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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 build_sidebar():
sidebar = """
<h1 style='text-align: center'>Fact Checking 🎸 Rocks!</h1>
<div style='text-align: center'>
<i>Fact checking baseline combining dense retrieval and textual entailment</i>
<p><br/><a href='https://github.com/anakin87/fact-checking-rocks'>Github project</a> - Based on <a href='https://github.com/deepset-ai/haystack'>Haystack</a></p>
<p><small><a href='https://en.wikipedia.org/wiki/List_of_mainstream_rock_performers'>Data crawled from Wikipedia</a></small></p>
</div>
"""
st.sidebar.markdown(sidebar, unsafe_allow_html=True)
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):
"""
Create a Plotly ternary plot for the given entailment dict.
"""
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 create_df_for_relevant_snippets(docs):
"""
Create a dataframe that contains all relevant snippets.
Also returns the URLs
"""
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)
df["Content"] = df["Content"].str.wrap(75)
df = df.style.apply(highlight_cols)
return df, urls
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)
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