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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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from utils import ( |
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create_dense_embeddings, |
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create_sparse_embeddings, |
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format_query, |
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generate_prompt, |
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get_data, |
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get_flan_t5_model, |
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get_mpnet_embedding_model, |
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get_sgpt_embedding_model, |
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get_splade_sparse_embedding_model, |
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get_t5_model, |
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gpt_model, |
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hybrid_score_norm, |
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query_pinecone, |
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query_pinecone_sparse, |
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retrieve_transcript, |
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save_key, |
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sentence_id_combine, |
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text_lookup, |
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) |
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st.set_page_config(layout="wide") |
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st.title("Abstractive Question Answering") |
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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 col1: |
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st.subheader("Question") |
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query_text = st.text_input( |
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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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with col1: |
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years_choice = ["2020", "2019", "2018", "2017", "2016", "All"] |
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with col1: |
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year = st.selectbox("Year", years_choice) |
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with col1: |
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quarter = st.selectbox("Quarter", ["Q1", "Q2", "Q3", "Q4", "All"]) |
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with col1: |
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participant_type = st.selectbox("Speaker", ["Company Speaker", "Analyst"]) |
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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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with col1: |
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ticker = st.selectbox("Company", ticker_choice) |
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with st.sidebar: |
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st.subheader("Select Options:") |
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with st.sidebar: |
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num_results = int( |
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st.number_input("Number of Results to query", 1, 15, value=6) |
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) |
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encoder_models_choice = ["MPNET", "SGPT", "Hybrid MPNET - SPLADE"] |
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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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decoder_models_choice = [ |
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"GPT3 - (text-davinci-003)", |
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"T5", |
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"FLAN-T5", |
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] |
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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 == "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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window = int(st.number_input("Sentence Window Size", 0, 10, value=1)) |
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with st.sidebar: |
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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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data = get_data() |
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if encoder_model == "Hybrid SGPT - SPLADE": |
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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 |
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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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threshold, |
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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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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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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: |
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context_list = format_query(query_results) |
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prompt = generate_prompt(query_text, context_list) |
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if decoder_model == "GPT3 - (text-davinci-003)": |
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with col2: |
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with st.form("my_form"): |
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edited_prompt = st.text_area( |
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label="Model Prompt", value=prompt, height=270 |
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) |
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openai_key = st.text_input( |
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"Enter OpenAI key", |
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value="", |
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type="password", |
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) |
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submitted = st.form_submit_button("Submit") |
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if submitted: |
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api_key = save_key(openai_key) |
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openai.api_key = api_key |
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generated_text = gpt_model(edited_prompt) |
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with col2: |
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st.subheader("Answer:") |
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st.write(generated_text) |
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elif decoder_model == "T5": |
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t5_pipeline = get_t5_model() |
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output_text = [] |
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for context_text in context_list: |
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output_text.append(t5_pipeline(context_text)[0]["summary_text"]) |
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with col2: |
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st.subheader("Answer:") |
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for text in output_text: |
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st.markdown(f"- {text}") |
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elif decoder_model == "FLAN-T5": |
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flan_t5_pipeline = get_flan_t5_model() |
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output_text = [] |
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for context_text in context_list: |
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output_text.append(flan_t5_pipeline(context_text)[0]["summary_text"]) |
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with col2: |
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st.subheader("Answer:") |
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for text in output_text: |
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st.markdown(f"- {text}") |
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with col1: |
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with st.expander("See Retrieved Text"): |
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for context_text in context_list: |
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st.markdown(f"- {context_text}") |
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file_text = retrieve_transcript(data, year, quarter, ticker) |
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with col1: |
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with st.expander("See Transcript"): |
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stx.scrollableTextbox( |
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file_text, height=700, border=False, fontFamily="Helvetica" |
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) |
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