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import re |
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import numpy as np |
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import openai |
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import streamlit_scrollable_textbox as stx |
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import pinecone |
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import streamlit as st |
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st.set_page_config(layout="wide") |
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from utils import nltkmodules |
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from utils.entity_extraction import ( |
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clean_entities, |
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extract_keywords, |
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extract_quarter_year, |
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extract_ticker_spacy, |
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format_entities_flan_alpaca, |
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generate_alpaca_ner_prompt, |
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) |
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from utils.models import ( |
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generate_entities_flan_alpaca_checkpoint, |
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generate_entities_flan_alpaca_inference_api, |
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generate_text_flan_t5, |
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get_alpaca_model, |
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get_data, |
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get_flan_alpaca_xl_model, |
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get_flan_t5_model, |
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get_instructor_embedding_model, |
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get_instructor_embedding_model_api, |
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get_bm25_model, |
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preprocess_text, |
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get_mpnet_embedding_model, |
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get_sgpt_embedding_model, |
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get_spacy_model, |
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get_splade_sparse_embedding_model, |
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get_t5_model, |
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gpt_turbo_model, |
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save_key, |
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) |
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from utils.prompts import ( |
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generate_flant5_prompt_instruct_chunk_context, |
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generate_flant5_prompt_instruct_chunk_context_single, |
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generate_flant5_prompt_instruct_complete_context, |
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generate_flant5_prompt_summ_chunk_context, |
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generate_flant5_prompt_summ_chunk_context_single, |
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generate_gpt_j_two_shot_prompt_1, |
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generate_gpt_j_two_shot_prompt_2, |
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generate_gpt_prompt_alpaca, |
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generate_gpt_prompt_alpaca_multi_doc, |
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generate_gpt_prompt_alpaca_multi_doc_multi_company, |
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generate_gpt_prompt_original, |
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generate_multi_doc_context, |
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get_context_list_prompt, |
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) |
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from utils.retriever import ( |
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format_query, |
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query_pinecone, |
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query_pinecone_sparse, |
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sentence_id_combine, |
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text_lookup, |
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year_quarter_range, |
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get_bm25_search_hits, |
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) |
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from utils.transcript_retrieval import retrieve_transcript |
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from utils.vector_index import ( |
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create_dense_embeddings, |
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create_sparse_embeddings, |
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hybrid_score_norm, |
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) |
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st.title("Question Answering on Earnings Call Transcripts") |
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st.write( |
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"The app uses the quarterly earnings call transcripts for 10 companies (Apple, AMD, Amazon, Cisco, Google, Microsoft, Nvidia, ASML, Intel, Micron) for the years 2016 to 2020." |
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) |
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col1, col2 = st.columns([3, 3], gap="medium") |
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with st.sidebar: |
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use_bm25 = st.checkbox("Use BM25 for filtering results") |
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ner_choice = st.selectbox("Select NER Model", ["Spacy", "Alpaca"]) |
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document_type = st.selectbox( |
