import os
import re
import nltk
import copy
import time
import joblib
import msgpack
import numpy as np
import streamlit as st
from io import StringIO
from numpy import ndarray
from llama_cpp import Llama
from langchain_core.documents.base import Document
from scipy.sparse import csr_matrix, save_npz, load_npz, vstack
from qdrant_client import QdrantClient, models
from langchain_community.document_loaders.wikipedia import WikipediaLoader
from langchain_experimental.text_splitter import SemanticChunker
from fastembed.sparse.splade_pp import supported_splade_models
from fastembed import SparseTextEmbedding, SparseEmbedding
from fastembed_ext import FastEmbedEmbeddingsLc
from huggingface_hub import hf_hub_download
from unstructured.partition.auto import partition
from qdrant_client.models import (
    NamedSparseVector,
    NamedVector,
    SparseVector,
    PointStruct,
    SearchRequest,
    ScoredPoint,
)

def make_points(chunks: list[str], dense: list[ndarray], sparse: list[SparseEmbedding])-> list[PointStruct]:
    points = []
    for idx, (sparse_vector, chunk, dense_vector) in enumerate(zip(sparse, chunks, dense)):
        sparse_vec = SparseVector(indices=sparse_vector.indices.tolist(), values=sparse_vector.values.tolist())
        point = PointStruct(
            id=idx,
            vector={
                "text-sparse": sparse_vec,
                "text-dense": dense_vector,
            },
            payload={
                "text": chunk
            }
        )
        points.append(point)
    return points

def search(client: QdrantClient, collection_name: str, dense, sparse):
    search_results = client.search_batch(
        collection_name,
        [
            SearchRequest(
                vector=NamedVector(
                    name="text-dense",
                    vector=dense,
                ),
                limit=10
            ),
            SearchRequest(
                vector=NamedSparseVector(
                    name="text-sparse",
                    vector=SparseVector(
                        indices=sparse[0].indices.tolist(),
                        values=sparse[0].values.tolist(),
                    ),
                ),
                limit=10
            ),
        ],
    )

    return search_results

def rank_list(search_result: list[ScoredPoint]):
    return [(point.id, rank + 1) for rank, point in enumerate(search_result)]

def rrf(rank_lists, alpha=60, default_rank=1000):
    """
    Optimized Reciprocal Rank Fusion (RRF) using NumPy for large rank lists.

    :param rank_lists: A list of rank lists. Each rank list should be a list of (item, rank) tuples.
    :param alpha: The parameter alpha used in the RRF formula. Default is 60.
    :param default_rank: The default rank assigned to items not present in a rank list. Default is 1000.
    :return: Sorted list of items based on their RRF scores.
    """
    all_items = set(item for rank_list in rank_lists for item, _ in rank_list)
    item_to_index = {item: idx for idx, item in enumerate(all_items)}
    rank_matrix = np.full((len(all_items), len(rank_lists)), default_rank)

    for list_idx, rank_list in enumerate(rank_lists):
        for item, rank in rank_list:
            rank_matrix[item_to_index[item], list_idx] = rank

    rrf_scores = np.sum(1.0 / (alpha + rank_matrix), axis=1)
    sorted_indices = np.argsort(-rrf_scores)
    sorted_items = [(list(item_to_index.keys())[idx], rrf_scores[idx]) for idx in sorted_indices]

    return sorted_items


def main(query: str, client: QdrantClient, collection_name: str, llm, dense_model, sparse_model):
    dense_query = list(dense_model.embed_query(query, 32))
    sparse_query = list(sparse_model.embed(query, 32))

    search_results = search(
        client,
        collection_name,
        dense_query,
        sparse_query
    )

    dense_rank_list, sparse_rank_list = rank_list(search_results[0]), rank_list(search_results[1])
    rrf_rank_list = rrf([dense_rank_list, sparse_rank_list])

    records_list = client.retrieve(
        collection_name,
        [item[0] for item in rrf_rank_list]
    )
    
    docs = [record.payload['text'] for record in records_list[:3]]

    context = "\n".join(docs)

    response = llm.create_chat_completion(
        messages = [
          {"role": "system", "content": "You are a helpful assistant."},
          {
              "role": "user",
              "content": f"""
              You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.
              
