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'] 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 bi_enc_dict = {'mpnet-base-v2':"all-mpnet-base-v2", 'instructor-base': 'hkunlp/instructor-base', 'setfit-finance': 'nickmuchi/setfit-finetuned-financial-text-classification'} 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("Embedding Model", options=list(bi_enc_dict.keys()), key='sbox') chunk_size = st.sidebar.slider("Number of Words per Chunk of Text",min_value=100,max_value=250,value=200) overlap_size = st.sidebar.slider("Number of Overlap Words in Search Response",min_value=30,max_value=100,value=50) chain_type = st.sidebar.radio("Langchain Chain Type",options = ['Normal','Refined']) try: if search_input: 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'] title = st.session_state['title'] with st.spinner( text=f"Loading {bi_enc_dict[sbert_model_name]} embedding model and Generating Response..." ): result = embed_text(search_input,st.session_state['earnings_passages'],title, bi_enc_dict[sbert_model_name], emb_tokenizer,chain_type=chain_type) print(result) references = [doc.page_content for doc in result['input_documents']] answer = result['output_text'] sentiment_label = gen_sentiment(answer) ##### Sematic Search ##### df = pd.DataFrame([(num,res,lab) for num, res, lab in zip(1,answer,sentiment_label)],columns=['Index','Text','Sentiment']) text_annotations = gen_annotated_text(df)[0] with st.expander(label='Query Result', expanded=True): annotated_text(text_annotations) with st.expander(label='References from Corpus used to Generate Result'): for ref in references: st.write(ref) 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') except RuntimeError: st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file')