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"Select Query Type", ["Single-Document", "Multi-Document"] |
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) |
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if document_type == "Multi-Document": |
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multi_company_choice = st.selectbox( |
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"Select Company Query Type", |
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["Single-Company", "Compare Companies"], |
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) |
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data = get_data() |
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corpus, bm25 = get_bm25_model(data) |
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if ner_choice == "Spacy": |
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ner_model = get_spacy_model() |
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alpaca_model = get_alpaca_model() |
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with col1: |
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st.subheader("Question") |
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if document_type == "Single-Document": |
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query_text = st.text_area( |
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"Input Query", |
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value="What was discussed regarding Wearables revenue performance?", |
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) |
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else: |
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if multi_company_choice == "Single-Company": |
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query_text = st.text_area( |
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"Input Query", |
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value="What was the reported revenue for Wearables over the last 2 years?", |
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) |
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else: |
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query_text = st.text_area( |
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"Input Query", |
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value="How was AAPL's capex spend compared to GOOGL?", |
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) |
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tokenized_query = preprocess_text(query_text).split() |
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sparse_scores = np.argsort(bm25.get_scores(tokenized_query), axis=0)[::-1] |
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indices_hits = get_bm25_search_hits(corpus, sparse_scores, 50) |
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if use_bm25 == True: |
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indices = indices_hits |
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else: |
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indices = None |
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keywords = extract_keywords(query_text, alpaca_model) |
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years_choice = ["2020", "2019", "2018", "2017", "2016", "All"] |
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quarters_choice = ["Q1", "Q2", "Q3", "Q4", "All"] |
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ticker_choice = [ |
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"AAPL", |
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"CSCO", |
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"MSFT", |
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"ASML", |
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"NVDA", |
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"GOOGL", |
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"MU", |
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"INTC", |
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"AMZN", |
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"AMD", |
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] |
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if document_type == "Single-Document": |
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if ner_choice == "Alpaca": |
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ner_prompt = generate_alpaca_ner_prompt(query_text) |
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entity_text = generate_entities_flan_alpaca_inference_api(ner_prompt) |
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company_ent, quarter_ent, year_ent = format_entities_flan_alpaca( |
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entity_text |
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) |
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else: |
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company_ent = extract_ticker_spacy(query_text, ner_model) |
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quarter_ent, year_ent = extract_quarter_year(query_text) |
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ticker_index, quarter_index, year_index = clean_entities( |
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company_ent, quarter_ent, year_ent |
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) |
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with col1: |
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if ( |
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query_text |
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== "What was discussed regarding Wearables revenue performance?" |
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): |
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year = st.selectbox("Year", years_choice) |
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quarter = st.selectbox("Quarter", quarters_choice) |
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ticker = st.selectbox("Company", ticker_choice) |
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else: |
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year = st.selectbox("Year", years_choice, index=year_index) |
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quarter = st.selectbox( |
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"Quarter", quarters_choice, index=quarter_index |
