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import streamlit as st |
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from functions import * |
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from langchain.chains import QAGenerationChain |
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import itertools |
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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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st.markdown( |
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""" |
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<style> |
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#MainMenu {visibility: hidden; |
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# } |
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footer {visibility: hidden; |
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} |
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.css-card { |
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border-radius: 0px; |
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padding: 30px 10px 10px 10px; |
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background-color: black; |
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box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); |
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margin-bottom: 10px; |
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font-family: "IBM Plex Sans", sans-serif; |
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} |
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.card-tag { |
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border-radius: 0px; |
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padding: 1px 5px 1px 5px; |
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margin-bottom: 10px; |
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position: absolute; |
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left: 0px; |
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top: 0px; |
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font-size: 0.6rem; |
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font-family: "IBM Plex Sans", sans-serif; |
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color: white; |
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background-color: green; |
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} |
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.css-zt5igj {left:0; |
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} |
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span.css-10trblm {margin-left:0; |
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} |
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div.css-1kyxreq {margin-top: -40px; |
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} |
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</style> |
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""", |
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unsafe_allow_html=True, |
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) |
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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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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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st.sidebar.markdown('Earnings QnA Generator') |
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chunk_size = 1000 |
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overlap_size = 50 |
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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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earnings_text = st.session_state['earnings_passages'] |
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print(f'earnings_to_be_embedded:{earnings_text}') |
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st.session_state.eval_set = generate_eval( |
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earnings_text, 10, 3000) |
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for i, qa_pair in enumerate(st.session_state.eval_set): |
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st.sidebar.markdown( |
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f""" |
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<div class="css-card"> |
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<span class="card-tag">Question {i + 1}</span> |
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<p style="font-size: 12px;">{qa_pair['question']}</p> |
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<p style="font-size: 12px;">{qa_pair['answer']}</p> |
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</div> |
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""", |
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unsafe_allow_html=True, |
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) |
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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(earnings_text,title, embedding_model) |
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result = embed_text(search_input,docsearch) |
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references = [doc.page_content for doc in result['source_documents']] |
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answer = result['answer'] |
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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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