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import streamlit as st
from functions import *
st.set_page_config(page_title="Earnings Semantic Search", page_icon="🔎")
st.sidebar.header("Semantic Search")
st.markdown("## Earnings Semantic Search with SBert")
search_input = st.text_input(
label='Enter Your Search Query, e.g "What challenges did the business face?"', key='search')
top_k = st.sidebar.slider("Number of Top Hits Generated",min_value=1,max_value=5,value=2)
window_size = st.sidebar.slider("Number of Sentences Generated in Search Response",min_value=1,max_value=5,value=3)
## Save to a dataframe for ease of visualization
sen_df = st.session_state['sen_def']
passages = preprocess_plain_text(st.session_state['earnings_passages'],window_size=window_size)
##### Sematic Search #####
# Encode the query using the bi-encoder and find potentially relevant passages
corpus_embeddings = sbert.encode(passages, convert_to_tensor=True, show_progress_bar=True)
question_embedding = sbert.encode(search_input, convert_to_tensor=True)
question_embedding = question_embedding.cpu()
hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k,score_function=util.dot_score)
hits = hits[0] # Get the hits for the first query
##### Re-Ranking #####
# Now, score all retrieved passages with the cross_encoder
cross_inp = [[query, passages[hit['corpus_id']]] for hit in hits]
cross_scores = cross_encoder.predict(cross_inp)
# Sort results by the cross-encoder scores
for idx in range(len(cross_scores)):
hits[idx]['cross-score'] = cross_scores[idx]
# Output of top-3 hits from re-ranker
hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
score='cross-score'
df = pd.DataFrame([(hit[score],passages[hit['corpus_id']]) for hit in hits[0:2]],columns=['Score','Text'])
df['Score'] = round(df['Score'],2)
def gen_annotated_text(para):
tag_list = []
for i in sent_tokenize(para):
label = sen_df.loc[sen_df['text']==i, 'label'].values[0]
if label == 'Negative':
tag_list.append((i,label,'#faa'))
elif label == 'Positive':
tag_list.append((i,label,'#afa'))
else:
tag_list.append((i,label,'#fea'))
return tag_list
text_to_annotate = [gen_annotated_text(para) for para in df.Text.tolist()]
for i in text_to_annotate:
annotated_text(i)
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