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) |
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ticker = st.selectbox("Company", ticker_choice, ticker_index) |
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participant_type = st.selectbox( |
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"Speaker", ["Company Speaker", "Analyst"] |
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) |
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else: |
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with col1: |
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if multi_company_choice == "Single-Company": |
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if ( |
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query_text |
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== "What was the reported revenue for Wearables over the last 2 years?" |
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): |
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start_year = st.selectbox("Start Year", years_choice, index=2) |
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start_quarter = st.selectbox( |
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"Start Quarter", quarters_choice, index=0 |
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) |
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end_year = st.selectbox("End Year", years_choice, index=0) |
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end_quarter = st.selectbox( |
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"End Quarter", quarters_choice, index=0 |
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) |
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ticker = st.selectbox("Company", ticker_choice, index=0) |
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else: |
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start_year = st.selectbox("Start Year", years_choice, index=2) |
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start_quarter = st.selectbox( |
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"Start Quarter", quarters_choice, index=0 |
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) |
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end_year = st.selectbox("End Year", years_choice, index=0) |
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end_quarter = st.selectbox( |
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"End Quarter", quarters_choice, index=0 |
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) |
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ticker = st.selectbox("Company", ticker_choice, index=0) |
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if multi_company_choice == "Compare Companies": |
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if query_text == "How was AAPL's capex spend compared to GOOGL?": |
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start_year = st.selectbox("Start Year", years_choice, index=1) |
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start_quarter = st.selectbox( |
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"Start Quarter", quarters_choice, index=0 |
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) |
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end_year = st.selectbox("End Year", years_choice, index=0) |
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end_quarter = st.selectbox( |
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"End Quarter", quarters_choice, index=0 |
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) |
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ticker_first = st.selectbox( |
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"First Company", ticker_choice, index=0 |
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) |
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ticker_second = st.selectbox( |
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"Second Company", ticker_choice, index=5 |
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) |
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else: |
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start_year = st.selectbox("Start Year", years_choice, index=2) |
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start_quarter = st.selectbox( |
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"Start Quarter", quarters_choice, index=0 |
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) |
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end_year = st.selectbox("End Year", years_choice, index=0) |
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end_quarter = st.selectbox( |
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"End Quarter", quarters_choice, index=0 |
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) |
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ticker_first = st.selectbox( |
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"First Company", ticker_choice, index=0 |
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) |
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ticker_second = st.selectbox( |
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"Second Company", ticker_choice, index=1 |
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) |
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participant_type = st.selectbox( |
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"Speaker", ["Company Speaker", "Analyst"] |
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) |
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with st.sidebar: |
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st.subheader("Select Options:") |
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if document_type == "Single-Document": |
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num_results = int( |
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st.number_input("Number of Results to query", 1, 15, value=5) |
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) |
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else: |
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num_results = int( |
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st.number_input("Number of Results to query", 1, 15, value=4) |
