onnx==1.12.0
onnxruntime==1.12.1
torchvision
torch
matplotlib==3.5.0
opencv-python
imutils
pytz
ipywidgets

# preload needed libraries 

import wallaroo
from wallaroo.object import EntityNotFoundError
from wallaroo.framework import Framework
from IPython.display import display
from IPython.display import Image
import pandas as pd
import json
import datetime
import time
import cv2
import matplotlib.pyplot as plt
import string
import random
import pyarrow as pa
import sys
import asyncio
import numpy as np

import utils
pd.set_option('display.max_colwidth', None)

import datetime

# api based inference request
import requests

wl = wallaroo.Client(request_timeout=600)

workspace_name = "cv-retail-edge-observability"
model_name = "resnet-with-intensity"
model_file_name = "./models/model-with-pixel-intensity.zip"
pipeline_name = "retail-inv-tracker-edge-obs"

workspace = wl.get_workspace(name=workspace_name, create_if_not_exist=True)
wl.set_current_workspace(workspace)

input_schema = pa.schema([
    pa.field('tensor', pa.list_(
        pa.list_(
            pa.list_(
                pa.float32(), # images are normalized
                list_size=640
            ),
            list_size=480
        ),
        list_size=3
    )),
])

output_schema = pa.schema([
    pa.field('boxes', pa.list_(pa.list_(pa.float32(), list_size=4))),
    pa.field('classes', pa.list_(pa.int64())),
    pa.field('confidences', pa.list_(pa.float32())),
    pa.field('avg_px_intensity', pa.list_(pa.float32())),
    pa.field('avg_confidence', pa.list_(pa.float32())),
])

model = wl.upload_model(model_name, 
                        model_file_name, 
                        framework=Framework.CUSTOM,
                        input_schema=input_schema, 
                        output_schema=output_schema)

deployment_config = wallaroo.DeploymentConfigBuilder() \
    .replica_count(1) \
    .cpus(1) \
    .memory("2Gi") \
    .sidekick_cpus(model, 1) \
    .sidekick_memory(model, '6Gi') \
    .build()

pipeline = wl.build_pipeline(pipeline_name)
pipeline.clear()
pipeline.add_model_step(model)
pipeline.deploy(deployment_config = deployment_config)

baseline_images = [
    "./data/images/input/example/dairy_bottles.png",
    "./data/images/input/example/dairy_products.png",
    "./data/images/input/example/product_cheeses.png"
]
   
blurred_images = [
    "./data/images/input/example/blurred-dairy_bottles.png",
    "./data/images/input/example/blurred-dairy_products.png",
    "./data/images/input/example/blurred-product_cheeses.png"
]

baseline_images_list = utils.processImages(baseline_images)
blurred_images_list = utils.processImages(blurred_images)

# baseline image
df_test = pd.DataFrame({'tensor': wallaroo.utils.flatten_np_array_columns(baseline_images_list[0], 'tensor')})

df_test

result = pipeline.infer(df_test, 
                        dataset=['time', 'out.avg_confidence','out.avg_px_intensity','out.boxes','out.classes','out.confidences','check_failures','metadata'])
result

# example of an inference from a bad photo

df_test = pd.DataFrame({'tensor': wallaroo.utils.flatten_np_array_columns(blurred_images_list[0], 'tensor')})

result = pipeline.infer(df_test, dataset=['time', 'out.avg_confidence','out.avg_px_intensity','out.boxes','out.classes','out.confidences','check_failures','metadata'])
result

headers = wl.auth.auth_header()

headers['Content-Type'] = 'application/json; format=pandas-records'

deploy_url = pipeline._deployment._url()

response = requests.post(
                    deploy_url, 
                    headers=headers, 
                    data=df_test.to_json(orient="records")
                )

display(pd.DataFrame(response.json()))

ops_partition = result.loc[0, 'metadata.partition']
ops_partition

import requests

def ops_pipeline_inference(df):
    df_flattened = pd.DataFrame({'tensor': wallaroo.utils.flatten_np_array_columns(df, 'tensor')})
    # api based inference request
    headers = wl.auth.auth_header()

    headers['Content-Type'] = 'application/json; format=pandas-records'

