from itertools import chain

from injector import inject, singleton
from llama_index.core import (
    Document,
    StorageContext,
    SummaryIndex,
)
from llama_index.core.base.response.schema import Response, StreamingResponse
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.response_synthesizers import ResponseMode
from llama_index.core.storage.docstore.types import RefDocInfo
from llama_index.core.types import TokenGen

from private_gpt.components.embedding.embedding_component import EmbeddingComponent
from private_gpt.components.llm.llm_component import LLMComponent
from private_gpt.components.node_store.node_store_component import NodeStoreComponent
from private_gpt.components.vector_store.vector_store_component import (
    VectorStoreComponent,
)
from private_gpt.open_ai.extensions.context_filter import ContextFilter
from private_gpt.settings.settings import Settings

#######
# Modification par SPC
#
# Forcer à utiliser le prompt de settings.yaml et non un prompt par défaut
#
# DEFAULT_SUMMARIZE_PROMPT = (
#     "Provide a comprehensive summary of the provided context information. "
#     "The summary should cover all the key points and main ideas presented in "
#     "the original text, while also condensing the information into a concise "
#     "and easy-to-understand format. Please ensure that the summary includes "
#     "relevant details and examples that support the main ideas, while avoiding "
#      "any unnecessary information or repetition."
# )

from private_gpt.settings.settings import settings
DEFAULT_SUMMARIZE_PROMPT = settings().ui.default_summarization_system_prompt
#
# Fin modification par SPC
#######

@singleton
class SummarizeService:
    @inject
    def __init__(
        self,
        settings: Settings,
        llm_component: LLMComponent,
        node_store_component: NodeStoreComponent,
        vector_store_component: VectorStoreComponent,
        embedding_component: EmbeddingComponent,
    ) -> None:
        self.settings = settings
        self.llm_component = llm_component
        self.node_store_component = node_store_component
        self.vector_store_component = vector_store_component
        self.embedding_component = embedding_component
        self.storage_context = StorageContext.from_defaults(
            vector_store=vector_store_component.vector_store,
            docstore=node_store_component.doc_store,
            index_store=node_store_component.index_store,
        )

    @staticmethod
    def _filter_ref_docs(
        ref_docs: dict[str, RefDocInfo], context_filter: ContextFilter | None
    ) -> list[RefDocInfo]:
        if context_filter is None or not context_filter.docs_ids:
            return list(ref_docs.values())

        return [
            ref_doc
            for doc_id, ref_doc in ref_docs.items()
            if doc_id in context_filter.docs_ids
        ]

    def _summarize(
        self,
        use_context: bool = False,
        stream: bool = False,
        text: str | None = None,
        instructions: str | None = None,
        context_filter: ContextFilter | None = None,
        prompt: str | None = None,
    ) -> str | TokenGen:

        nodes_to_summarize = []

        # Add text to summarize
        if text:
            text_documents = [Document(text=text)]
            nodes_to_summarize += (
                SentenceSplitter.from_defaults().get_nodes_from_documents(
                    text_documents
                )
            )

        # Add context documents to summarize
        if use_context:
            # 1. Recover all ref docs
            ref_docs: dict[str, RefDocInfo] | None = (
                self.storage_context.docstore.get_all_ref_doc_info()
            )
            if ref_docs is None:
                raise ValueError("No documents have been ingested yet.")

            # 2. Filter documents based on context_filter (if provided)
            filtered_ref_docs = self._filter_ref_docs(ref_docs, context_filter)

            # 3. Get all nodes from the filtered documents
            filtered_node_ids = chain.from_iterable(
                [ref_doc.node_ids for ref_doc in filtered_ref_docs]
            )
            filtered_nodes = self.storage_context.docstore.get_nodes(
                node_ids=list(filtered_node_ids),
            )

            nodes_to_summarize += filtered_nodes

        # Create a SummaryIndex to summarize the nodes
        summary_index = SummaryIndex(
            nodes=nodes_to_summarize,
            storage_context=StorageContext.from_defaults(),  # In memory SummaryIndex
            show_progress=True,
        )

        # Make a tree summarization query
        # above the set of all candidate nodes
        query_engine = summary_index.as_query_engine(
            llm=self.llm_component.llm,
            response_mode=ResponseMode.TREE_SUMMARIZE,
            streaming=stream,
            use_async=self.settings.summarize.use_async,
        )

        prompt = prompt or DEFAULT_SUMMARIZE_PROMPT

        summarize_query = prompt + "\n" + (instructions or "")

        response = query_engine.query(summarize_query)
        if isinstance(response, Response):
            return response.response or ""
        elif isinstance(response, StreamingResponse):
            return response.response_gen
        else:
            raise TypeError(f"The result is not of a supported type: {type(response)}")

    def summarize(
        self,
        use_context: bool = False,
        text: str | None = None,
        instructions: str | None = None,
        context_filter: ContextFilter | None = None,
        prompt: str | None = None,
    ) -> str:
        return self._summarize(
            use_context=use_context,
            stream=False,
            text=text,
            instructions=instructions,
            context_filter=context_filter,
            prompt=prompt,
        )  # type: ignore

    def stream_summarize(
        self,
        use_context: bool = False,
        text: str | None = None,
        instructions: str | None = None,
        context_filter: ContextFilter | None = None,
        prompt: str | None = None,
    ) -> TokenGen:
        return self._summarize(
            use_context=use_context,
            stream=True,
            text=text,
            instructions=instructions,
            context_filter=context_filter,
            prompt=prompt,
        )  # type: ignore