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) |
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encoder_models_choice = [ |
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"MPNET", |
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"Instructor", |
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"Hybrid Instructor - SPLADE", |
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"SGPT", |
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"Hybrid MPNET - SPLADE", |
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] |
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with st.sidebar: |
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encoder_model = st.selectbox("Select Encoder Model", encoder_models_choice) |
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if document_type == "Single-Document": |
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decoder_models_choice = ["GPT-3.5 Turbo", "T5", "FLAN-T5", "GPT-J"] |
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else: |
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decoder_models_choice = ["GPT-3.5 Turbo"] |
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with st.sidebar: |
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decoder_model = st.selectbox("Select Decoder Model", decoder_models_choice) |
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if encoder_model == "MPNET": |
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pinecone.init( |
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api_key=st.secrets["pinecone_mpnet"], environment="us-east1-gcp" |
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) |
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pinecone_index_name = "week2-all-mpnet-base" |
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pinecone_index = pinecone.Index(pinecone_index_name) |
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retriever_model = get_mpnet_embedding_model() |
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elif encoder_model == "SGPT": |
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pinecone.init( |
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api_key=st.secrets["pinecone_sgpt"], environment="us-east1-gcp" |
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) |
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pinecone_index_name = "week2-sgpt-125m" |
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pinecone_index = pinecone.Index(pinecone_index_name) |
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retriever_model = get_sgpt_embedding_model() |
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elif encoder_model == "Instructor": |
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pinecone.init( |
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api_key=st.secrets["pinecone_instructor"], |
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environment="us-west4-gcp-free", |
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) |
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pinecone_index_name = "week13-instructor-xl" |
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pinecone_index = pinecone.Index(pinecone_index_name) |
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retriever_model = get_instructor_embedding_model_api() |
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instruction = ( |
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"Represent the financial question for retrieving supporting documents:" |
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) |
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elif encoder_model == "Hybrid Instructor - SPLADE": |
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pinecone.init( |
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api_key=st.secrets["pinecone_instructor_splade"], |
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environment="us-west4-gcp-free", |
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) |
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pinecone_index_name = "week13-splade-instructor-xl" |
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pinecone_index = pinecone.Index(pinecone_index_name) |
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retriever_model = get_instructor_embedding_model_api() |
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( |
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sparse_retriever_model, |
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sparse_retriever_tokenizer, |
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) = get_splade_sparse_embedding_model() |
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instruction = ( |
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"Represent the financial question for retrieving supporting documents:" |
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) |
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elif encoder_model == "Hybrid MPNET - SPLADE": |
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pinecone.init( |
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api_key=st.secrets["pinecone_hybrid_splade_mpnet"], |
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environment="us-central1-gcp", |
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) |
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pinecone_index_name = "splade-mpnet" |
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pinecone_index = pinecone.Index(pinecone_index_name) |
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retriever_model = get_mpnet_embedding_model() |
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( |
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sparse_retriever_model, |
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sparse_retriever_tokenizer, |
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) = get_splade_sparse_embedding_model() |
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with st.sidebar: |
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if document_type == "Single-Document": |
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window = int(st.number_input("Sentence Window Size", 0, 10, value=1)) |
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|
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threshold = float( |
