# audio time series classification 

import boto3
import os
import numpy as np
import json
import onnxruntime as rt
import base64
import six
from functools import partial
import os.path
from os import path


def get_model_onnx(runtimemodel_s3_filename="runtime_model.onnx"):
    s3 = boto3.resource('s3')
    obj = s3.Object("$bucket_name", "$unique_model_id" +
                    "/runtime_model.onnx")
    model = rt.InferenceSession(obj.get()['Body'].read())
    return model

def get_preprocessor(preprocessor_s3_filename="runtime_preprocessor.zip"):
    import pickle
    from zipfile import ZipFile
    from io import BytesIO
    import os
    s3 = boto3.resource("s3")
    bucket = s3.Bucket("$bucket_name")
    
    zip_obj = s3.Object(bucket_name="$bucket_name", key="$unique_model_id/runtime_preprocessor.zip")
    buffer = BytesIO(zip_obj.get()["Body"].read())
    z = ZipFile(buffer)
    # Extract all the contents of zip file in current directory
    z.extractall("/tmp/")
    
    folderpath=os.path.dirname(os.path.abspath("/tmp/preprocessor.py"))
    file_name=os.path.basename("/tmp/preprocessor.py")

    #Then import all pkl files you want from bucket (need to generate this list from...
    # function globals)
    import os
    pickle_file_list=[]
    for file in os.listdir(folderpath):
          if file.endswith(".pkl"):
              pickle_file_list.append(os.path.join(folderpath, file))

    for i in pickle_file_list: 
          objectname=str(os.path.basename(i)).replace(".pkl","")
          objects={objectname:""}
          globals()[objectname]=pickle.load(open(str(i), "rb" ) )
      # First import preprocessor function to session from preprocessor.py
    exec(open(os.path.join(folderpath,'preprocessor.py')).read(),globals())
    return preprocessor


def get_runtimedata(runtimedata_s3_filename="runtime_data.json"):

      s3 = boto3.resource('s3')
      obj = s3.Object("$bucket_name", "$unique_model_id"+"/"+runtimedata_s3_filename)
      runtime_data = json.load(obj.get()['Body'])

      return runtime_data


runtime_data=get_runtimedata(runtimedata_s3_filename="runtime_data.json")

# preprocessor_type=runtime_data["runtime_preprocessor"]

runtime_model=runtime_data["runtime_model"]["name"]

model=get_model_onnx(runtimemodel_s3_filename='runtime_model.onnx')

# Load preprocessor

preprocessor=get_preprocessor(preprocessor_s3_filename="runtime_preprocessor.zip")
    
def predict(event,model,preprocessor):

# Load base64 encoded audio stored within "data" key of event dictionary
  print(event["body"])
  body = event["body"]
  if isinstance(event["body"], six.string_types):
        body = json.loads(event["body"])
# only supporting wav extension as of now
  extension = body['extension']
  bodydata=body["data"]

  
  sample = base64.decodebytes(bytearray(bodydata, "utf-8"))

# Save audio to local file, read into session, and preprocess image with preprocessor function
  with open("/tmp/audiotopredict."+extension, "wb") as fh:
    fh.write(base64.b64decode(bodydata))

  
  input_data = preprocessor(f"/tmp/audiotopredict.{extension}")

# Generate prediction using preprocessed input data
  print("The model expects input shape:", model.get_inputs()[0].shape)
  
  input_name = model.get_inputs()[0].name

  input_data = np.float32(input_data)

  res = model.run(None, {input_name: input_data})
 
  #extract predicted probability for all classes, extract predicted label
  
  prob = res[0]

  def predict_classes(x): 
    proba=x
    if proba.shape[-1] > 1:
      return proba.argmax(axis=-1)
    else:
      return (proba > 0.5).astype("int32")
      
  prediction_index=predict_classes(prob)

  labels=$labels
  result=list(map(lambda x: labels[x], prediction_index))

  os.remove("/tmp/audiotopredict."+extension)

  return result

def handler(event, context):
    result = predict(event,model,preprocessor)
    return {"statusCode": 200,
    "headers": {
    "Access-Control-Allow-Origin" : "*",
    "Access-Control-Allow-Credentials": True,
    "Allow" : "GET, OPTIONS, POST",
    "Access-Control-Allow-Methods" : "GET, OPTIONS, POST",
    "Access-Control-Allow-Headers" : "*"
    },
    "body" : json.dumps(result)}
