# from absl import app
# from absl import flags
import base64
import requests

from google.cloud import aiplatform
from google.protobuf import struct_pb2
from typing import NamedTuple, Sequence, Optional


class EmbeddingResponse(NamedTuple):
    text_embedding: Optional[Sequence[float]]
    image_embedding: Optional[Sequence[float]]


def load_image_bytes(image_uri: str) -> bytes:
    """Load image bytes from a remote or local URI."""
    if image_uri.startswith("http://") or image_uri.startswith("https://"):
        response = requests.get(image_uri, stream=True)
        if response.status_code == 200:
            image_bytes = response.content
    else:
        image_bytes = open(image_uri, "rb").read()
    return image_bytes


class MultimodalEmbeddingPredictionClient:
    """Wrapper around Prediction Service Client."""

    def __init__(
        self,
        project_id: str,
        location: str = "us-central1",
        api_regional_endpoint: str = "us-central1-aiplatform.googleapis.com",
    ):
        client_options = {"api_endpoint": api_regional_endpoint}
        # Initialize client that will be used to create and send requests.
        # This client only needs to be created once, and can be reused for multiple requests.
        self.client = aiplatform.gapic.PredictionServiceClient(
            client_options=client_options
        )
        self.location = location
        self.project_id = project_id

    def get_embedding(
        self, text: Optional[str] = None, image_file: Optional[str] = None
    ):
        if not text and not image_file:
            raise ValueError("At least one of text or image_file must be specified.")

        # Load image file
        image_bytes = None
        if image_file:
            image_bytes = load_image_bytes(image_file)

        instance = struct_pb2.Struct()
        if text:
            instance.fields["text"].string_value = text

        if image_bytes:
            encoded_content = base64.b64encode(image_bytes).decode("utf-8")
            image_struct = instance.fields["image"].struct_value
            image_struct.fields["bytesBase64Encoded"].string_value = encoded_content

        instances = [instance]
        endpoint = (
            f"projects/{self.project_id}/locations/{self.location}"
            "/publishers/google/models/multimodalembedding@001"
        )
        response = self.client.predict(endpoint=endpoint, instances=instances)

        text_embedding = None
        if text:
            text_emb_value: Sequence[float] = response.predictions[0]["textEmbedding"]
            text_embedding = [v for v in text_emb_value]

        image_embedding = None
        if image_bytes:
            image_emb_value: Sequence[float] = response.predictions[0]["imageEmbedding"]
            image_embedding = [v for v in image_emb_value]

        return EmbeddingResponse(
            text_embedding=text_embedding, image_embedding=image_embedding
        )