import openai
import pinecone
import streamlit_scrollable_textbox as stx

import streamlit as st
from utils import (
    clean_entities,
    create_dense_embeddings,
    create_sparse_embeddings,
    extract_entities,
    format_query,
    generate_flant5_prompt,
    generate_gpt_prompt,
    get_context_list_prompt,
    get_data,
    get_flan_t5_model,
    get_mpnet_embedding_model,
    get_sgpt_embedding_model,
    get_spacy_model,
    get_splade_sparse_embedding_model,
    get_t5_model,
    gpt_model,
    hybrid_score_norm,
    query_pinecone,
    query_pinecone_sparse,
    retrieve_transcript,
    save_key,
    sentence_id_combine,
    text_lookup,
)

st.set_page_config(layout="wide")  # isort: skip


st.title("Abstractive Question Answering")


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")


spacy_model = get_spacy_model()

with col1:
    st.subheader("Question")
    query_text = st.text_input(
        "Input Query",
        value="What was discussed regarding Wearables revenue performance in Q1 2020?",
    )

company_ent, quarter_ent, year_ent = extract_entities(query_text, spacy_model)
ticker_index, quarter_index, year_index = clean_entities(
    company_ent, quarter_ent, year_ent
)

with col1:
    years_choice = ["2020", "2019", "2018", "2017", "2016", "All"]

with col1:
    year = st.selectbox("Year", years_choice, index=year_index)

with col1:
    quarter = st.selectbox(
        "Quarter", ["Q1", "Q2", "Q3", "Q4", "All"], index=quarter_index
    )

with col1:
    participant_type = st.selectbox("Speaker", ["Company Speaker", "Analyst"])

ticker_choice = [
    "AAPL",
    "CSCO",
    "MSFT",
    "ASML",
    "NVDA",
    "GOOGL",
    "MU",
    "INTC",
    "AMZN",
    "AMD",
]

with col1:
    ticker = st.selectbox("Company", ticker_choice, ticker_index)

with st.sidebar:
    st.subheader("Select Options:")

with st.sidebar:
    num_results = int(
        st.number_input("Number of Results to query", 1, 15, value=6)
    )


# Choose encoder model

encoder_models_choice = ["MPNET", "SGPT", "Hybrid MPNET - SPLADE"]
with st.sidebar:
    encoder_model = st.selectbox("Select Encoder Model", encoder_models_choice)


# Choose decoder model

decoder_models_choice = [
    "GPT3 - (text-davinci-003)",
    "T5",
    "FLAN-T5",
]

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 == "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:
    window = int(st.number_input("Sentence Window Size", 0, 10, value=1))

with st.sidebar:
    threshold = float(
        st.number_input(
            label="Similarity Score Threshold",
            step=0.05,
            format="%.2f",
            value=0.25,
        )
    )

data = get_data()

if encoder_model == "Hybrid SGPT - SPLADE":
    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
    )
    query_results = query_pinecone_sparse(
        dense_query_embedding,
        sparse_query_embedding,
        num_results,
        pinecone_index,
        year,
        quarter,
        ticker,
        participant_type,
        threshold,
    )

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,
        threshold,
    )


if threshold <= 0.90:
    context_list = sentence_id_combine(data, query_results, lag=window)
else:
    context_list = format_query(query_results)


if decoder_model == "GPT3 - (text-davinci-003)":
    prompt = generate_gpt_prompt(query_text, context_list)
    with col2:
        with st.form("my_form"):
            edited_prompt = st.text_area(
                label="Model Prompt", value=prompt, height=270
            )

            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_model(edited_prompt)
                st.subheader("Answer:")
                st.write(generated_text)


elif decoder_model == "T5":
    prompt = generate_flant5_prompt(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=270
            )
            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":
    prompt = generate_flant5_prompt(query_text, context_list)
    flan_t5_pipeline = get_flan_t5_model()
    output_text = []
    with col2:
        with st.form("my_form"):
            edited_prompt = st.text_area(
                label="Model Prompt", value=prompt, height=270
            )
            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(
                        flan_t5_pipeline("Summarize:" + context_text)[0][
                            "summary_text"
                        ]
                    )
                st.subheader("Answer:")
                for text in output_text:
                    if "(iii)" not in text:
                        st.markdown(f"- {text}")


with col1:
    with st.expander("See Retrieved Text"):
        for context_text in context_list:
            st.markdown(f"- {context_text}")

file_text = retrieve_transcript(data, year, quarter, ticker)

with col1:
    with st.expander("See Transcript"):
        stx.scrollableTextbox(
            file_text, height=700, border=False, fontFamily="Helvetica"
        )