# Copyright 2021-2023 H2020 TeraFlow (https://www.teraflow-h2020.eu/)
#
# 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.

from __future__ import print_function
from datetime import datetime
import os
import grpc
import numpy as np
import onnxruntime as rt
import logging
from common.proto.l3_centralizedattackdetector_pb2 import (
    Empty,
)
from common.proto.l3_centralizedattackdetector_pb2_grpc import (
    L3CentralizedattackdetectorServicer,
)

from common.proto.l3_attackmitigator_pb2 import (
    Output,
)
from common.proto.l3_attackmitigator_pb2_grpc import (
    L3AttackmitigatorStub,
)

LOGGER = logging.getLogger(__name__)
here = os.path.dirname(os.path.abspath(__file__))
MODEL_FILE = os.path.join(here, "ml_model/teraflow_rf.onnx")

class l3_centralizedattackdetectorServiceServicerImpl(L3CentralizedattackdetectorServicer):

    def __init__(self):
        LOGGER.debug("Creating Servicer...")
        self.inference_values = []
        self.model = rt.InferenceSession(MODEL_FILE)
        self.input_name = self.model.get_inputs()[0].name
        self.label_name = self.model.get_outputs()[0].name
        self.prob_name = self.model.get_outputs()[1].name
        

    def make_inference(self, request):
        # ML MODEL
        x_data = np.array([
                [
                    request.n_packets_server_seconds,
                    request.n_packets_client_seconds,
                    request.n_bits_server_seconds,
                    request.n_bits_client_seconds,
                    request.n_bits_server_n_packets_server,
                    request.n_bits_client_n_packets_client,
                    request.n_packets_server_n_packets_client,
                    request.n_bits_server_n_bits_client,
                ]
            ])

        predictions = self.model.run(
            [self.prob_name], {self.input_name: x_data.astype(np.float32)})[0]
        # Output format
        output_message = {
            "confidence": None,
            "timestamp": datetime.now().strftime("%d/%m/%Y %H:%M:%S"),
            "ip_o": request.ip_o,
            "tag_name": None,
            "tag": None,
            "flow_id": request.flow_id,
            "protocol": request.protocol,
            "port_d": request.port_d,
            "ml_id": "RandomForest",
            "time_start": request.time_start,
            "time_end": request.time_end,
        }
        if predictions[0][1] >= 0.5:
            output_message["confidence"] = predictions[0][1]
            output_message["tag_name"] = "Crypto"
            output_message["tag"] = 1
        else:
            output_message["confidence"] = predictions[0][0]
            output_message["tag_name"] = "Normal"
            output_message["tag"] = 0

        return Output(**output_message)

    def SendInput(self, request, context):
        # PERFORM INFERENCE WITH SENT INPUTS
        logging.debug("")
        print("Inferencing ...")

        # STORE VALUES
        self.inference_values.append(request)

        # MAKE INFERENCE
        output = self.make_inference(request)

        # SEND INFO TO MITIGATION SERVER
        try:
                with grpc.insecure_channel("localhost:10002") as channel:
                    stub = L3AttackmitigatorStub(channel)
                    print("Sending to mitigator...")
                    response = stub.SendOutput(output)
                    print("Sent output to mitigator and received: ", response.message)

                # RETURN "OK" TO THE CALLER
                return Empty(
                    message="OK, information received and mitigator notified"
                )
        except:
            print('Couldnt find l3_attackmitigator')
            return Empty(
                message="Mitigator Not found"
            )

    def GetOutput(self, request, context):
        logging.debug("")
        print("Returing inference output...")
        k = np.multiply(self.inference_values, [2])
        k = np.sum(k)
        return self.make_inference(k)