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import re | |
import openai | |
import streamlit_scrollable_textbox as stx | |
import pinecone | |
import streamlit as st | |
st.set_page_config(layout="wide") # isort: split | |
from utils.entity_extraction import ( | |
clean_entities, | |
extract_quarter_year, | |
extract_ticker_spacy, | |
format_entities_flan_alpaca, | |
generate_alpaca_ner_prompt, | |
extract_keywords | |
) | |
from utils.models import ( | |
generate_entities_flan_alpaca_checkpoint, | |
generate_entities_flan_alpaca_inference_api, | |
generate_text_flan_t5, | |
get_data, | |
get_alpaca_model, | |
get_flan_alpaca_xl_model, | |
get_flan_t5_model, | |
get_instructor_embedding_model, | |
get_mpnet_embedding_model, | |
get_sgpt_embedding_model, | |
get_spacy_model, | |
get_splade_sparse_embedding_model, | |
get_t5_model, | |
gpt_turbo_model, | |
save_key, | |
) | |
from utils.prompts import ( | |
generate_flant5_prompt_instruct_chunk_context, | |
generate_flant5_prompt_instruct_chunk_context_single, | |
generate_flant5_prompt_instruct_complete_context, | |
generate_flant5_prompt_summ_chunk_context, | |
generate_flant5_prompt_summ_chunk_context_single, | |
generate_gpt_j_two_shot_prompt_1, | |
generate_gpt_j_two_shot_prompt_2, | |
generate_gpt_prompt_alpaca, | |
generate_gpt_prompt_alpaca_multi_doc, | |
generate_gpt_prompt_alpaca_multi_doc_multi_company, | |
generate_gpt_prompt_original, | |
generate_multi_doc_context, | |
get_context_list_prompt, | |
) | |
from utils.retriever import ( | |
format_query, | |
query_pinecone, | |
query_pinecone_sparse, | |
sentence_id_combine, | |
text_lookup, | |
year_quarter_range, | |
) | |
from utils.transcript_retrieval import retrieve_transcript | |
from utils.vector_index import ( | |
create_dense_embeddings, | |
create_sparse_embeddings, | |
hybrid_score_norm, | |
) | |
from utils import nltkmodules | |
st.title("Question Answering on Earnings Call Transcripts") | |
st.write( | |
"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." | |
) | |
col1, col2 = st.columns([3, 3], gap="medium") | |
with st.sidebar: | |
ner_choice = st.selectbox("Select NER Model", ["Spacy", "Alpaca"]) | |
document_type = st.selectbox( | |
"Select Query Type", ["Single-Document", "Multi-Document"] | |
) | |
if document_type == "Multi-Document": | |
multi_company_choice = st.selectbox( | |
"Select Company Query Type", | |
["Single-Company", "Compare Companies"], | |
) | |
if ner_choice == "Spacy": | |
ner_model = get_spacy_model() | |
alpaca_model = get_alpaca_model() | |
with col1: | |
st.subheader("Question") | |
if document_type == "Single-Document": | |
query_text = st.text_area( | |
"Input Query", | |
value="What was discussed regarding Wearables revenue performance?", | |
) | |
else: | |
if multi_company_choice == "Single-Company": | |
query_text = st.text_area( | |
"Input Query", | |
value="What was the reported revenue for Wearables over the last 2 years?", | |
) | |
else: | |
query_text = st.text_area( | |
"Input Query", | |
value="How was AAPL's capex spend compared to GOOGL?", | |
) | |
# Extract keywords from query | |
keywords = extract_keywords(query_text, alpaca_model) | |
years_choice = ["2020", "2019", "2018", "2017", "2016", "All"] | |
quarters_choice = ["Q1", "Q2", "Q3", "Q4", "All"] | |
ticker_choice = [ | |
"AAPL", | |
"CSCO", | |
"MSFT", | |
"ASML", | |
"NVDA", | |
"GOOGL", | |
"MU", | |
"INTC", | |
"AMZN", | |
"AMD", | |
] | |
if document_type == "Single-Document": | |
if ner_choice == "Alpaca": | |
ner_prompt = generate_alpaca_ner_prompt(query_text) | |
entity_text = generate_entities_flan_alpaca_inference_api(ner_prompt) | |
company_ent, quarter_ent, year_ent = format_entities_flan_alpaca( | |
entity_text | |
) | |
else: | |
company_ent = extract_ticker_spacy(query_text, ner_model) | |
quarter_ent, year_ent = extract_quarter_year(query_text) | |
ticker_index, quarter_index, year_index = clean_entities( | |
company_ent, quarter_ent, year_ent | |
) | |
with col1: | |
# Hardcoding the defaults for a question without metadata | |
if ( | |
query_text | |
== "What was discussed regarding Wearables revenue performance?" | |
): | |
year = st.selectbox("Year", years_choice) | |
quarter = st.selectbox("Quarter", quarters_choice) | |
ticker = st.selectbox("Company", ticker_choice) | |
else: | |
