from fastapi import FastAPI, APIRouter, Depends, UploadFile, status, Request
from fastapi.responses import JSONResponse
import os
from helpers.config import get_settings, Settings
from controllers import DataController, ProjectController, ProcessController
import aiofiles
from models import ResponseSignal
import logging
from .schemes.data import ProcessRequest
from models.ProjectModel import ProjectModel
from models.ChunkModel import ChunkModel
from models.AssetModel import AssetModel
from models.db_schemes.minirag.schemes import DataChunk, Asset
from models.enums.AssetTypeEnum import AssetTypeEnum
from controllers import NLPController

logger = logging.getLogger('uvicorn.error')

data_router = APIRouter(
    prefix="/api/v1/data",
    tags=["api_v1", "data"],
)

@data_router.post("/upload/{project_id}")
async def upload_data(request: Request, project_id: int, file: UploadFile,
                      app_settings: Settings = Depends(get_settings)):
        
    
    project_model = await ProjectModel.create_instance(
        db_client=request.app.db_client
    )

    project = await project_model.get_project_or_create_one(
        project_id=project_id
    )

    # validate the file properties
    data_controller = DataController()

    is_valid, result_signal = data_controller.validate_uploaded_file(file=file)

    if not is_valid:
        return JSONResponse(
            status_code=status.HTTP_400_BAD_REQUEST,
            content={
                "signal": result_signal.value
            }
        )

    project_dir_path = ProjectController().get_project_path(project_id=project_id)
    file_path, file_id = data_controller.generate_unique_filepath(
        orig_file_name=file.filename,
        project_id=project_id
    )

    try:
        async with aiofiles.open(file_path, "wb") as f:
            while chunk := await file.read(app_settings.FILE_DEFAULT_CHUNK_SIZE):
                await f.write(chunk)
    except Exception as e:

        logger.error(f"Error while uploading file: {e}")

        return JSONResponse(
            status_code=status.HTTP_400_BAD_REQUEST,
            content={
                # Bug: the enum name is `FILE_UPLOADED_FAILED`, not `FILE_UPLOAD_FAILED`.
                # Fix: use the actual enum member name.
                "signal": ResponseSignal.FILE_UPLOADED_FAILED.value
            }
        )

    # store the assets into the database
    asset_model = await AssetModel.create_instance(
        db_client=request.app.db_client
    )

    asset_resource = Asset(
        asset_project_id=project.project_id,
        asset_type=AssetTypeEnum.FILE.value,
        asset_name=file_id,
        asset_size=os.path.getsize(file_path)
    )

    asset_record = await asset_model.create_asset(asset=asset_resource)

    return JSONResponse(
            content={
                # Bug: the enum name is `FILE_UPLOADED_SUCCESS`, not `FILE_UPLOAD_SUCCESS`.
                # Fix: use the correct enum member.
                "signal": ResponseSignal.FILE_UPLOADED_SUCCESS.value,
                # Bug: later processing expects the saved filename, not the Mongo asset id.
                # Fix: return the stored asset name as `file_id`, and expose the DB id separately as `asset_id`.
                "file_id": asset_record.asset_name,
                "asset_id": str(asset_record.asset_id),
            }
        )

@data_router.post("/process/{project_id}")
async def process_endpoint(request: Request, project_id: int, process_request: ProcessRequest):

    chunk_size = process_request.chunk_size
    overlap_size = process_request.overlap_size
    do_reset = process_request.do_reset

    nlp_controller = NLPController(
        vectordb_client=request.app.vectordb_client,
        generation_client=request.app.generation_client,
        embedding_client=request.app.generation_client,
        template_parser=request.app.template_parser,
    )
    project_model = await ProjectModel.create_instance(
        db_client=request.app.db_client
    )

    project = await project_model.get_project_or_create_one(
        project_id=project_id
    )

    asset_model = await AssetModel.create_instance(
            db_client=request.app.db_client
        )

    project_files_ids = {}

    if process_request.file_id:
        asset_record = await asset_model.get_asset_record(
            asset_project_id=project.project_id,
            asset_name=process_request.file_id
        )
        if asset_record is None:
            return JSONResponse(
                status_code=status.HTTP_400_BAD_REQUEST,
                content={
                    "signal": ResponseSignal.FILE_ID_ERROR.value,
                }
            )
        project_files_ids = {
            asset_record.asset_id: asset_record.asset_name
        }
    
    else:
        project_files = await asset_model.get_all_project_assets(
            asset_project_id=project.project_id,
            asset_type=AssetTypeEnum.FILE.value,
        )

        project_files_ids = {
            record.asset_id: record.asset_name
            for record in project_files
        }

    if len(project_files_ids) == 0:
        return JSONResponse(
            status_code=status.HTTP_400_BAD_REQUEST,
            content={
                "signal": ResponseSignal.NO_FILES_ERROR.value,
            }
        )
    
    process_controller = ProcessController(project_id=project_id)

    no_records = 0
    no_files = 0

    chunk_model = await ChunkModel.create_instance(
                        db_client=request.app.db_client
                    )

    if do_reset == 1:
        # delete associated vectors collection 
        collection_name = nlp_controller.create_collection_name(project_id=project.project_id)
        _ = await request.app.vectordb_client.delete_collection(collection_name=collection_name)
        
        # delete associated chunks 
        _ = await chunk_model.delete_chunks_by_project_id(
            project_id=project.project_id
        )
    for asset_id, file_id in project_files_ids.items():

        file_content = process_controller.get_file_content(file_id=file_id)

        if file_content is None:
            logger.error(f"Error while processing file: {file_id}")
            continue

        file_chunks = process_controller.process_file_content(
            file_content=file_content,
            file_id=file_id,
            chunk_size=chunk_size,
            overlap_size=overlap_size
        )

        if file_chunks is None or len(file_chunks) == 0:
            return JSONResponse(
                status_code=status.HTTP_400_BAD_REQUEST,
                content={
                    "signal": ResponseSignal.PROCESSING_FAILED.value
                }
            )

        file_chunks_records = [
            DataChunk(
                chunk_text=chunk.page_content,
                chunk_metadata=chunk.metadata,
                chunk_order=i+1,
                chunk_project_id=project.project_id,
                chunk_asset_id=asset_id
            )
            for i, chunk in enumerate(file_chunks)
        ]

        no_records += await chunk_model.insert_many_chunks(chunks=file_chunks_records)
        no_files += 1

    return JSONResponse(
        content={
            # Note: this endpoint only processes files into chunks in Mongo.
            # The project already uses this success enum, so we keep it for compatibility even though the name is misleading.
            "signal": ResponseSignal.INSERT_INTO_VECTORDB_SUCCESS.value,
            "inserted_chunks": no_records,
            "processed_files": no_files
        }
    )