import streamlit as st import time import logging from json import JSONDecodeError # from markdown import markdown # from annotated_text import annotation # from urllib.parse import unquote import random import pandas as pd from app_utils.backend_utils import load_statements, query from app_utils.frontend_utils import set_state_if_absent, reset_results, entailment_html_messages from app_utils.config import RETRIEVER_TOP_K def main(): statements = load_statements() # Persistent state set_state_if_absent('statement', "Elvis Presley is alive") set_state_if_absent('answer', '') set_state_if_absent('results', None) set_state_if_absent('raw_json', None) set_state_if_absent('random_statement_requested', False) ## MAIN CONTAINER st.write("# Fact checking 🎸 Rocks!") st.write() st.markdown(""" ##### Enter a factual statement about [Rock music](https://en.wikipedia.org/wiki/List_of_mainstream_rock_performers) and let the AI check it out for you... """) # Search bar statement = st.text_input("", value=st.session_state.statement, max_chars=100, on_change=reset_results) col1, col2 = st.columns(2) col1.markdown( "", unsafe_allow_html=True) col2.markdown( "", unsafe_allow_html=True) # Run button run_pressed = col1.button("Run") # Random statement button if col2.button("Random statement"): reset_results() statement = random.choice(statements) # Avoid picking the same statement twice (the change is not visible on the UI) while statement == st.session_state.statement: statement = random.choice(statements) st.session_state.statement = statement st.session_state.random_statement_requested = True # Re-runs the script setting the random statement as the textbox value # Unfortunately necessary as the Random statement button is _below_ the textbox # raise st.script_runner.RerunException( # st.script_request_queue.RerunData(None)) else: st.session_state.random_statement_requested = False run_query = (run_pressed or statement != st.session_state.statement) \ and not st.session_state.random_statement_requested # Get results for query if run_query and statement: time_start = time.time() reset_results() st.session_state.statement = statement with st.spinner("🧠    Performing neural search on documents..."): try: st.session_state.results = query( statement, RETRIEVER_TOP_K) time_end = time.time() print(time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime())) print(f'elapsed time: {time_end - time_start}') except JSONDecodeError as je: st.error( "👓    An error occurred reading the results. Is the document store working?") return except Exception as e: logging.exception(e) st.error("🐞    An error occurred during the request.") return # Display results if st.session_state.results: results = st.session_state.results docs, agg_entailment_info = results['documents'], results['agg_entailment_info'] print(results) max_key = max(agg_entailment_info, key=agg_entailment_info.get) message = entailment_html_messages[max_key] st.markdown(f'

{message}

', unsafe_allow_html=True) st.markdown(f'###### Aggregate entailment information:') st.write(results['agg_entailment_info']) st.markdown(f'###### Relevant snippets:') # colms = st.columns((2, 5, 1, 1, 1, 1)) # fields = ["Page title",'Content', 'Relevance', 'contradiction', 'neutral', 'entailment'] # for col, field_name in zip(colms, fields): # # header # col.write(field_name) df = [] for doc in docs: # col1, col2, col3, col4, col5, col6 = st.columns((2, 5, 1, 1, 1, 1)) # col1.write(f"[{doc.meta['name']}]({doc.meta['url']})") # col2.write(f"{doc.content}") # col3.write(f"{doc.score:.3f}") # col4.write(f"{doc.meta['entailment_info']['contradiction']:.2f}") # col5.write(f"{doc.meta['entailment_info']['neutral']:.2f}") # col6.write(f"{doc.meta['entailment_info']['entailment']:.2f}") # 'con': f"{doc.meta['entailment_info']['contradiction']:.2f}", # 'neu': f"{doc.meta['entailment_info']['neutral']:.2f}", # 'ent': f"{doc.meta['entailment_info']['entailment']:.2f}", # # 'url': doc.meta['url'], # 'Content': doc.content} # # # 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}", # 'url': doc.meta['url'], 'Content': doc.content} df.append(row) st.dataframe(pd.DataFrame(df))#.style.apply(highlight)) # if len(st.session_state.results['answers']) == 0: # st.info("""🤔    Haystack is unsure whether any of # the documents contain an answer to your question. Try to reformulate it!""") # for result in st.session_state.results['answers']: # result = result.to_dict() # if result["answer"]: # if alert_irrelevance and result['score'] < LOW_RELEVANCE_THRESHOLD: # alert_irrelevance = False # st.write(""" #

Attention, the # following answers have low relevance:

""", # unsafe_allow_html=True) # answer, context = result["answer"], result["context"] # start_idx = context.find(answer) # end_idx = start_idx + len(answer) # # Hack due to this bug: https://github.com/streamlit/streamlit/issues/3190 # st.write(markdown("- ..."+context[:start_idx] + # str(annotation(answer, "ANSWER", "#3e1c21", "white")) + # context[end_idx:]+"..."), unsafe_allow_html=True) # source = "" # name = unquote(result['meta']['name']).replace('_', ' ') # url = result['meta']['url'] # source = f"[{name}]({url})" # st.markdown( # f"**Score:** {result['score']:.2f} - **Source:** {source}") # def make_pretty(styler): # styler.set_caption("Weather Conditions") # # styler.format(rain_condition) # styler.format_con(lambda v: v.float(v)) # styler.background_gradient(axis=None, vmin=0, vmax=1, cmap="YlGnBu") # return styler def highlight(s): return ['background-color: red']*5 main()