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import json
import os
from functools import partial

import flytekit
from flytekit import ImageSpec, Resources, Secret, map_task, task, workflow

embed_image = ImageSpec(
    name="langchain-flyte-vectordb",
    packages=[

samhita-alla

import json
import os
from functools import partial

import flytekit
from flytekit import ImageSpec, Resources, Secret, dynamic, map_task, task, workflow

load_data_image = ImageSpec(
    name="langchain-flyte-load-data",
    packages=[

samhita-alla

from dataclasses import dataclass
from typing import Dict, List

import flytekit
import matplotlib.pyplot as plt
import mpld3
import numpy as np
import pandas as pd
import pyarrow as pa
import vaex

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def to_literal(
    self,
    ctx: FlyteContext,
    python_val: T,
    python_type: Type[T],
    expected: LiteralType,
) -> Literal:
    meta = BlobMetadata(
        type=_core_types.BlobType(
            format=self.TENSORFLOW_FORMAT,

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...
ONNX_MODEL = "onnx_geolocator"
VERSION = "latest"


class GeoLocatorRunnable(bentoml.Runnable):
    """
    Custom BentoML runner to fetch multiple ONNX outputs
    """

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