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import streamlit as st | |
import pandas as pd | |
import plotly.graph_objects as go | |
entailment_html_messages = { | |
"entailment": 'The knowledge base seems to <span style="color:green">confirm</span> your statement', | |
"contradiction": 'The knowledge base seems to <span style="color:red">contradict</span> your statement', | |
"neutral": 'The knowledge base is <span style="color:darkgray">neutral</span> about your statement', | |
} | |
def set_state_if_absent(key, value): | |
if key not in st.session_state: | |
st.session_state[key] = value | |
# Small callback to reset the interface in case the text of the question changes | |
def reset_results(*args): | |
st.session_state.answer = None | |
st.session_state.results = None | |
st.session_state.raw_json = None | |
def create_ternary_plot(entailment_data): | |
hover_text = "" | |
for label, value in entailment_data.items(): | |
hover_text += f"{label}: {value}<br>" | |
fig = go.Figure( | |
go.Scatterternary( | |
{ | |
"cliponaxis": False, | |
"mode": "markers", | |
"a": [i for i in map(lambda x: x["entailment"], [entailment_data])], | |
"b": [i for i in map(lambda x: x["contradiction"], [entailment_data])], | |
"c": [i for i in map(lambda x: x["neutral"], [entailment_data])], | |
"hoverinfo": "text", | |
"text": hover_text, | |
"marker": { | |
"color": "#636efa", | |
"size": [0.01], | |
"sizemode": "area", | |
"sizeref": 2.5e-05, | |
"symbol": "circle", | |
}, | |
} | |
) | |
) | |
fig.update_layout( | |
{ | |
"ternary": { | |
"sum": 1, | |
"aaxis": makeAxis("Entailment", 0), | |
"baxis": makeAxis("<br>Contradiction", 45), | |
"caxis": makeAxis("<br>Neutral", -45), | |
} | |
} | |
) | |
return fig | |
def makeAxis(title, tickangle): | |
return { | |
"title": title, | |
"titlefont": {"size": 20}, | |
"tickangle": tickangle, | |
"tickcolor": "rgba(0,0,0,0)", | |
"ticklen": 5, | |
"showline": False, | |
"showgrid": True, | |
} | |
def highlight_cols(s): | |
coldict = {"con": "#FFA07A", "neu": "#E5E4E2", "ent": "#a9d39e"} | |
if s.name in coldict.keys(): | |
return ["background-color: {}".format(coldict[s.name])] * len(s) | |
return [""] * len(s) | |
def create_df_for_relevant_snippets(docs): | |
rows = [] | |
urls = {} | |
for doc in docs: | |
row = { | |
"Title": doc.meta["name"], | |
"Relevance": f"{doc.score:.3f}", | |
"con": f"{doc.meta['entailment_info']['contradiction']:.2f}", | |
"neu": f"{doc.meta['entailment_info']['neutral']:.2f}", | |
"ent": f"{doc.meta['entailment_info']['entailment']:.2f}", | |
"Content": doc.content, | |
} | |
urls[doc.meta["name"]] = doc.meta["url"] | |
rows.append(row) | |
df = pd.DataFrame(rows).style.apply(highlight_cols) | |
return df, urls | |