    deploy_url = pipeline._deployment._url()

    response = requests.post(
                        deploy_url, 
                        headers=headers, 
                        data=df_flattened.to_json(orient="records")
                    )

    display(pd.DataFrame(response.json()).loc[:, ['time', 'metadata']])

def edge_pipeline_inference(df):
    df_flattened = pd.DataFrame({'tensor': wallaroo.utils.flatten_np_array_columns(df, 'tensor')})
    # api based inference request
    # headers = wl.auth.auth_header()

    headers = {
        'Content-Type': 'application/json; format=pandas-records'
    }

    deploy_url = 'http://HOSTNAME:8080/pipelines/retail-inv-tracker-edge-obs'

    response = requests.post(
                        deploy_url, 
                        headers=headers, 
                        data=df_flattened.to_json(orient="records")
                    )

    display(pd.DataFrame(response.json()).loc[:, ['time', 'out', 'metadata']])

pup = pipeline.publish()

display(pup)

edge_name = 'cv-observability-demo-sample'
edge_publish = pup.add_edge(edge_name)
display(edge_publish)

# create docker run 

docker_command = f'''
docker run -p 8080:8080 \\
    -v ./data:/persist \\
    -e DEBUG=true \\
    -e OCI_REGISTRY=$REGISTRYURL \\
    -e EDGE_BUNDLE={edge_publish.docker_run_variables['EDGE_BUNDLE']} \\
    -e CONFIG_CPUS=6 \\
    -e OCI_USERNAME=$REGISTRYUSERNAME \\
    -e OCI_PASSWORD=$REGISTRYPASSWORD \\
    -e PIPELINE_URL={edge_publish.pipeline_url} \\
    {edge_publish.engine_url}
'''

print(docker_command)

logs = pipeline.logs(dataset=['time', 'metadata'])

ops_locations = [pd.unique(logs['metadata.partition']).tolist()][0]
display(ops_locations)
ops_location = ops_locations[0]

baseline_start = datetime.datetime.now(datetime.timezone.utc)

for i in range(10):
    for good_image in baseline_images_list:
        ops_pipeline_inference(good_image)

time.sleep(10)

baseline_end = datetime.datetime.now(datetime.timezone.utc)

# create baseline from numpy

assay_name_from_dates = "average confidence drift detection example"
step_name = "resnet-with-intensity"
assay_builder_from_dates = wl.build_assay(assay_name_from_dates, 
                               pipeline, 
                               step_name, 
                               iopath="output avg_confidence 0", 
                               baseline_start=baseline_start,
                               baseline_end=baseline_end)

# assay from recent updates

assay_builder_from_dates = assay_builder_from_dates.add_run_until(baseline_end)

# View 1 minute intervals
# just ops
(assay_builder_from_dates
    .window_builder()
    .add_width(minutes=1)
    .add_interval(minutes=1)
    .add_start(baseline_start)
    .add_location_filter([ops_partition, edge_name])
)

assay_config = assay_builder_from_dates.build()
assay_results = assay_config.interactive_run()

print(f"Generated {len(assay_results)} analyses")
assay_results.chart_scores()

assay_window_start = datetime.datetime.now(datetime.timezone.utc)

for i in range(1):
    for bad_image in blurred_images_list:
        edge_pipeline_inference(bad_image)
for i in range(9):
    for good_image in baseline_images_list:
        ops_pipeline_inference(good_image)

# assay window from dates

assay_window_end = datetime.datetime.now(datetime.timezone.utc)

assay_builder_from_dates = assay_builder_from_dates.add_run_until(assay_window_end)

# View 1 minute intervals
# just combined
(assay_builder_from_dates
    .window_builder()
    .add_width(minutes=6)
    .add_interval(minutes=6)
    .add_start(assay_window_start)
    .add_location_filter([ops_partition, edge_name])
)

assay_config = assay_builder_from_dates.build()
assay_results = assay_config.interactive_run()

print(f"Generated {len(assay_results)} analyses")
assay_results.chart_scores()

for i in range(8):
    for good_image in baseline_images_list:
        ops_pipeline_inference(good_image)
for i in range(2):
    for bad_image in blurred_images_list:
        edge_pipeline_inference(bad_image)

for i in range(7):
    print(i)
    for good_image in baseline_images_list:
        ops_pipeline_inference(good_image)
for i in range(3):
    print(i)
    for bad_image in blurred_images_list:
        edge_pipeline_inference(bad_image)

for i in range(6):
    print(i)
    for good_image in baseline_images_list:
        ops_pipeline_inference(good_image)
for i in range(4):
    print(i)
    for bad_image in blurred_images_list:
        edge_pipeline_inference(bad_image)