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st.number_input( |
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label="Similarity Score Threshold", |
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step=0.05, |
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format="%.2f", |
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value=0.25, |
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) |
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) |
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else: |
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window = int(st.number_input("Sentence Window Size", 0, 10, value=1)) |
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|
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threshold = float( |
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st.number_input( |
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label="Similarity Score Threshold", |
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step=0.05, |
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format="%.2f", |
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value=0.6, |
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) |
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) |
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|
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if document_type == "Single-Document": |
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if encoder_model in ["Hybrid SGPT - SPLADE", "Hybrid Instructor - SPLADE"]: |
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if encoder_model == "Hybrid Instructor - SPLADE": |
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dense_query_embedding = create_dense_embeddings( |
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query_text, retriever_model, instruction |
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) |
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else: |
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dense_query_embedding = create_dense_embeddings( |
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query_text, retriever_model |
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) |
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sparse_query_embedding = create_sparse_embeddings( |
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query_text, sparse_retriever_model, sparse_retriever_tokenizer |
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) |
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dense_query_embedding, sparse_query_embedding = hybrid_score_norm( |
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dense_query_embedding, sparse_query_embedding, 0.3 |
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) |
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query_results = query_pinecone_sparse( |
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dense_query_embedding, |
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sparse_query_embedding, |
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num_results, |
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pinecone_index, |
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year, |
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quarter, |
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ticker, |
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participant_type, |
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keywords, |
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indices, |
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threshold, |
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) |
|
|
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else: |
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if encoder_model == "Instructor": |
|
dense_query_embedding = create_dense_embeddings( |
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query_text, retriever_model, instruction |
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) |
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else: |
|
dense_query_embedding = create_dense_embeddings( |
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query_text, retriever_model |
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) |
|
query_results = query_pinecone( |
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dense_query_embedding, |
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num_results, |
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pinecone_index, |
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year, |
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quarter, |
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ticker, |
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participant_type, |
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keywords, |
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indices, |
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threshold, |
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) |
|
|
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if threshold <= 0.90: |
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context_list = sentence_id_combine(data, query_results, lag=window) |
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else: |
|
context_list = format_query(query_results) |
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|
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else: |
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|
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if multi_company_choice == "Single-Company": |
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if encoder_model in [ |
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"Hybrid SGPT - SPLADE", |
|
"Hybrid Instructor - SPLADE", |
|
]: |
|
if encoder_model == "Hybrid Instructor - SPLADE": |
|
dense_query_embedding = create_dense_embeddings( |
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query_text, retriever_model, instruction |
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) |
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else: |
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dense_query_embedding = create_dense_embeddings( |
