template
)
chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine", return_refine_steps=False,
question_prompt=initial_qa_prompt, refine_prompt=refine_prompt)
answer = chain({"input_documents": docs, "question": query}, return_only_outputs=False)
return answer
@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, _asr_model):
'''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")
results = _asr_model.transcribe(path, task='transcribe', language='en')
return results['text'], yt.title
elif upload:
results = _asr_model.trasncribe(upload, task='transcribe', language='en')
return results['text'], "Transcribed Earnings Audio"
@st.experimental_memo(suppress_st_warning=True)
def sentiment_pipe(earnings_text):
'''Determine the sentiment of the text'''
earnings_sentences = chunk_long_text(earnings_text,150,1,1)
earnings_sentiment = sent_pipe(earnings_sentences)
return earnings_sentiment, earnings_sentences
@st.experimental_memo(suppress_st_warning=True)
def summarize_text(text_to_summarize,max_len,min_len):
'''Summarize text with HF model'''
summarized_text = sum_pipe(text_to_summarize,max_length=max_len,min_length=min_len,clean_up_tokenization_spaces=True,no_repeat_ngram_size=4,
encoder_no_repeat_ngram_size=3,
repetition_penalty=3.5,
num_beams=4,
early_stopping=True)
summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text])
return summarized_text
@st.experimental_memo(suppress_st_warning=True)
def clean_text(text):
'''Clean all text'''
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
return text
@st.experimental_memo(suppress_st_warning=True)
def chunk_long_text(text,threshold,window_size=3,stride=2):
'''Preprocess text and chunk for sentiment analysis'''
#Convert cleaned text into sentences
sentences = sent_tokenize(text)
out = []
#Limit the length of each sentence to a threshold
for chunk in sentences:
if len(chunk.split()) < threshold:
out.append(chunk)
else:
words = chunk.split()
num = int(len(words)/threshold)
for i in range(0,num*threshold+1,threshold):
out.append(' '.join(words[i:threshold+i]))
passages = []
#Combine sentences into a window of size window_size
for paragraph in [out]:
for start_idx in range(0, len(paragraph), stride):
end_idx = min(start_idx+window_size, len(paragraph))
passages.append(" ".join(paragraph[start_idx:end_idx]))
return passages
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))
# XLM ENTITIES
entities_xlm = [entity["word"] for entity in ner_pipe(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(sbert.encode(entity, show_progress_bar=False),
sbert.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)
## Knowledge Graphs code
def extract_relations_from_model_output(text):
relations = []
relation, subject, relation, object_ = '', '', '', ''
text = text.strip()
current = 'x'
text_replaced = text.replace("<s>", "").replace("<pad>", "").replace("</s>", "")
for token in text_replaced.split():
if token == "<triplet>":
current = 't'
if relation != '':
relations.append({
'head': subject.strip(),
'type': relation.strip(),
'tail': object_.strip()
})
relation = ''
subject = ''
elif token == "<subj>":
current = 's'
if relation != '':
relations.append({
'head': subject.strip(),
'type': relation.strip(),
'tail': object_.strip()
})
object_ = ''
elif token == "<obj>":
current = 'o'
relation = ''
else:
if current == 't':
subject += ' ' + token
elif current == 's':
object_ += ' ' + token
elif current == 'o':
relation += ' ' + token
if subject != '' and relation != '' and object_ != '':
relations.append({
'head': subject.strip(),
'type': relation.strip(),
'tail': object_.strip()
})
return relations
def from_text_to_kb(text, model, tokenizer, article_url, span_length=128, article_title=None,
article_publish_date=None, verbose=False):
# tokenize whole text
inputs = tokenizer([text], return_tensors="pt")
# compute span boundaries
num_tokens = len(inputs["input_ids"][0])
if verbose:
print(f"Input has {num_tokens} tokens")
num_spans = math.ceil(num_tokens / span_length)
if verbose:
print(f"Input has {num_spans} spans")
overlap = math.ceil((num_spans * span_length - num_tokens) /
max(num_spans - 1, 1))
spans_boundaries = []
start = 0
for i in range(num_spans):
spans_boundaries.append([start + span_length * i,
start + span_length * (i + 1)])
start -= overlap
if verbose:
print(f"Span boundaries are {spans_boundaries}")
# transform input with spans
tensor_ids = [inputs["input_ids"][0][boundary[0]:boundary[1]]
for boundary in spans_boundaries]
tensor_masks = [inputs["attention_mask"][0][boundary[0]:boundary[1]]
for boundary in spans_boundaries]
inputs = {
"input_ids": torch.stack(tensor_ids),
"attention_mask": torch.stack(tensor_masks)
}
# generate relations
num_return_sequences = 3
gen_kwargs = {
"max_length": 256,
"length_penalty": 0,
"num_beams": 3,
"num_return_sequences": num_return_sequences
}
generated_tokens = model.generate(
**inputs,
**gen_kwargs,
)
# decode relations
decoded_preds = tokenizer.batch_decode(generated_tokens,
skip_special_tokens=False)
# create kb
kb = KB()
i = 0
for sentence_pred in decoded_preds:
current_span_index = i // num_return_sequences
relations = extract_relations_from_model_output(sentence_pred)
for relation in relations:
relation["meta"] = {
article_url: {
"spans": [spans_boundaries[current_span_index]]
}
}
kb.add_relation(relation, article_title, article_publish_date)
i += 1
return kb
def get_article(url):
article = Article(url)
article.download()
article.parse()
return article
def from_url_to_kb(url, model, tokenizer):
article = get_article(url)
config = {
"article_title": article.title,
"article_publish_date": article.publish_date
}
kb = from_text_to_kb(article.text, model, tokenizer, article.url, **config)
return kb
def get_news_links(query, lang="en", region="US", pages=1):
googlenews = GoogleNews(lang=lang, region=region)
googlenews.search(query)
all_urls = []
for page in range(pages):
googlenews.get_page(page)
all_urls += googlenews.get_links()
return list(set(all_urls))
def from_urls_to_kb(urls, model, tokenizer, verbose=False):
kb = KB()
if verbose:
print(f"{len(urls)} links to visit")
for url in urls:
if verbose:
print(f"Visiting {url}...")