year = st.selectbox("Year", years_choice, index=year_index) | |
quarter = st.selectbox( | |
"Quarter", quarters_choice, index=quarter_index | |
) | |
ticker = st.selectbox("Company", ticker_choice, ticker_index) | |
participant_type = st.selectbox( | |
"Speaker", ["Company Speaker", "Analyst"] | |
) | |
else: | |
# Multi-Document Case | |
with col1: | |
# Single Company Summary | |
if multi_company_choice == "Single-Company": | |
# Hardcoding the defaults for a question without metadata | |
if ( | |
query_text | |
== "What was the reported revenue for Wearables over the last 2 years?" | |
): | |
start_year = st.selectbox("Start Year", years_choice, index=2) | |
start_quarter = st.selectbox( | |
"Start Quarter", quarters_choice, index=0 | |
) | |
end_year = st.selectbox("End Year", years_choice, index=0) | |
end_quarter = st.selectbox( | |
"End Quarter", quarters_choice, index=0 | |
) | |
ticker = st.selectbox("Company", ticker_choice, index=0) | |
else: | |
start_year = st.selectbox("Start Year", years_choice, index=2) | |
start_quarter = st.selectbox( | |
"Start Quarter", quarters_choice, index=0 | |
) | |
end_year = st.selectbox("End Year", years_choice, index=0) | |
end_quarter = st.selectbox( | |
"End Quarter", quarters_choice, index=0 | |
) | |
ticker = st.selectbox("Company", ticker_choice, index=0) | |
# Single Company Summary | |
if multi_company_choice == "Compare Companies": | |
# Hardcoding the defaults for a question without metadata | |
if query_text == "How was AAPL's capex spend compared to GOOGL?": | |
start_year = st.selectbox("Start Year", years_choice, index=1) | |
start_quarter = st.selectbox( | |
"Start Quarter", quarters_choice, index=0 | |
) | |
end_year = st.selectbox("End Year", years_choice, index=0) | |
end_quarter = st.selectbox( | |
"End Quarter", quarters_choice, index=0 | |
) | |
ticker_first = st.selectbox( | |
"First Company", ticker_choice, index=0 | |
) | |
ticker_second = st.selectbox( | |
"Second Company", ticker_choice, index=5 | |
) | |
else: | |
start_year = st.selectbox("Start Year", years_choice, index=2) | |
start_quarter = st.selectbox( | |
"Start Quarter", quarters_choice, index=0 | |
) | |
end_year = st.selectbox("End Year", years_choice, index=0) | |
end_quarter = st.selectbox( | |
"End Quarter", quarters_choice, index=0 | |
) | |
ticker_first = st.selectbox( | |
"First Company", ticker_choice, index=0 | |
) | |
ticker_second = st.selectbox( | |
"Second Company", ticker_choice, index=1 | |
) | |
participant_type = st.selectbox( | |
"Speaker", ["Company Speaker", "Analyst"] | |
) | |
with st.sidebar: | |
st.subheader("Select Options:") | |
if document_type == "Single-Document": | |
num_results = int( | |
st.number_input("Number of Results to query", 1, 15, value=5) | |
) | |
else: | |
num_results = int( | |
st.number_input("Number of Results to query", 1, 15, value=4) | |
) | |
# Choose encoder model | |
encoder_models_choice = [ | |
"MPNET", | |
"Instructor", | |
"Hybrid Instructor - SPLADE", | |
"SGPT", | |
"Hybrid MPNET - SPLADE", | |
] | |
with st.sidebar: | |
encoder_model = st.selectbox("Select Encoder Model", encoder_models_choice) | |
# Choose decoder model | |
# Restricting multi-document to only GPT-3 | |
if document_type == "Single-Document": | |
decoder_models_choice = ["GPT-3.5 Turbo", "T5", "FLAN-T5", "GPT-J"] | |
else: | |
decoder_models_choice = ["GPT-3.5 Turbo"] | |
with st.sidebar: | |
decoder_model = st.selectbox("Select Decoder Model", decoder_models_choice) | |
if encoder_model == "MPNET": | |
# Connect to pinecone environment | |
pinecone.init( | |
api_key=st.secrets["pinecone_mpnet"], environment="us-east1-gcp" | |
) | |
pinecone_index_name = "week2-all-mpnet-base" | |
pinecone_index = pinecone.Index(pinecone_index_name) | |
retriever_model = get_mpnet_embedding_model() | |
elif encoder_model == "SGPT": | |
# Connect to pinecone environment | |
pinecone.init( | |
api_key=st.secrets["pinecone_sgpt"], environment="us-east1-gcp" | |
) | |
pinecone_index_name = "week2-sgpt-125m" | |
pinecone_index = pinecone.Index(pinecone_index_name) | |
retriever_model = get_sgpt_embedding_model() | |
elif encoder_model == "Instructor": | |
# Connect to pinecone environment | |
pinecone.init( | |
api_key=st.secrets["pinecone_instructor"], | |
environment="us-west4-gcp-free", | |
) | |
pinecone_index_name = "week13-instructor-xl" | |
