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| import os | |
| import streamlit as st | |
| import difflib | |
| import spacy | |
| import streamlit_analytics | |
| from utils import add_logo_to_sidebar, add_footer | |
| def load_model(): | |
| return spacy.load('en_core_web_md') | |
| streamlit_analytics.start_tracking() | |
| ## Layout stuff | |
| st.set_page_config( | |
| page_title="Compare 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('🔗 Compare Demo') | |
| st.write("""This demo shows how AI can be used to compare passages of text.""") | |
| with st.spinner('⚙️ Loading model...'): | |
| nlp = load_model() | |
| EXAMPLE_TEXT_1 = """This Agreement shall be governed by and interpreted under the laws of the | |
| State of Delaware without regard to its conflicts of law provisions.""" | |
| EXAMPLE_TEXT_2 = """This agreement will be governed by and must be construed in accordance with the laws of the State of Israel.""" | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown('### 🖊 Enter a passage of text') | |
| text_1 = st.text_area('Enter a passage of text', label_visibility='collapsed', value=EXAMPLE_TEXT_1, height=100, key='input1') | |
| with col2: | |
| st.markdown('### 🖊 Enter a second passage of text') | |
| text_2 = st.text_area('Enter a second passage of text', label_visibility='collapsed', value=EXAMPLE_TEXT_2, height=100, key='input2') | |
| button = st.button('Compare', type='primary', use_container_width=True) | |
| def get_tokens(doc): | |
| return [token.lower for token in doc] | |
| def get_pos_tags(doc): | |
| return [token.pos_ for token in doc] | |
| def add_md_color(text, match): | |
| color = 'green' if match else 'red' | |
| return f":{color}[{text}]" | |
| def add_em(text, match): | |
| if match: | |
| return f"**{text}**" | |
| else: | |
| return f"*{text}*" | |
| def create_str_output(doc, idxs): | |
| out = [] | |
| for token in doc: | |
| text = token.text | |
| # higlight word diff | |
| if any(token.i in range(start, end) for start, end in idxs): | |
| text = add_md_color(text, match=True) | |
| else: | |
| text = add_md_color(text, match=False) | |
| out.append(text) | |
| return ' '.join(out) | |
| def get_matching_idxs(items_1, items_2): | |
| sm = difflib.SequenceMatcher(None, items_1, items_2) | |
| matching_blocks = [match for match in sm.get_matching_blocks()] | |
| doc_1_matching_idxs = [] | |
| doc_2_matching_idxs = [] | |
| for a, b, n in matching_blocks: | |
| doc_1_matching_idxs.append((a, a + n)) | |
| doc_2_matching_idxs.append((b, b + n)) | |
| return doc_1_matching_idxs, doc_2_matching_idxs | |
| if button: | |
| with st.spinner('⚙️ Comparing Texts...'): | |
| doc_1 = nlp(text_1) | |
| doc_2 = nlp(text_2) | |
| st.header('🧪 Comparison') | |
| st.markdown('We can highlight the :green[similarities] and :red[differences] in **wording** across the two texts.') | |
| doc_1_token_idxs, doc_2_token_idxs = get_matching_idxs(get_tokens(doc_1), get_tokens(doc_2)) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown(create_str_output(doc_1, doc_1_token_idxs)) | |
| with col2: | |
| st.markdown(create_str_output(doc_2, doc_2_token_idxs)) | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| # perform simple sequence matching | |
| sm = difflib.SequenceMatcher(None, get_tokens(doc_1), get_tokens(doc_2)) | |
| st.subheader('📑 Textual Similarity') | |
| st.markdown('We can measure the similarity based on the *wording* of the two texts.') | |
| st.metric(label='Textual Similarity', value=f"{sm.ratio() * 100:.1f}%") | |
| with col2: | |
| st.subheader('📏 Linguistic Similarity') | |
| st.markdown('We can measure the similarity based on the *linguistic features* of the two texts.') | |
| postags_1 = [token.pos_ for token in doc_1] | |
| postags_2 = [token.pos_ for token in doc_2] | |
| sm = difflib.SequenceMatcher(None, postags_1, postags_2) | |
| st.metric(label='Linguistic Similarity', value=f"{sm.ratio() * 100:.1f}%") | |
| with col3: | |
| st.subheader('💭 Semantic Similarity') | |
| st.markdown('We can measure the similarity based on the *meaning* of the two texts.') | |
| st.metric(label='Semantic Similarity', value=f"{doc_1.similarity(doc_2) * 100:.1f}%") | |
| add_footer() | |
| streamlit_analytics.stop_tracking(unsafe_password=os.environ["ANALYTICS_PASSWORD"]) |