# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2021 deepset GmbH. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
from json import JSONDecodeError
import streamlit as st
from markdown import markdown
from pipelines.document_stores import FAISSDocumentStore, MilvusDocumentStore
from pipelines.nodes import DensePassageRetriever, ErnieRanker
from pipelines.utils import (
    convert_files_to_dicts,
    fetch_archive_from_http,
    print_documents,
)

# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu",
                    help="Select which device to run dense_qa system, defaults to gpu.")
parser.add_argument("--index_name", default='dureader_index', type=str, help="The ann index name of ANN.")
parser.add_argument("--search_engine", choices=['faiss', 'milvus'], default="faiss",
                    help="The type of ANN search engine.")
parser.add_argument("--max_seq_len_query", default=64, type=int,
                    help="The maximum total length of query after tokenization.")
parser.add_argument("--max_seq_len_passage", default=256, type=int,
                    help="The maximum total length of passage after tokenization.")
parser.add_argument("--retriever_batch_size", default=16, type=int,
                    help="The batch size of retriever to extract passage embedding for building ANN index.")
parser.add_argument("--query_embedding_model", default="rocketqa-zh-nano-query-encoder", type=str,
                    help="The query_embedding_model path")
parser.add_argument("--passage_embedding_model", default="rocketqa-zh-nano-query-encoder", type=str,
                    help="The passage_embedding_model path")
parser.add_argument("--params_path", default="checkpoints/model_40/model_state.pdparams", type=str,
                    help="The checkpoint path")
parser.add_argument("--embedding_dim", default=312, type=int, help="The embedding_dim of index")
parser.add_argument('--host', type=str, default="localhost", help='host ip of ANN search engine')
parser.add_argument('--port', type=str, default="8530", help='port of ANN search engine')
parser.add_argument('--embed_title', default=False, type=bool, help="The title to be  embedded into embedding")
parser.add_argument('--model_type', choices=['ernie_search', 'ernie', 'bert', 'neural_search'], default="ernie",
                    help="the ernie model types")
args = parser.parse_args()


# yapf: enable


def get_faiss_retriever(use_gpu):
    faiss_document_store = "faiss_document_store.db"
    if os.path.exists(args.index_name) and os.path.exists(faiss_document_store):
        # connect to existed FAISS Index
        print("connect to existed FAISS Index!")
        document_store = FAISSDocumentStore.load(args.index_name)
        retriever = DensePassageRetriever(
            document_store=document_store,
            query_embedding_model=args.query_embedding_model,
            passage_embedding_model=args.passage_embedding_model,
            params_path=args.params_path,
            output_emb_size=args.embedding_dim if args.model_type in ["ernie_search", "neural_search"] else None,
            max_seq_len_query=args.max_seq_len_query,
            max_seq_len_passage=args.max_seq_len_passage,
            batch_size=args.retriever_batch_size,
            use_gpu=use_gpu,
            embed_title=args.embed_title,
        )
    else:
        doc_dir = "resume_data"
        dicts = convert_files_to_dicts(dir_path=doc_dir, split_paragraphs=True, encoding="utf-8")

        if os.path.exists(args.index_name):
            os.remove(args.index_name)
        if os.path.exists(faiss_document_store):
            os.remove(faiss_document_store)

        document_store = FAISSDocumentStore(embedding_dim=args.embedding_dim, faiss_index_factory_str="Flat")
        document_store.write_documents(dicts)

        retriever = DensePassageRetriever(
            document_store=document_store,
            query_embedding_model=args.query_embedding_model,
            passage_embedding_model=args.passage_embedding_model,
            params_path=args.params_path,
            output_emb_size=args.embedding_dim if args.model_type in ["ernie_search", "neural_search"] else None,
            max_seq_len_query=args.max_seq_len_query,
            max_seq_len_passage=args.max_seq_len_passage,
            batch_size=args.retriever_batch_size,
            use_gpu=use_gpu,
            embed_title=args.embed_title,
        )

        # update Embedding
        document_store.update_embeddings(retriever)

        # save index
        document_store.save(args.index_name)
    return retriever


def get_milvus_retriever(use_gpu):
    milvus_document_store = "milvus_document_store.db"
    if os.path.exists(milvus_document_store):
        document_store = MilvusDocumentStore(
            embedding_dim=args.embedding_dim,
            host=args.host,
            index=args.index_name,
            port=args.port,
            index_param={"M": 16, "efConstruction": 50},
            index_type="HNSW",
        )
        # connect to existed Milvus Index
        retriever = DensePassageRetriever(
            document_store=document_store,
            query_embedding_model=args.query_embedding_model,
            passage_embedding_model=args.passage_embedding_model,
            params_path=args.params_path,
            output_emb_size=args.embedding_dim if args.model_type in ["ernie_search", "neural_search"] else None,
            max_seq_len_query=args.max_seq_len_query,
            max_seq_len_passage=args.max_seq_len_passage,
            batch_size=args.retriever_batch_size,
            use_gpu=use_gpu,
            embed_title=args.embed_title,
        )
    else:
        doc_dir = "data/dureader_dev"
        dureader_data = "https://paddlenlp.bj.bcebos.com/applications/dureader_dev.zip"