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query_text, retriever_model |
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) |
|
sparse_query_embedding = create_sparse_embeddings( |
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query_text, sparse_retriever_model, sparse_retriever_tokenizer |
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) |
|
dense_query_embedding, sparse_query_embedding = hybrid_score_norm( |
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dense_query_embedding, sparse_query_embedding, 0.3 |
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) |
|
year_quarter_list = year_quarter_range( |
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start_quarter, start_year, end_quarter, end_year |
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) |
|
|
|
context_group = [] |
|
for year, quarter in year_quarter_list: |
|
query_results = query_pinecone_sparse( |
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dense_query_embedding, |
|
sparse_query_embedding, |
|
num_results, |
|
pinecone_index, |
|
year, |
|
quarter, |
|
ticker, |
|
participant_type, |
|
keywords, |
|
indices, |
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threshold, |
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) |
|
results_list = sentence_id_combine( |
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data, query_results, lag=window |
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) |
|
context_group.append((results_list, year, quarter, ticker)) |
|
|
|
else: |
|
if encoder_model == "Instructor": |
|
dense_query_embedding = create_dense_embeddings( |
|
query_text, retriever_model, instruction |
|
) |
|
else: |
|
dense_query_embedding = create_dense_embeddings( |
|
query_text, retriever_model |
|
) |
|
year_quarter_list = year_quarter_range( |
|
start_quarter, start_year, end_quarter, end_year |
|
) |
|
|
|
context_group = [] |
|
for year, quarter in year_quarter_list: |
|
query_results = query_pinecone( |
|
dense_query_embedding, |
|
num_results, |
|
pinecone_index, |
|
year, |
|
quarter, |
|
ticker, |
|
participant_type, |
|
keywords, |
|
indices, |
|
threshold, |
|
) |
|
results_list = sentence_id_combine( |
|
data, query_results, lag=window |
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) |
|
context_group.append((results_list, year, quarter, ticker)) |
|
|
|
multi_doc_context = generate_multi_doc_context(context_group) |
|
|
|
else: |
|
if encoder_model in [ |
|
"Hybrid SGPT - SPLADE", |
|
"Hybrid Instructor - SPLADE", |
|
]: |
|
if encoder_model == "Hybrid Instructor - SPLADE": |
|
dense_query_embedding = create_dense_embeddings( |
|
query_text, retriever_model, instruction |
|
) |
|
else: |
|
dense_query_embedding = create_dense_embeddings( |
|
query_text, retriever_model |
|
) |
|
sparse_query_embedding = create_sparse_embeddings( |
|
query_text, sparse_retriever_model, sparse_retriever_tokenizer |
|
) |
|
dense_query_embedding, sparse_query_embedding = hybrid_score_norm( |
|
dense_query_embedding, sparse_query_embedding, 0.3 |
|
) |
|
year_quarter_list = year_quarter_range( |
|
start_quarter, start_year, end_quarter, end_year |
|
) |
|
|
|
|
|
context_group_first = [] |
|
for year, quarter in year_quarter_list: |
|
query_results = query_pinecone_sparse( |
|
dense_query_embedding, |
|
sparse_query_embedding, |
|
num_results, |
|
pinecone_index, |
|
year, |
|
quarter, |
|
ticker_first, |
|
participant_type, |
|
keywords, |
|
indices, |
|
threshold, |
|
) |
|
results_list = sentence_id_combine( |
|
data, query_results, lag=window |
|
) |
|
context_group_first.append( |
|
(results_list, year, quarter, ticker_first) |
|
) |
|
|
|
|
|
context_group_second = [] |
|
for year, quarter in year_quarter_list: |
|
query_results = query_pinecone_sparse( |
|
dense_query_embedding, |
|
sparse_query_embedding, |
|
num_results, |
|
pinecone_index, |
|
year, |
|
quarter, |
|
ticker_second, |
|
participant_type, |
|
keywords, |
|
indices, |
|
threshold, |
|
) |
|
results_list = sentence_id_combine( |
|
data, query_results, lag=window |
|
) |
|
context_group_second.append( |
|
(results_list, year, quarter, ticker_second) |
|
) |
|
|
|
else: |
|
if encoder_model == "Instructor": |
|
dense_query_embedding = create_dense_embeddings( |
|
query_text, retriever_model, instruction |
|
) |
|
else: |
|
dense_query_embedding = create_dense_embeddings( |
|
query_text, retriever_model |
|
) |
|
year_quarter_list = year_quarter_range( |
|
start_quarter, start_year, end_quarter, end_year |
|
) |
|
|
|
|
|
context_group_first = [] |
|
for year, quarter in year_quarter_list: |
|
query_results = query_pinecone( |
|
dense_query_embedding, |
|
num_results, |
|
pinecone_index, |
|
year, |
|
quarter, |
|
ticker_first, |
|
participant_type, |
|
keywords, |
|
indices, |
|
threshold, |
|
) |
|
results_list = sentence_id_combine( |
|
data, query_results, lag=window |
|
) |
|
context_group_first.append( |
|
(results_list, year, quarter, ticker_first) |
|
) |
|
|
|
|
|
context_group_second = [] |
|
for year, quarter in year_quarter_list: |
|
query_results = query_pinecone( |
|
dense_query_embedding, |
|
num_results, |
|
pinecone_index, |
|
year, |
|
quarter, |
|
ticker_second, |
|
participant_type, |
|
keywords, |
|
indices, |
|
threshold, |
|
) |
|
results_list = sentence_id_combine( |
|
data, query_results, lag=window |
|
) |
|
context_group_second.append( |
|
(results_list, year, quarter, ticker_second) |
|
) |
|
|
|
multi_doc_context_first = generate_multi_doc_context( |
|
context_group_first |
|
) |
|
multi_doc_context_second = generate_multi_doc_context( |
|
context_group_second |
|
) |
|
|
|
if decoder_model == "GPT-3.5 Turbo": |
|
if document_type == "Single-Document": |
|
prompt = generate_gpt_prompt_alpaca(query_text, context_list) |
|
else: |
|
if multi_company_choice == "Single-Company": |
|
prompt = generate_gpt_prompt_alpaca_multi_doc( |
|
query_text, context_group |
|
) |
|
else: |
|
prompt = generate_gpt_prompt_alpaca_multi_doc_multi_company( |
|
query_text, context_group_first, context_group_second |
|
) |
|
|
|
with col2: |
|
with st.form("my_form"): |
|
edited_prompt = st.text_area( |
|
label="Model Prompt", value=prompt, height=400 |
|
) |
|
|
|
openai_key = st.text_input( |
|
"Enter OpenAI key", |
|
value="", |
|
type="password", |
|
) |
|
submitted = st.form_submit_button("Submit") |
|
if submitted: |
