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import streamlit as st |
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from functions import * |
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st.set_page_config(page_title="Earnings Question/Answering", page_icon="🔎") |
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st.sidebar.header("Semantic Search") |
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st.markdown("## Earnings Semantic Search with LangChain, OpenAI & SBert") |
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def gen_sentiment(text): |
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'''Generate sentiment of given text''' |
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return sent_pipe(text)[0]['label'] |
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def gen_annotated_text(df): |
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'''Generate annotated text''' |
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tag_list=[] |
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for row in df.itertuples(): |
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label = row[2] |
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text = row[1] |
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if label == 'Positive': |
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tag_list.append((text,label,'#8fce00')) |
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elif label == 'Negative': |
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tag_list.append((text,label,'#f44336')) |
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else: |
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tag_list.append((text,label,'#000000')) |
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return tag_list |
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bi_enc_dict = {'mpnet-base-v2':"all-mpnet-base-v2", |
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'instructor-base': 'hkunlp/instructor-base', |
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'setfit-finance': 'nickmuchi/setfit-finetuned-financial-text-classification'} |
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search_input = st.text_input( |
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label='Enter Your Search Query',value= "What key challenges did the business face?", key='search') |
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sbert_model_name = st.sidebar.selectbox("Embedding Model", options=list(bi_enc_dict.keys()), key='sbox') |
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chunk_size = st.sidebar.slider("Number of Words per Chunk of Text",min_value=100,max_value=250,value=200) |
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overlap_size = st.sidebar.slider("Number of Overlap Words in Search Response",min_value=30,max_value=100,value=50) |
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chain_type = st.sidebar.radio("Langchain Chain Type",options = ['Normal','Refined']) |
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try: |
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if search_input: |
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if "sen_df" in st.session_state and "earnings_passages" in st.session_state: |
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sen_df = st.session_state['sen_df'] |
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title = st.session_state['title'] |
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embedding_model = bi_enc_dict[sbert_model_name] |
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with st.spinner( |
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text=f"Loading {embedding_model} embedding model and Generating Response..." |
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): |
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docsearch = process_corpus(st.session_state['earnings_passages'],title, embedding_model) |
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result = embed_text(search_input,title,embedding_model,docsearch,chain_type) |
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references = [doc.page_content for doc in result['input_documents']] |
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answer = result['output_text'] |
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sentiment_label = gen_sentiment(answer) |
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df = pd.DataFrame.from_dict({'Text':[answer],'Sentiment':[sentiment_label]}) |
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text_annotations = gen_annotated_text(df)[0] |
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with st.expander(label='Query Result', expanded=True): |
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annotated_text(text_annotations) |
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with st.expander(label='References from Corpus used to Generate Result'): |
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for ref in references: |
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st.write(ref) |
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else: |
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st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file') |
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else: |
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st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file') |
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except RuntimeError: |
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st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file') |
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