        fetch_archive_from_http(url=dureader_data, output_dir=doc_dir)
        dicts = convert_files_to_dicts(dir_path=doc_dir, split_paragraphs=True, encoding="utf-8")
        document_store = MilvusDocumentStore(
            embedding_dim=args.embedding_dim,
            host=args.host,
            index=args.index_name,
            port=args.port,
            index_param={"M": 16, "efConstruction": 50},
            index_type="HNSW",
        )
        retriever = DensePassageRetriever(
            document_store=document_store,
            query_embedding_model=args.query_embedding_model,
            passage_embedding_model=args.passage_embedding_model,
            params_path=args.params_path,
            output_emb_size=args.embedding_dim if args.model_type in ["ernie_search", "neural_search"] else None,
            max_seq_len_query=args.max_seq_len_query,
            max_seq_len_passage=args.max_seq_len_passage,
            batch_size=args.retriever_batch_size,
            use_gpu=use_gpu,
            embed_title=args.embed_title,
        )

        document_store.write_documents(dicts)
        # update Embedding
        document_store.update_embeddings(retriever)

    return retriever


use_gpu = True if args.device == "gpu" else False

if args.search_engine == "milvus":
    retriever = get_milvus_retriever(use_gpu)
else:
    retriever = get_faiss_retriever(use_gpu)

# Ranker
ranker = ErnieRanker(model_name_or_path="rocketqa-zh-dureader-cross-encoder", use_gpu=use_gpu)

# Pipeline
from pipelines import SemanticSearchPipeline

pipe = SemanticSearchPipeline(retriever, ranker)


def semantic_search_tutorial(query):
    prediction = pipe.run(query=query, params={"Retriever": {"top_k": 20}, "Ranker": {"top_k": 10}})
    print(prediction)
    docs = prediction["documents"]
    return docs


# Adjust to a question that you would like users to see in the search bar when they load the UI:
DEFAULT_QUESTION_AT_STARTUP = os.getenv("DEFAULT_QUESTION_AT_STARTUP", "具有金融科技背景的人才?")
DEFAULT_ANSWER_AT_STARTUP = os.getenv("DEFAULT_ANSWER_AT_STARTUP", "")

# Sliders
DEFAULT_DOCS_FROM_RETRIEVER = int(os.getenv("DEFAULT_DOCS_FROM_RETRIEVER", "50"))
DEFAULT_NUMBER_OF_ANSWERS = int(os.getenv("DEFAULT_NUMBER_OF_ANSWERS", "10"))


def set_state_if_absent(key, value):
    if key not in st.session_state:
        st.session_state[key] = value


def on_change_text():
    st.session_state.question = st.session_state.quest
    st.session_state.answer = None
    st.session_state.results = None
    st.session_state.raw_json = None


def main():
    st.set_page_config(
        page_title="人才简历语义检索演示系统",
        page_icon="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/imgs/logo.png",
    )

    # Persistent state
    set_state_if_absent("question", DEFAULT_QUESTION_AT_STARTUP)
    set_state_if_absent("results", None)
    set_state_if_absent("raw_json", None)
    set_state_if_absent("random_question_requested", False)

    # Small callback to reset the interface in case the text of the question changes
    def reset_results(*args):
        st.session_state.answer = None
        st.session_state.results = None
        st.session_state.raw_json = None

    # Title
    st.write("# PaddleNLP 人才简历语义检索演示系统")
    # Sidebar
    st.sidebar.header("选项")
    top_k_reader = st.sidebar.slider(
        "最大的答案的数量",
        min_value=1,
        max_value=30,
        value=DEFAULT_NUMBER_OF_ANSWERS,
        step=1,
        on_change=reset_results,
    )
    top_k_retriever = st.sidebar.slider(
        "最大检索数量",
        min_value=1,
        max_value=100,
        value=DEFAULT_DOCS_FROM_RETRIEVER,
        step=1,
        on_change=reset_results,
    )

    # Search bar
    question = st.text_input(
        "",
        value=st.session_state.question,
        key="quest",
        on_change=on_change_text,
        max_chars=100,
        placeholder="请输入您的问题",
    )
    col1, col2 = st.columns(2)
    col1.markdown("<style>.stButton button {width:100%;}</style>", unsafe_allow_html=True)
    col2.markdown("<style>.stButton button {width:100%;}</style>", unsafe_allow_html=True)
    # Run button
    run_pressed = col1.button("运行")

    st.session_state.random_question_requested = False

    run_query = ( run_pressed or question != st.session_state.question) and not st.session_state.random_question_requested
    
    # Get results for query
    if (run_query or st.session_state.results is None) and question:
        reset_results()
        st.session_state.question = question
        with st.spinner(
                "🧠 &nbsp;&nbsp; Performing neural search on documents... \n "
                "Do you want to optimize speed or accuracy? \n"
        ):
            try:
                st.session_state.results = semantic_search_tutorial(question)
            except JSONDecodeError:
                st.error("👓 &nbsp;&nbsp; An error occurred reading the results. Is the document store working?")
                return

    if st.session_state.results:

        st.write("## 返回结果:")
        for count, result in enumerate(st.session_state.results):
            context = result.content
            st.write(
                markdown(context),
                unsafe_allow_html=True,
            )
            # st.write("**Relevance:** ", result["relevance"])

            st.write("___")


if __name__ == "__main__":
    main()