# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# 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 os
from typing import Any, Dict, List

from fastapi import FastAPI, HTTPException

from ...infra import utils as serving_utils
from ...infra.config import AppConfig
from ...infra.models import ResultResponse
from ...schemas.video_classification import INFER_ENDPOINT, InferRequest, InferResult
from .._app import create_app, primary_operation


def create_pipeline_app(pipeline: Any, app_config: AppConfig) -> FastAPI:
    app, ctx = create_app(
        pipeline=pipeline, app_config=app_config, app_aiohttp_session=True
    )

    @primary_operation(
        app,
        INFER_ENDPOINT,
        "infer",
    )
    async def _infer(request: InferRequest) -> ResultResponse[InferResult]:
        pipeline = ctx.pipeline
        aiohttp_session = ctx.aiohttp_session

        file_bytes = await serving_utils.get_raw_bytes_async(
            request.video, aiohttp_session
        )
        ext = serving_utils.infer_file_ext(request.video)
        if ext is None:
            raise HTTPException(
                status_code=422, detail="File extension cannot be inferred"
            )
        video_path = await serving_utils.call_async(
            serving_utils.write_to_temp_file,
            file_bytes,
            suffix=ext,
        )

        try:
            result = (await pipeline.infer(video_path, topk=request.topk))[0]
        finally:
            await serving_utils.call_async(os.unlink, video_path)

        if "label_names" in result:
            cat_names = result["label_names"]
        else:
            cat_names = [str(id_) for id_ in result["class_ids"]]
        categories: List[Dict[str, Any]] = []
        for id_, name, score in zip(result["class_ids"], cat_names, result["scores"]):
            categories.append(dict(id=id_, name=name, score=score))

        return ResultResponse[InferResult](
            logId=serving_utils.generate_log_id(),
            result=InferResult(categories=categories),
        )

    return app