# assay window from dates

assay_window_end = datetime.datetime.now(datetime.timezone.utc)

assay_builder_from_dates = assay_builder_from_dates.add_run_until(assay_window_end)

# View 1 minute intervals
# just combined
(assay_builder_from_dates
    .window_builder()
    .add_width(minutes=7)
    .add_interval(minutes=7)
    .add_start(assay_window_start)
    .add_location_filter([ops_partition, edge_name])
)

assay_config = assay_builder_from_dates.build()
assay_results = assay_config.interactive_run()

print(f"Generated {len(assay_results)} analyses")
assay_results.chart_scores()

for i in range(5):
    print(i)
    for good_image in baseline_images_list:
        ops_pipeline_inference(good_image)
for i in range(5):
    print(i)
    for bad_image in blurred_images_list:
        edge_pipeline_inference(bad_image)

for i in range(4):
    print(i)
    for good_image in baseline_images_list:
        ops_pipeline_inference(good_image)
for i in range(6):
    print(i)
    for bad_image in blurred_images_list:
        edge_pipeline_inference(bad_image)

for i in range(3):
    print(i)
    for good_image in baseline_images_list:
        ops_pipeline_inference(good_image)
for i in range(7):
    print(i)
    for bad_image in blurred_images_list:
        edge_pipeline_inference(bad_image)

for i in range(2):
    print(i)
    for good_image in baseline_images_list:
        ops_pipeline_inference(good_image)
for i in range(8):
    print(i)
    for bad_image in blurred_images_list:
        edge_pipeline_inference(bad_image)

for i in range(1):
    print(i)
    for good_image in baseline_images_list:
        ops_pipeline_inference(good_image)
for i in range(9):
    print(i)
    for bad_image in blurred_images_list:
        edge_pipeline_inference(bad_image)

for i in range(10):
    print(i)
    for bad_image in blurred_images_list:
        edge_pipeline_inference(bad_image)

# assay window from dates

assay_window_end = datetime.datetime.now(datetime.timezone.utc)

assay_builder_from_dates = assay_builder_from_dates.add_run_until(assay_window_end)

# just combined
(assay_builder_from_dates
    .window_builder()
    .add_width(minutes=6)
    .add_interval(minutes=6)
    .add_start(assay_window_start)
    .add_location_filter([ops_partition, edge_name])
)

assay_config = assay_builder_from_dates.build()
assay_results = assay_config.interactive_run()

print(f"Generated {len(assay_results)} analyses")
assay_results.chart_scores()

# assay window from dates

assay_window_end = datetime.datetime.now(datetime.timezone.utc)

assay_builder_from_dates = assay_builder_from_dates.add_run_until(assay_window_end)

# just combined
(assay_builder_from_dates
    .window_builder()
    .add_width(minutes=6)
    .add_interval(minutes=6)
    .add_start(assay_window_start)
    .add_location_filter([ops_partition])
)

assay_config = assay_builder_from_dates.build()
assay_results = assay_config.interactive_run()

print(f"Generated {len(assay_results)} analyses")
assay_results.chart_scores()

# assay window from dates

assay_window_end = datetime.datetime.now(datetime.timezone.utc)

assay_builder_from_dates = assay_builder_from_dates.add_run_until(assay_window_end)

# just combined
(assay_builder_from_dates
    .window_builder()
    .add_width(minutes=6)
    .add_interval(minutes=6)
    .add_start(assay_window_start)
    .add_location_filter([edge_name])
)

assay_config = assay_builder_from_dates.build()
assay_results = assay_config.interactive_run()

print(f"Generated {len(assay_results)} analyses")
assay_results.chart_scores()

pipeline.undeploy()