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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")
def gen_sentiment(text):
'''Generate sentiment of given text'''
return sent_pipe(text)[0]['label']
bi_enc_options = ["multi-qa-mpnet-base-dot-v1","all-mpnet-base-v2","multi-qa-MiniLM-L6-cos-v1","neeva/query2query"]
search_input = st.text_input(
label='Enter Your Search Query',value= "What key challenges did the business face?", key='search')
sbert_model_name = st.sidebar.selectbox("Encoder Model", options=bi_enc_options, key='sbox')
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=7,value=3)
if search_input and sbert_model_name:
if "sen_df" in st.session_state and "earnings_passages" in st.session_state:
## Save to a dataframe for ease of visualization
sen_df = st.session_state['sen_df']
passages = preprocess_plain_text(st.session_state['earnings_passages'],window_size=window_size)
with st.spinner(
text=f"Loading {sbert_model_name} encoder and embedding text into vector space. This might take a few seconds depending on the length of text..."
):
sbert = load_sbert(sbert_model_name)
##### 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 = [[search_input, 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:int(top_k)]],columns=['Score','Text'])
df['Score'] = round(df['Score'],2)
df['Sentiment'] = df.Text.apply(gen_sentiment)
def gen_annotated_text(df):
'''Generate annotated text'''
tag_list=[]
for row in df.itertuples():
label = row[3]
text = row[2]
if label == 'Positive':
tag_list.append((text,label,'#8fce00'))
elif label == 'Negative':
tag_list.append((text,label,'#f44336'))
else:
tag_list.append((text,label,'#000000'))
return tag_list
text_annotations = gen_annotated_text(df)
first, second = text_annotations[0], text_annotations[1]
with st.expander(label='Best Search Query Result', expanded=True):
annotated_text(first)
with st.expander(label='Alternative Search Query Result'):
annotated_text(second)
else:
st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file')
else:
st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file')
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