import os
import streamlit as st
from elasticsearch import Elasticsearch

from embedders.labse import LaBSE

def search():
    status_indicator.write(f"Loading model {model_name} (it can take ~1 minute the first time)...")
    model = globals()[model_name]()
    
    status_indicator.write(f"Computing query embeddings...")
    query_vector = model(query)[0, :].tolist()
    
    status_indicator.write(f"Performing query...")
    target_field = f"{model_name}_features"
    results = es.search(
        index="sentences",
        query={
            "script_score": {
                "query": {"match_all": {}},
                "script": {
                    "source": f"cosineSimilarity(params.query_vector, '{target_field}') + 1.0",
                    "params": {"query_vector": query_vector}
                }
            }
        },
        size=limit
    )

    for result in results["hits"]["hits"]:
        sentence = result['_source']['sentence']
        score =  result['_score']
        document = result['_source']['document']
        number = result['_source']['number']

        previous = es.search(
            index="sentences",
            query={
                "bool": {
                    "must": [{
                        "term": {
                            "document": document
                            }
                        },{
                        "range": {
                            "number": {
                            "gte": number-3,
                            "lt": number,
                                }
                            }
                        }
                    ]
                }
            }
        )

        previous_hits = sorted(previous["hits"]["hits"], key=lambda e: e["_source"]["number"])
        previous_context = "".join([r["_source"]["sentence"] for r in previous_hits])


        subsequent = es.search(
            index="sentences",
            query={
                "bool": {
                    "must": [{
                        "term": {
                            "document": document
                            }
                        },{
                        "range": {
                            "number": {
                            "lte": number+3,
                            "gt": number,
                                }
                            }
                        }
                    ]
                }
            }
        )

        subsequent_hits = sorted(subsequent["hits"]["hits"], key=lambda e: e["_source"]["number"])
        subsequent_context = "".join([r["_source"]["sentence"] for r in subsequent_hits])


        document_name_results = es.search(
            index="documents",
            query={
                "bool": {
                    "must": [{
                        "term": {
                            "id": document
                            }
                        }
                    ]
                }
            }
        )

        document_name_data = document_name_results["hits"]["hits"][0]["_source"]
        document_name = f"{document_name_data['title']} - {document_name_data['author']}"

        results_placeholder.markdown(f"#### {document_name} (score: {score:.2f})\n{previous_context} **{sentence}** {subsequent_context}")


    status_indicator.write(f"Results ready...")

es = Elasticsearch(os.environ["ELASTIC_HOST"], basic_auth=os.environ["ELASTIC_AUTH"].split(":"))

st.header("Serica Intelligent Search")
st.write("Perform an intelligent search using a Sentence Embedding Transformer model on the SERICA database")
model_name = st.selectbox("Model", ["LaBSE"])
limit = st.number_input("Number of results", 10)
query = st.text_input("Query", value="")
status_indicator = st.empty()
do_search = st.button("Search")
results_placeholder = st.container()

if do_search:
    search()