# Create a Cloud Storage bucket for Cloud Function source code
resource "google_storage_bucket" "function_source" {
  name     = "${var.project_id}-cloud-function-source"
  location = var.region
  uniform_bucket_level_access = true
  
  depends_on = [google_project_service.services]
}

# Create a ZIP archive of the Cloud Function source code
data "archive_file" "function_source" {
  type        = "zip"
  output_path = "${path.module}/function-source.zip"
  source_dir  = "${path.module}/../cloud-function"
}

# Upload the ZIP file to Cloud Storage
resource "google_storage_bucket_object" "function_source" {
  name   = "function-source-${data.archive_file.function_source.output_md5}.zip"
  bucket = google_storage_bucket.function_source.name
  source = data.archive_file.function_source.output_path
}

# Create the Cloud Function
resource "google_cloudfunctions2_function" "image_processor" {
  name        = "process-image-embedding"
  location    = var.region
  description = "Process image embeddings and store in vector database"

  build_config {
    runtime     = "python311"
    entry_point = "process_image_embedding"
    
    source {
      storage_source {
        bucket = google_storage_bucket.function_source.name
        object = google_storage_bucket_object.function_source.name
      }
    }
  }

  service_config {
    max_instance_count = 10
    min_instance_count = 0
    available_memory   = "512M"
    timeout_seconds    = 540
    
    environment_variables = {
      # Qdrant configuration
      QDRANT_HOST       = google_compute_instance.vector_db_vm.network_interface[0].access_config[0].nat_ip
      QDRANT_PORT       = "6333"
      QDRANT_API_KEY    = var.qdrant_api_key
      QDRANT_COLLECTION = "image_vectors"
      QDRANT_HTTPS      = "false"
      
      # Firestore configuration
      FIRESTORE_PROJECT_ID     = var.project_id
      FIRESTORE_DATABASE_NAME  = var.firestore_db_name
      
      # Google Cloud Storage configuration
      GCS_BUCKET_NAME = var.storage_bucket_name
      
      # Google Cloud Vision API
      VISION_API_ENABLED = "true"
      
      # Logging
      LOG_LEVEL = "INFO"
    }
    
    service_account_email = local.cloud_function_service_account
  }

  event_trigger {
    trigger_region = var.region
    event_type     = "google.cloud.pubsub.topic.v1.messagePublished"
    pubsub_topic   = google_pubsub_topic.image_processing.id
    retry_policy   = "RETRY_POLICY_RETRY"
  }

  depends_on = [
    google_project_service.services,
    google_compute_instance.vector_db_vm,
    google_pubsub_topic.image_processing
  ]
}

# Grant the Cloud Function service account necessary permissions
resource "google_project_iam_member" "function_firestore" {
  project = var.project_id
  role    = "roles/datastore.user"
  member  = "serviceAccount:${local.cloud_function_service_account}"
}

resource "google_project_iam_member" "function_storage" {
  project = var.project_id
  role    = "roles/storage.objectViewer"
  member  = "serviceAccount:${local.cloud_function_service_account}"
}

resource "google_project_iam_member" "function_vision" {
  project = var.project_id
  role    = "roles/ml.developer"
  member  = "serviceAccount:${local.cloud_function_service_account}"
}

# Additional permissions required for Cloud Functions v2 with Eventarc
resource "google_project_iam_member" "function_eventarc_receiver" {
  project = var.project_id
  role    = "roles/eventarc.eventReceiver"
  member  = "serviceAccount:${local.cloud_function_service_account}"
}

resource "google_project_iam_member" "function_pubsub_subscriber" {
  project = var.project_id
  role    = "roles/pubsub.subscriber"
  member  = "serviceAccount:${local.cloud_function_service_account}"
}

# Output the Cloud Function details
output "cloud_function_name" {
  description = "Name of the deployed Cloud Function"
  value       = google_cloudfunctions2_function.image_processor.name
}

output "cloud_function_url" {
  description = "URL of the deployed Cloud Function"
  value       = google_cloudfunctions2_function.image_processor.service_config[0].uri
}