class Multiwoven::Integrations::Source::VertexModel::Client

def build_url(url, connection_config)
  case url
  when GOOGLE_VERTEX_MODEL_NAME
    format(url, project_id: connection_config[:project_id],
                region: connection_config[:region],
                endpoint_id: connection_config[:endpoint_id])
  when GOOGLE_VERTEX_ENDPOINT_SERVICE_URL
    format(url, region: connection_config[:region])
  end
end

def check_connection(connection_config)
  connection_config = connection_config.with_indifferent_access
  create_connection(connection_config)
  @client.get_endpoint(name: build_url(GOOGLE_VERTEX_MODEL_NAME, connection_config))
  ConnectionStatus.new(status: ConnectionStatusType["succeeded"]).to_multiwoven_message
rescue StandardError => e
  ConnectionStatus.new(status: ConnectionStatusType["failed"], message: e.message).to_multiwoven_message
end

def create_connection(connection_config)
  Google::Cloud::AIPlatform::V1::EndpointService::Client.configure do |config|
    config.endpoint = build_url(GOOGLE_VERTEX_ENDPOINT_SERVICE_URL, connection_config)
    config.credentials = connection_config["credentials_json"]
  end
  Google::Cloud::AIPlatform::V1::PredictionService::Client.configure do |config|
    config.endpoint = build_url(GOOGLE_VERTEX_ENDPOINT_SERVICE_URL, connection_config)
    config.credentials = connection_config["credentials_json"]
  end
  @client = Google::Cloud::AIPlatform::V1::EndpointService::Client.new
  @endpoint = Google::Cloud::AIPlatform::V1::PredictionService::Client.new
end

def discover(_connection_config = nil)
  catalog_json = read_json(CATALOG_SPEC_PATH)
  catalog = build_catalog(catalog_json)
  catalog.to_multiwoven_message
rescue StandardError => e
  handle_exception(e, {
                     context: "GOOGLE:VERTEX MODEL:DISCOVER:EXCEPTION",
                     type: "error"
                   })
end

def process_response(response)
  data = JSON.parse(response.data)
  [RecordMessage.new(data: data, emitted_at: Time.now.to_i).to_multiwoven_message]
end

def read(sync_config)
  connection_config = sync_config.source.connection_specification
  connection_config = connection_config.with_indifferent_access
  # The server checks the ConnectorQueryType.
  # If it's "ai_ml," the server calculates the payload and passes it as a query in the sync config model protocol.
  # This query is then sent to the AI/ML model.
  payload = JSON.parse(sync_config.model.query)
  run_model(connection_config, payload)
rescue StandardError => e
  handle_exception(e, {
                     context: "GOOGLE:VERTEX MODEL:READ:EXCEPTION",
                     type: "error"
                   })
end

def run_model(connection_config, payload)
  create_connection(connection_config)
  http_body = Google::Api::HttpBody.new(data: JSON.generate(payload))
  response = @endpoint.raw_predict(endpoint: build_url(GOOGLE_VERTEX_MODEL_NAME, connection_config), http_body: http_body)
  process_response(response)
rescue StandardError => e
  handle_exception(e, context: "GOOGLE:VERTEX MODEL:RUN_MODEL:EXCEPTION", type: "error")
end