              Question: {query} 
              
              Context: {context} 
              
              Answer:"""
          }
      ], stop=["</s>"], temperature=0, frequency_penalty=0.2, presence_penalty=0.4, top_p=0.2)

    text = response["choices"][0]["message"]['content']
    print(f'TEXT: {text}')

    output = llm.create_chat_completion(
        messages = [
          {"role": "system", "content": """Act like a professional summary writer. You have been providing summarization services for various types of documents, including academic papers, legal texts, and business reports, for over 20 years. 
          Your expertise includes extracting key points and important details concisely without adding unnecessary introductory phrases."""
          },
          {
              "role": "user",
              "content": f"""Write a summary of the following text delimited by triple backquotes. Ensure the summary covers the key points of the text. Do not introduce the summary with sentences like "Here is the summary:" or similar. The summary should be detailed, precise, and directly convey the essential information from the text.
              
              ```{text}```
              
              Take a deep breath and work on this problem step-by-step."""
          }
      ], stop=["</s>"], temperature=0.7, max_tokens=3000)
    final_response = output['choices'][0]['message']['content']
    print(f'OUTPUT: {output}')
    return final_response

@st.cache_resource
def load_models_and_documents():
    print('load')
    supported_splade_models[0] = {
        "model": "prithivida/Splade_PP_en_v2",
        "vocab_size": 30522,
        "description": "Implementation of SPLADE++ Model for English v2",
        "size_in_GB": 0.532,
        "sources": {
            "hf": "devve1/Splade_PP_en_v2_onnx"
        },
        "model_file": "model.onnx"
    }
    
    with st.spinner('Load models...'):
        llm = Llama.from_pretrained(
            repo_id="MaziyarPanahi/Llama-3-8B-Instruct-32k-v0.1-GGUF",
            filename="*Q8_0.gguf",
            verbose=False,
            chat_format="chatml",
            n_ctx=16000,
            n_gpu_layers=32
        )

        provider = ['CUDAExecutionProvider']
        
        dense_model = FastEmbedEmbeddingsLc(
            model_name='mixedbread-ai/mxbai-embed-large-v1',
            providers=provider,
            cache_dir=os.getenv('HF_HOME'),
            batch_size=32
        )
        
        sparse_model = SparseTextEmbedding(
            'prithivida/Splade_PP_en_v2',
            cache_dir=os.getenv('HF_HOME'),
            providers=provider
        )

        nltk.download('punkt')
        nltk.download('averaged_perceptron_tagger')

    client = QdrantClient(':memory:')
    collection_name = 'collection_demo'
    
    client.create_collection(
        collection_name,
        {
            "text-dense": models.VectorParams(
                size=1024,
                distance=models.Distance.COSINE,
                on_disk=False,
                quantization_config=models.BinaryQuantization(
                    binary=models.BinaryQuantizationConfig(
                        always_ram=True
                    )
                )
            )
        },
        {
            "text-sparse": models.SparseVectorParams(
                index=models.SparseIndexParams(
                    on_disk=False
                )
            )
        },
        2,
        optimizers_config=models.OptimizersConfigDiff(
            indexing_threshold=0
        ),
        hnsw_config=models.HnswConfigDiff(
            on_disk=False, 
            m=16, 
            ef_construct=100
        )
    )
        
    with st.spinner('Parse and chunk documents...'):
        name = 'action_rpg'
        embeddings_path = os.path.join(os.getenv('HF_HOME'), 'embeddings')

        chunks_path = os.path.join(embeddings_path, name + '_chunks.msgpack')
        dense_path = os.path.join(embeddings_path, name + '_dense.npz')
        sparse_path = os.path.join(embeddings_path, name + '_sparse.npz')

        if not os.path.exists(embeddings_path):
            os.mkdir(embeddings_path)

            docs_1 = WikipediaLoader(query='Action-RPG').load()
            docs_2 = WikipediaLoader(query='Real-time strategy').load()
            docs_3 = WikipediaLoader(query='First-person shooter').load()
            docs_4 = WikipediaLoader(query='Multiplayer online battle arena').load()
            docs_5 = WikipediaLoader(query='List of video game genres').load()
            docs = docs_1 + docs_2 + docs_3 + docs_4 + docs_5
            chunks, dense_embeddings, sparse_embeddings = chunk_documents(docs, dense_model, sparse_model)

            with open(chunks_path, "wb") as outfile:
                packed = msgpack.packb(chunks, use_bin_type=True)
                outfile.write(packed)
            
            np.savez_compressed(dense_path, *dense_embeddings)
            max_index = max(np.max(embedding.indices) for embedding in sparse_embeddings)
        
