import logging
from typing import Any, cast, List

import numpy as np
from eth_abi import decode, encode  # type: ignore
from infernet_ml.utils.model_loader import ModelSource
from infernet_ml.utils.service_models import InfernetInput, InfernetInputSource
from infernet_ml.workflows.inference.onnx_inference_workflow import (
    ONNXInferenceWorkflow,
)
from quart import Quart, request
from quart.json.provider import DefaultJSONProvider

log = logging.getLogger(__name__)


class NumpyJsonEncodingProvider(DefaultJSONProvider):
    @staticmethod
    def default(obj: Any) -> Any:
        if isinstance(obj, np.ndarray):
            # Convert NumPy arrays to list
            return obj.tolist()
        # fallback to default JSON encoding
        return DefaultJSONProvider.default(obj)


def create_app() -> Quart:
    Quart.json_provider_class = NumpyJsonEncodingProvider
    app = Quart(__name__)
    # we are downloading the model from the hub.
    # model repo is located at: https://huggingface.co/Ritual-Net/iris-dataset
    model_source = ModelSource.HUGGINGFACE_HUB
    model_args = {"repo_id": "Ritual-Net/iris-dataset", "filename": "iris.onnx"}

    workflow = ONNXInferenceWorkflow(model_source=model_source, model_args=model_args)
    workflow.setup()

    @app.route("/")
    def index() -> str:
        """
        Utility endpoint to check if the service is running.
        """
        return "ONNX Iris Classifier Example Program"

    @app.route("/service_output", methods=["POST"])
    async def inference() -> dict[str, Any]:
        req_data = await request.get_json()
        """
        InfernetInput has the format:
            source: (0 on-chain, 1 off-chain)
            data: dict[str, Any]
        """
        infernet_input: InfernetInput = InfernetInput(**req_data)

        if infernet_input.source == InfernetInputSource.OFFCHAIN:
            web2_input = cast(dict[str, Any], infernet_input.data)
            values = cast(List[List[float]], web2_input["input"])
        else:
            # On-chain requests are sent as a generalized hex-string which we will
            # decode to the appropriate format.
            web3_input: List[int] = decode(
                ["uint256[]"], bytes.fromhex(cast(str, infernet_input.data))
            )[0]
            values = [[float(v) / 1e6 for v in web3_input]]

        """
        The input to the onnx inference workflow needs to conform to ONNX runtime's
        input_feed format. For more information refer to:
        https://docs.ritual.net/ml-workflows/inference-workflows/onnx_inference_workflow
        """
        result: dict[str, Any] = workflow.inference({"input": values})

        if infernet_input.source == InfernetInputSource.OFFCHAIN:
            """
            In case of an off-chain request, the result is returned as is.
            """
            return result
        else:
            """
            In case of an on-chain request, the result is returned in the format:
            {
                "raw_input": str,
                "processed_input": str,
                "raw_output": str,
                "processed_output": str,
                "proof": str,
            }
            refer to: https://docs.ritual.net/infernet/node/containers for more info.
            """
            predictions = cast(List[List[List[float]]], result)
            predictions_normalized = [int(p * 1e6) for p in predictions[0][0]]
            return {
                "raw_input": "",
                "processed_input": "",
                "raw_output": encode(["uint256[]"], [predictions_normalized]).hex(),
                "processed_output": "",
                "proof": "",
            }

    return app


if __name__ == "__main__":
    """
    Utility to run the app locally. For development purposes only.
    """
    create_app().run(port=3000)