pinecone_index = pinecone.Index(pinecone_index_name) | |
retriever_model = get_instructor_embedding_model() | |
instruction = ( | |
"Represent the financial question for retrieving supporting documents:" | |
) | |
elif encoder_model == "Hybrid Instructor - SPLADE": | |
# Connect to pinecone environment | |
pinecone.init( | |
api_key=st.secrets["pinecone_instructor_splade"], | |
environment="us-west4-gcp-free", | |
) | |
pinecone_index_name = "week13-splade-instructor-xl" | |
pinecone_index = pinecone.Index(pinecone_index_name) | |
retriever_model = get_instructor_embedding_model() | |
( | |
sparse_retriever_model, | |
sparse_retriever_tokenizer, | |
) = get_splade_sparse_embedding_model() | |
instruction = ( | |
"Represent the financial question for retrieving supporting documents:" | |
) | |
elif encoder_model == "Hybrid MPNET - SPLADE": | |
pinecone.init( | |
api_key=st.secrets["pinecone_hybrid_splade_mpnet"], | |
environment="us-central1-gcp", | |
) | |
pinecone_index_name = "splade-mpnet" | |
pinecone_index = pinecone.Index(pinecone_index_name) | |
retriever_model = get_mpnet_embedding_model() | |
( | |
sparse_retriever_model, | |
sparse_retriever_tokenizer, | |
) = get_splade_sparse_embedding_model() | |
with st.sidebar: | |
if document_type == "Single-Document": | |
window = int(st.number_input("Sentence Window Size", 0, 10, value=1)) | |
threshold = float( | |
st.number_input( | |
label="Similarity Score Threshold", | |
step=0.05, | |
format="%.2f", | |
value=0.25, | |
) | |
) | |
else: | |
window = int(st.number_input("Sentence Window Size", 0, 10, value=1)) | |
threshold = float( | |
st.number_input( | |
label="Similarity Score Threshold", | |
step=0.05, | |
format="%.2f", | |
value=0.6, | |
) | |
) | |
data = get_data() | |
if document_type == "Single-Document": | |
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 | |
) | |
query_results = query_pinecone_sparse( | |
dense_query_embedding, | |
sparse_query_embedding, | |
num_results, | |
pinecone_index, | |
year, | |
quarter, | |
ticker, | |
participant_type, | |
keywords, | |
threshold, | |
) | |
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 | |
) | |
query_results = query_pinecone( | |
dense_query_embedding, | |
num_results, | |
pinecone_index, | |
year, | |
quarter, | |
ticker, | |
participant_type, | |
keywords, | |
threshold, | |
) | |
if threshold <= 0.90: | |
context_list = sentence_id_combine(data, query_results, lag=window) | |
else: | |
context_list = format_query(query_results) | |
else: | |
# Multi-Document Retreival | |
# Single Company | |
if multi_company_choice == "Single-Company": | |
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 = [] | |
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, | |
participant_type, | |
keywords, | |
threshold, | |
) | |
results_list = sentence_id_combine( | |
data, query_results, lag=window | |
) | |
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, | |
threshold, | |
) | |
results_list = sentence_id_combine( | |
data, query_results, lag=window | |
) | |
context_group.append((results_list, year, quarter, ticker)) | |
multi_doc_context = generate_multi_doc_context(context_group) | |
# Companies Comparison | |
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 | |
) | |
# First Company Context | |
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, | |
threshold, | |
) | |
results_list = sentence_id_combine( | |
data, query_results, lag=window | |
) | |
context_group_first.append( | |
(results_list, year, quarter, ticker_first) | |
) | |
# Second Company Context | |
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, | |
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 | |
) | |
# First Company Context | |
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, | |
threshold, | |
) | |
results_list = sentence_id_combine( | |
data, query_results, lag=window | |
) | |
context_group_first.append( | |
(results_list, year, quarter, ticker_first) | |
) | |
# Second Company Context | |
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, | |
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(["Retrived 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() | |
] | |
# Add "Document: " back to the beginning of each section | |
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", | |
) | |