|
api_key = save_key(openai_key) |
|
openai.api_key = api_key |
|
generated_text = gpt_turbo_model(edited_prompt) |
|
st.subheader("Answer:") |
|
regex_pattern_sentences = ( |
|
"(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s" |
|
) |
|
generated_text_list = re.split( |
|
regex_pattern_sentences, generated_text |
|
) |
|
for answer_text in generated_text_list: |
|
answer_text = f"""{answer_text}""" |
|
st.write( |
|
f"<ul><li><p>{answer_text}</p></li></ul>", |
|
unsafe_allow_html=True, |
|
) |
|
|
|
|
|
elif decoder_model == "T5": |
|
prompt = generate_flant5_prompt_instruct_complete_context( |
|
query_text, context_list |
|
) |
|
t5_pipeline = get_t5_model() |
|
output_text = [] |
|
with col2: |
|
with st.form("my_form"): |
|
edited_prompt = st.text_area( |
|
label="Model Prompt", value=prompt, height=400 |
|
) |
|
context_list = get_context_list_prompt(edited_prompt) |
|
submitted = st.form_submit_button("Submit") |
|
if submitted: |
|
for context_text in context_list: |
|
output_text.append( |
|
t5_pipeline(context_text)[0]["summary_text"] |
|
) |
|
st.subheader("Answer:") |
|
for text in output_text: |
|
st.markdown(f"- {text}") |
|
|
|
elif decoder_model == "FLAN-T5": |
|
flan_t5_model, flan_t5_tokenizer = get_flan_t5_model() |
|
output_text = [] |
|
with col2: |
|
prompt_type = st.selectbox( |
|
"Select prompt type", |
|
["Complete Text QA", "Chunkwise QA", "Chunkwise Summarize"], |
|
) |
|
if prompt_type == "Complete Text QA": |
|
prompt = generate_flant5_prompt_instruct_complete_context( |
|
query_text, context_list |
|
) |
|
elif prompt_type == "Chunkwise QA": |
|
st.write("The following prompt is not editable.") |
|
prompt = generate_flant5_prompt_instruct_chunk_context( |
|
query_text, context_list |
|
) |
|
elif prompt_type == "Chunkwise Summarize": |
|
st.write("The following prompt is not editable.") |
|
prompt = generate_flant5_prompt_summ_chunk_context( |
|
query_text, context_list |
|
) |
|
else: |
|
prompt = "" |
|
with st.form("my_form"): |
|
edited_prompt = st.text_area( |
|
label="Model Prompt", value=prompt, height=400 |
|
) |
|
submitted = st.form_submit_button("Submit") |
|
if submitted: |
|
if prompt_type == "Complete Text QA": |
|
output_text_string = generate_text_flan_t5( |
|
flan_t5_model, flan_t5_tokenizer, prompt |
|
) |
|
st.subheader("Answer:") |
|
st.write(output_text_string) |
|
elif prompt_type == "Chunkwise QA": |
|
for context_text in context_list: |
|
model_input = generate_flant5_prompt_instruct_chunk_context_single( |
|
query_text, context_text |
|
) |
|
output_text.append( |
|
generate_text_flan_t5( |
|
flan_t5_model, flan_t5_tokenizer, model_input |
|
) |
|
) |
|
st.subheader("Answer:") |
|
for text in output_text: |
|
if "(iii)" not in text: |
|
st.markdown(f"- {text}") |
|
elif prompt_type == "Chunkwise Summarize": |
|
for context_text in context_list: |
|
model_input = ( |
|
generate_flant5_prompt_summ_chunk_context_single( |
|
query_text, context_text |
|
) |
|
) |
|
output_text.append( |
|
generate_text_flan_t5( |
|
flan_t5_model, flan_t5_tokenizer, model_input |
|
) |
|
) |
|
st.subheader("Answer:") |
|
for text in output_text: |
|
if "(iii)" not in text: |
|
st.markdown(f"- {text}") |
|
|
|
if decoder_model == "GPT-J": |
|
if ticker in ["AAPL", "AMD"]: |
|
prompt = generate_gpt_j_two_shot_prompt_1(query_text, context_list) |
|
elif ticker in ["NVDA", "INTC", "AMZN"]: |
|
prompt = generate_gpt_j_two_shot_prompt_2(query_text, context_list) |
|
else: |
|
prompt = generate_gpt_j_two_shot_prompt_1(query_text, context_list) |
|
with col2: |
|
with st.form("my_form"): |
|
edited_prompt = st.text_area( |
|
label="Model Prompt", value=prompt, height=400 |
|
) |
|
st.write( |
|
"The app currently just shows the prompt. The app does not load the model due to memory limitations." |
|
) |
|
submitted = st.form_submit_button("Submit") |
|
|
|
tab1, tab2 = st.tabs(["Retrieved Text", "Retrieved Documents"]) |
|
|
|
|
|
with tab1: |
|
if document_type == "Single-Document": |
|
with st.expander("See Retrieved Text"): |
|
st.subheader("Retrieved Text:") |
|
for context_text in context_list: |
|
context_text = f"""{context_text}""" |
|
st.write( |
|
f"<ul><li><p>{context_text}</p></li></ul>", |
|
unsafe_allow_html=True, |
|
) |
|
else: |
|
with st.expander("See Retrieved Text"): |
|
st.subheader("Retrieved Text:") |
|
if multi_company_choice == "Compare Companies": |
|
multi_doc_context = ( |
|
multi_doc_context_first + multi_doc_context_second |
|
) |
|
|
|
sections = [ |
|
s.strip() |
|
for s in multi_doc_context.split("Document: ") |
|
if s.strip() |
|
] |
|
|
|
|
|
context_list = [ |
|
"Document: " + s[0:7] + "\n" + s[7:] for s in sections |
|
] |
|
for context_text in context_list: |
|
context_text = f"""{context_text}""" |
|
st.write( |
|
f"<ul><li><p>{context_text}</p></li></ul>", |
|
unsafe_allow_html=True, |
|
) |
|
|
|
|
|
with tab2: |
|
if document_type == "Single-Document": |
|
file_text = retrieve_transcript(data, year, quarter, ticker) |
|
with st.expander("See Transcript"): |
|
st.subheader("Earnings Call Transcript:") |
|
stx.scrollableTextbox( |
|
file_text, height=700, border=False, fontFamily="Helvetica" |
|
) |
|
else: |
|
if multi_company_choice == "Single-Company": |
|
for year, quarter in year_quarter_list: |
|
file_text = retrieve_transcript(data, year, quarter, ticker) |
|
with st.expander(f"See Transcript - {quarter} {year}"): |
|
st.subheader( |
|
"Earnings Call Transcript - {quarter} {year}:" |
|
) |
|
stx.scrollableTextbox( |
|
file_text, |
|
height=700, |
|
border=False, |
|
fontFamily="Helvetica", |
|
) |
|
else: |
|
for year, quarter in year_quarter_list: |
|
file_text = retrieve_transcript( |
|
data, year, quarter, ticker_first |
|
) |
|
with st.expander(f"See Transcript - {quarter} {year}"): |
|
st.subheader( |
|
"Earnings Call Transcript - {quarter} {year}:" |
|
) |
|
stx.scrollableTextbox( |
|
file_text, |
|
height=700, |
|
border=False, |
|
fontFamily="Helvetica", |
|
) |
|
for year, quarter in year_quarter_list: |
|
file_text = retrieve_transcript( |
|
data, year, quarter, ticker_second |
|
) |
|
with st.expander(f"See Transcript - {quarter} {year}"): |
|
st.subheader( |
|
"Earnings Call Transcript - {quarter} {year}:" |
|
) |
|
stx.scrollableTextbox( |
|
file_text, |
|
height=700, |
|
border=False, |
|
fontFamily="Helvetica", |
|
) |
|
|