try:
kb_url = from_url_to_kb(url, model, tokenizer)
kb.merge_with_kb(kb_url)
except ArticleException:
if verbose:
print(f" Couldn't download article at url {url}")
return kb
def save_network_html(kb, filename="network.html"):
# create network
net = Network(directed=True, width="700px", height="700px")
# nodes
color_entity = "#00FF00"
for e in kb.entities:
net.add_node(e, shape="circle", color=color_entity)
# edges
for r in kb.relations:
net.add_edge(r["head"], r["tail"],
title=r["type"], label=r["type"])
# save network
net.repulsion(
node_distance=200,
central_gravity=0.2,
spring_length=200,
spring_strength=0.05,
damping=0.09
)
net.set_edge_smooth('dynamic')
net.show(filename)
def save_kb(kb, filename):
with open(filename, "wb") as f:
pickle.dump(kb, f)
class CustomUnpickler(pickle.Unpickler):
def find_class(self, module, name):
if name == 'KB':
return KB
return super().find_class(module, name)
def load_kb(filename):
res = None
with open(filename, "rb") as f:
res = CustomUnpickler(f).load()
return res
class KB():
def __init__(self):
self.entities = {} # { entity_title: {...} }
self.relations = [] # [ head: entity_title, type: ..., tail: entity_title,
# meta: { article_url: { spans: [...] } } ]
self.sources = {} # { article_url: {...} }
def merge_with_kb(self, kb2):
for r in kb2.relations:
article_url = list(r["meta"].keys())[0]
source_data = kb2.sources[article_url]
self.add_relation(r, source_data["article_title"],
source_data["article_publish_date"])
def are_relations_equal(self, r1, r2):
return all(r1[attr] == r2[attr] for attr in ["head", "type", "tail"])
def exists_relation(self, r1):
return any(self.are_relations_equal(r1, r2) for r2 in self.relations)
def merge_relations(self, r2):
r1 = [r for r in self.relations
if self.are_relations_equal(r2, r)][0]
# if different article
article_url = list(r2["meta"].keys())[0]
if article_url not in r1["meta"]:
r1["meta"][article_url] = r2["meta"][article_url]
# if existing article
else:
spans_to_add = [span for span in r2["meta"][article_url]["spans"]
if span not in r1["meta"][article_url]["spans"]]
r1["meta"][article_url]["spans"] += spans_to_add
def get_wikipedia_data(self, candidate_entity):
try:
page = wikipedia.page(candidate_entity, auto_suggest=False)
entity_data = {
"title": page.title,
"url": page.url,
"summary": page.summary
}
return entity_data
except:
return None
def add_entity(self, e):
self.entities[e["title"]] = {k:v for k,v in e.items() if k != "title"}
def add_relation(self, r, article_title, article_publish_date):
# check on wikipedia
candidate_entities = [r["head"], r["tail"]]
entities = [self.get_wikipedia_data(ent) for ent in candidate_entities]
# if one entity does not exist, stop
if any(ent is None for ent in entities):
return
# manage new entities
for e in entities:
self.add_entity(e)
# rename relation entities with their wikipedia titles
r["head"] = entities[0]["title"]
r["tail"] = entities[1]["title"]
# add source if not in kb
article_url = list(r["meta"].keys())[0]
if article_url not in self.sources:
self.sources[article_url] = {
"article_title": article_title,
"article_publish_date": article_publish_date
}
# manage new relation
if not self.exists_relation(r):
self.relations.append(r)
else:
self.merge_relations(r)
def get_textual_representation(self):
res = ""
res += "### Entities\n"
for e in self.entities.items():
# shorten summary
e_temp = (e[0], {k:(v[:100] + "..." if k == "summary" else v) for k,v in e[1].items()})
res += f"- {e_temp}\n"
res += "\n"
res += "### Relations\n"
for r in self.relations:
res += f"- {r}\n"
res += "\n"
res += "### Sources\n"
for s in self.sources.items():
res += f"- {s}\n"
return res
def save_network_html(kb, filename="network.html"):
# create network
net = Network(directed=True, width="700px", height="700px", bgcolor="#eeeeee")
# nodes
color_entity = "#00FF00"
for e in kb.entities:
net.add_node(e, shape="circle", color=color_entity)
# edges
for r in kb.relations:
net.add_edge(r["head"], r["tail"],
title=r["type"], label=r["type"])
# save network
net.repulsion(
node_distance=200,
central_gravity=0.2,
spring_length=200,
spring_strength=0.05,
damping=0.09
)
net.set_edge_smooth('dynamic')
net.show(filename)
nlp = get_spacy()
sent_pipe, sum_pipe, ner_pipe, cross_encoder, kg_model, kg_tokenizer, emb_tokenizer, sbert = load_models()
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