            sparse_matrices = []
            for embedding in sparse_embeddings:
                data = embedding.values
                indices = embedding.indices
                indptr = np.array([0, len(data)])
                matrix = csr_matrix((data, indices, indptr), shape=(1, max_index + 1))
                sparse_matrices.append(matrix)
            
            combined_sparse_matrix = vstack(sparse_matrices)
            save_npz(sparse_path, combined_sparse_matrix)
        else:
            with open(chunks_path, "rb") as data_file:
                byte_data = data_file.read()
                
            chunks = msgpack.unpackb(byte_data, raw=False)

            dense_embeddings = list(np.load(dense_path).values())

            sparse_embeddings = []
            loaded_sparse_matrix = load_npz(sparse_path)
        
            for i in range(loaded_sparse_matrix.shape[0]):
                row = loaded_sparse_matrix.getrow(i)
                values = row.data
                indices = row.indices
                embedding = SparseEmbedding(values, indices)
                sparse_embeddings.append(embedding)

    with st.spinner('Save documents...'):
        client.upsert(
            collection_name,
            make_points(
                chunks, 
                dense_embeddings, 
                sparse_embeddings
            )
        )
    client.update_collection(
        collection_name=collection_name,
        optimizer_config=models.OptimizersConfigDiff(indexing_threshold=20000)
    )

    return client, collection_name, llm, dense_model, sparse_model

def chunk_documents(docs, dense_model, sparse_model):
    text_splitter = SemanticChunker(
        dense_model,
        breakpoint_threshold_type='standard_deviation'
    )

    texts, metadatas = [], []
    for doc in docs:
        texts.append(doc.page_content)
        metadatas.append(doc.metadata)

    _metadatas = metadatas or [{}] * len(texts)
    documents = []

    def create_document(text: str, i: int, _metadatas: list):
        index = -1
        for chunk in text_splitter.split_text(text):
            metadata = copy.deepcopy(_metadatas[i])
            if text_splitter._add_start_index:
                index = text.find(chunk, index + 1)
                metadata['start_index'] = index
        new_doc = Document(page_content=chunk, metadata=metadata)       
        documents.append(new_doc)
        
    joblib.Parallel(n_jobs=joblib.cpu_count(), verbose=1, require='sharedmem')(
        joblib.delayed(create_document)(text, i, _metadatas) for i, text in enumerate(texts))

    docs = [doc.page_content for doc in documents]

    dense_embeddings = dense_model.embed_documents(docs,32)
    sparse_embeddings = list(sparse_model.embed(docs, 32))
    
    return documents, dense_embeddings, sparse_embeddings
    
if __name__ == '__main__':
    st.set_page_config(page_title="Video Game Assistant",
                       layout="wide"
                       )
    st.title("Video Game Assistant")
    
    client, collection_name, llm, dense_model, sparse_model = load_models_and_documents()

    if "messages" not in st.session_state:
        st.session_state.messages = []

    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    if prompt := st.chat_input("Message Video Game Assistant"):
        st.chat_message("user").markdown(prompt)
        st.session_state.messages.append({"role": "user", "content": prompt})

        ai_response = main(prompt, client, collection_name, llm, dense_model, sparse_model)
        with st.chat_message("assistant"):
            message_placeholder = st.empty()
            full_response = ""
            for chunk in re.split(r'(\s+)', ai_response):
                full_response += chunk + " "
                time.sleep(0.01)
                message_placeholder.markdown(full_response + "▌")
        st.session_state.messages.append({"role": "assistant", "content": full_response})

    st.sidebar.title("Upload your file")
    uploaded_files = st.sidebar.file_uploader("Choose a file", accept_multiple_files=True, type=['docx', 'doc', 'odt', 'pptx', 'ppt', 'xlsx', 'csv', 'tsv', 'eml', 'msg', 'rtf', 'epub', 'html', 'xml', 'pdf', 'png', 'jpg', 'heic','txt'])
    print(f'uploaded-files : {uploaded_files}')
    for uploaded_file in uploaded_files:
        print('count')
        elements = partition(file=uploaded_file,
                             strategy='hi_res',
                             skip_infer_table_types=['png', 'pdf', 'jpg', 'xls', 'xlsx', 'heic'],
                             hi_res_model_name='yolox',
                             include_page_breaks=True
                            )
        docs = [Document(elem.text) for elem in elements]

        chunks, dense_embeddings, sparse_embeddings = chunk_documents(docs, dense_model, sparse_model)
        client.upsert(
            collection_name,
            make_points(
                chunks, 
                dense_embeddings, 
                sparse_embeddings
            )
        )