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| import os | |
| import joblib | |
| import plotly.graph_objects as go | |
| import streamlit_analytics | |
| from huggingface_hub import hf_hub_download | |
| import streamlit as st | |
| import streamlit.components.v1 as components | |
| from lime.lime_text import LimeTextExplainer | |
| from utils import add_logo_to_sidebar, add_footer | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| REPO_ID = "simplexico/cuad-sklearn-clause-classifier" | |
| FILENAME = "CUAD-clause-classifier.pkl" | |
| EXAMPLE_TEXT = """This Agreement and any dispute or claim arising out of or in connection with it | |
| or its subject matter or formation (including non-contractual disputes or claims) shall be | |
| governed by and construed in accordance with the law of England.""" | |
| streamlit_analytics.start_tracking() | |
| ## Layout stuff | |
| st.set_page_config( | |
| page_title="Label Clause 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('🏷 Label Clause Demo') | |
| st.write(""" | |
| This demo shows how AI can be used to label text. | |
| We've trained an AI model to label a clause by its clause type. | |
| """) | |
| def load_model(): | |
| model = joblib.load( | |
| hf_hub_download(repo_id=REPO_ID, filename=FILENAME, token=HF_TOKEN) | |
| ) | |
| return model | |
| def get_prediction_prob(text): | |
| y_pred = model.predict([text])[0] | |
| y_probs = model.predict_proba([text])[0] | |
| return y_pred, y_probs | |
| st.markdown('### 🖊 Enter clause text') | |
| text = st.text_area(label='**Enter Clause Text**', label_visibility='collapsed', value=EXAMPLE_TEXT, height=100) | |
| button = st.button('**Label Clause**', type='primary', use_container_width=True) | |
| with st.spinner('⚙️ Loading model...'): | |
| model = load_model() | |
| classes = [s.upper() for s in model.classes_] | |
| if button: | |
| with st.spinner('⚙️ Processing Clause...'): | |
| y_pred, y_probs = get_prediction_prob(text) | |
| explainer = LimeTextExplainer(class_names=[cls[:9] + '…' for cls in model.classes_]) | |
| exp = explainer.explain_instance(text, | |
| model.predict_proba, | |
| num_features=10, | |
| top_labels=1) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown('### 🤖 Prediction Results') | |
| st.write( | |
| f"The model predicts that this is a **{y_pred}** clause with **{y_probs.max() * 100:.2f}%** confidence.") | |
| fig = go.Figure(go.Bar( | |
| x=y_probs * 100, | |
| y=model.classes_, | |
| orientation='h')) | |
| fig.update_layout( | |
| title="Model Confidence", | |
| xaxis_title="Confidence (%)", | |
| yaxis_title="Clause Type", | |
| ) | |
| st.plotly_chart(fig, use_container_width=True) | |
| with col2: | |
| st.markdown('### 🔮 Prediction Explainability') | |
| st.write( | |
| 'We can perform an analysis to work out what terms in the clause were most important in deciding the predicted clause type:') | |
| components.html(exp.as_html(predict_proba=False), height=600) | |
| add_footer() | |
| streamlit_analytics.stop_tracking(unsafe_password=os.environ["ANALYTICS_PASSWORD"]) |