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| import os | |
| import re | |
| import streamlit_analytics | |
| from huggingface_hub import snapshot_download | |
| import streamlit as st | |
| import streamlit.components.v1 as components | |
| import spacy | |
| from spacy import displacy | |
| from spacy.tokens import Span | |
| import pandas as pd | |
| import numpy as np | |
| from utils import add_logo_to_sidebar, add_footer | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| REPO_ID = "simplexico/cuad-spacy-custom-ner" | |
| EXAMPLE_TEXT = """Exhibit 10.16 CONSULTING AGREEMENT | |
| This Consulting Agreement (the "Agreement") is made and entered into as of this 2nd day of January 2020, | |
| by and between Global Technologies, Ltd (hereinafter the "Company"), | |
| a Delaware corporation whose address is 501 1st Ave N., Suite 901, St. Petersburg, FL 33701 and Timothy Cabrera (hereinafter the "Consultant"), | |
| an individual whose address is 11718 SE Federal Hwy., Suite 372, Hobe Sound, FL 33455 (individually, a "Party"; collectively, the "Parties").""" | |
| streamlit_analytics.start_tracking() | |
| ## Layout stuff | |
| st.set_page_config( | |
| page_title="Extract Demo", | |
| page_icon="⛏", | |
| layout="wide", | |
| initial_sidebar_state="expanded", | |
| menu_items={ | |
| 'Get Help': 'mailto:hello@simplexico.ai', | |
| 'Report a bug': None, | |
| 'About': "## This a demo showcasing different Legal AI Actions" | |
| } | |
| ) | |
| add_logo_to_sidebar() | |
| st.title('⛏ Extract Demo') | |
| st.write(""" | |
| This demo shows how AI can be used to extract information from text. | |
| We've trained an AI model to extract key pieces of information from a contract recital. | |
| """) | |
| def load_model(): | |
| snapshot_download(repo_id=REPO_ID, token=HF_TOKEN, local_dir='./') | |
| nlp = spacy.load('model-best') | |
| return nlp | |
| st.markdown('### 🖊 Enter a contract recital') | |
| text = st.text_area('Enter Clause Text', label_visibility='collapsed', value=EXAMPLE_TEXT, height=100) | |
| button = st.button('Extract Data', type='primary', use_container_width=True) | |
| with st.spinner('⚙️ Loading model...'): | |
| nlp = load_model() | |
| def check_span_pair_for_overlap(span1, span2): | |
| """ Checks a pair of spans for any overlapping ranges | |
| Args: | |
| span1: (start, end) tuple | |
| span2: (start, end) tuple | |
| Return: | |
| True if overlap, False otherwise | |
| """ | |
| # remove offset | |
| minimum = min(span1[0], span2[0]) | |
| span1 = (span1[0] - minimum, span1[1] - minimum) | |
| span2 = (span2[0] - minimum, span2[1] - minimum) | |
| maximum = max(span1[1], span2[1]) | |
| vec1 = np.zeros(maximum) | |
| vec1[span1[0]:span1[1]] = 1 | |
| vec2 = np.zeros(maximum) | |
| vec2[span2[0]:span2[1]] = 1 | |
| if np.dot(vec1, vec2): | |
| return True | |
| return False | |
| def add_detected_persons_as_parties(doc): | |
| nlp = spacy.load('en_core_web_md') | |
| doc_ = nlp(doc.text) | |
| original_ents = list(doc.ents) | |
| for ent in doc_.ents: | |
| if ent.label_ == 'PERSON': | |
| if not any([check_span_pair_for_overlap((ent.start, ent.end), (ent_.start, ent_.end)) for ent_ in | |
| original_ents]): | |
| print(ent) | |
| # check for overlapping ents | |
| original_ents.append(Span(doc, ent.start, ent.end, label='parties')) | |
| doc.ents = original_ents | |
| return doc | |
| def add_rule_based_entites(doc): | |
| """Adds rule based entity spans to document | |
| Args: | |
| doc (spacy.tokens.doc.Doc) | |
| """ | |
| patterns = [ | |
| ('[0-9]+[\s]+[a-zA-Z0-9.\-\,\#]+[\s]*[a-zA-Z0-9.\-\,\#]+[a-zA-Z0-9\s.\-\,\#]*\s[0-9]+', 'address'), | |
| ('Consultant|Company|Party|Parties', 'role'), | |
| ] | |
| for pattern, label in patterns: | |
| ents = [] | |
| for match in re.finditer(pattern, doc.text): | |
| start, end = match.span() | |
| span = doc.char_span(start, end) | |
| if span is not None: | |
| ents.append((span.start, span.end, span.text)) | |
| original_ents = list(doc.ents) | |
| for ent in ents: | |
| start, end, address = ent | |
| per_ent = Span(doc, start, end, label=label) | |
| original_ents.append(per_ent) | |
| doc.ents = original_ents | |
| return doc | |
| if button: | |
| col1, col2 = st.columns(2) | |
| with st.spinner('⚙️ Extracting Data...'): | |
| doc = nlp(text) | |
| doc = add_rule_based_entites(doc) | |
| doc = add_detected_persons_as_parties(doc) | |
| with col1: | |
| st.subheader('🎨 Highlighted Text') | |
| colors = {'party': "#85C1E9", "address": "#ff6961", "agreement_date": "#5de36f", "role": "#b05de3"} | |
| options = {"ents": ['party', 'address', 'agreement_date', 'role'], "colors": colors} | |
| label_aliases = { | |
| 'parties': 'Party', | |
| 'address': 'Address', | |
| 'agreement_date': 'Agreement Date', | |
| 'role': 'Role' | |
| } | |
| doc.spans["sc"] = [ | |
| Span(doc, ent.start, ent.end, label_aliases[ent.label_]) for ent in doc.ents | |
| ] | |
| html = displacy.render(doc, style="span", options=options) | |
| components.html(html, height=400) | |
| with col2: | |
| # display table | |
| data = { | |
| 'Text': [], | |
| 'Label': [] | |
| } | |
| st.subheader('📊 Extracted Data') | |
| for span in doc.spans['sc']: | |
| data['Label'].append(span.label_) | |
| data['Text'].append(span.text) | |
| df = pd.DataFrame(data) | |
| hide_dataframe_row_index = """ | |
| <style> | |
| .row_heading.level0 {display:none} | |
| .blank {display:none} | |
| </style> | |
| """ | |
| # Inject CSS with Markdown | |
| st.markdown(hide_dataframe_row_index, unsafe_allow_html=True) | |
| st.table(df) | |
| add_footer() | |
| streamlit_analytics.stop_tracking(unsafe_password=os.environ["ANALYTICS_PASSWORD"]) |