import streamlit as st
from sentence_transformers import SentenceTransformer
import datasets
import faiss
import torch
from sentence_transformers.util import semantic_search
import time


if "initialized" not in st.session_state:
    st.session_state.dataset = datasets.load_dataset('A-Roucher/english_historical_quotes', download_mode="force_redownload")['train']
    st.session_state.all_authors = list(set(st.session_state.dataset['author']))
    model_name = "sentence-transformers/all-MiniLM-L6-v2" # BAAI/bge-small-en-v1.5" # "Cohere/Cohere-embed-english-light-v3.0" # "sentence-transformers/all-MiniLM-L6-v2"
    st.session_state.encoder = SentenceTransformer(model_name)
    st.session_state.embeddings = st.session_state.encoder.encode(
        st.session_state.dataset["quote"],
        batch_size=4,
        show_progress_bar=True,
        convert_to_numpy=True,
        normalize_embeddings=True,
    )
    st.session_state.initialized=True

dataset_embeddings_tensor = torch.Tensor(st.session_state.embeddings)

sentence = "Knowledge of history is power."

def search(query, selected_authors):
    start = time.time()
    if len(query.strip()) == 0:
        return ""
    
    query_embedding = st.session_state.encoder.encode([query])
    sentence_embedding_tensor = torch.Tensor(query_embedding)

    if len(selected_authors) == 0:
        author_indexes = [i for i in range(len(st.session_state.dataset))]
    else:
        author_indexes = [i for i in range(len(st.session_state.dataset)) if st.session_state.dataset['author'][i] in selected_authors]
    hits = semantic_search(sentence_embedding_tensor, dataset_embeddings_tensor[author_indexes, :], top_k=5)

    indices = [author_indexes[i['corpus_id']] for i in hits[0]]

    if len(indices) == 0:
        return ""
    result = "\n\n"
    for i in indices:
        result += f"###### {st.session_state.dataset['author'][i]}\n> {st.session_state.dataset['quote'][i]}\n----\n"
    delay = "%.3f" % (time.time() - start)
    return f"_Computation time: **{delay} seconds**_{result}"


st.markdown(
    """
    <style>
        div[data-testid="column"]
        {
            align-self:flex-end;
        }
    </style>
    """,unsafe_allow_html=True
)
col1, col2 = st.columns([8, 2])
text_input = col1.text_input("Type your idea here:")
submit_button = col2.button("_Search quotes!_")
selected_authors = st.multiselect("(Optional) - Restrict search to these authors:", st.session_state.all_authors)

if submit_button:
    st.markdown(search(text_input, selected_authors))