ChatGPT Vision to Grafana Cloud Metrics Example

chatgpt-vision-grafana-cloud-metrics-example.rb

require 'base64'
require "json"
require "logger"
require "openai"
require "net/http"

#
# Initialize logger
#
logger = Logger.new(STDOUT)
logger.info 'Begin image parser...'

#
# Initialize OpenAI client
#
@openai_client = OpenAI::Client.new(
  access_token: ENV["OPENAI_API_KEY"],
  organization_id: ENV["OPENAI_API_ORGANIZATION_ID"]
)
logger.info("OpenAI client initialized")

#
# Prepare input for the ChatGPT Vision model, asking to identify matches of the first image in the second image.
#
system_context = ""
system_context = "You are an expert image analyst capable of identifying patterns between images. You count a match when you find an object in the third image that looks like a car or truck from the first or second images. Only count a match if you're very confident a match exists."

user_messages = ""
user_messages = [
  { "type": "text", "text": "How many times does the object from the first or second image appear in the third image? Be precise."},
  { "type": "image_url",
    "image_url": {
      "url": "https://images.unsplash.com/photo-1616549972169-0a0d961c9905",
    },
  },
  { "type": "image_url",
    "image_url": {
      "url": "https://images.unsplash.com/photo-1544601640-b256c49a192d",
    },
  },
  { "type": "image_url",
    "image_url": {
      "url": "https://www.nps.gov/webcams-yell/mammoth_arch.jpg",
    },
  }
]

example_output = ""
example_output = '
              Example response object:
               {
                 "matches": integer,
               }
             '
logger.info("Prompts prepared for OpenAI API")

#
# Call OpenAI API
#
response = ''
begin
  response = @openai_client.chat(
    parameters: {
      model: "gpt-4-vision-preview",
      messages: [
        { role: "system", content: system_context },
        { role: "system", content: example_output },
        { role: "user", content: user_messages }
      ],
      temperature: 0.4,
      max_tokens: 100
  })
rescue => err
  logger.fatal(err)
  return
else
  logger.info("OpenAI API response received and successfully processed")
  logger.info("Response:\n#{response}")
end

#
# Save and print the the summarized version of what the business does
#
if response != ''
  hash_results = {}
  hash_results = JSON.parse(response.dig("choices", 0, "message", "content"))
  puts "Summary result:\n#{hash_results}"
end

#
# Save results to Grafana Cloud
#
logger.info ""
logger.info "Saving results to Grafana Cloud..."

#
# Initialize Grafana Cloud auth variables
#
@grafana_base64_encoded_auth_token = Base64.strict_encode64(ENV['GRAFANA_CLOUD_METRICS_USER_ID'] + ':' + ENV['GRAFANA_CLOUD_METRICS_API_KEY'])

#
# Set metrics payload
#
metrics_payload = ""
metrics_payload = "nps_entrance,park=yellowstone vehicles=#{hash_results['matches']}"

#
# Push metric to Grafana Cloud using the Influx Line Protocol
#
begin
  uri = URI.parse(ENV['GRAFANA_CLOUD_METRICS_INFLUX_PROXY_ENDPOINT'])
  response = Net::HTTP.start(uri.host, uri.port, use_ssl: true) do |client|
    request                  = Net::HTTP::Post.new(uri.path)
    request.body             = metrics_payload
    request["Authorization"] = "Basic #{@grafana_base64_encoded_auth_token}"
    request["Content-Type"]  = "text/plain"
    client.request(request)
  end
rescue => err
  logger.fatal('!!!')
  logger.fatal("Caught exception; exiting")
  logger.fatal(err)
  logger.fatal('!!!')
  return
else
  logger.info 'Grafana Cloud response:'
  logger.info response.code
  logger.info ''
  return
end