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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).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) | |