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#!/usr/bin/env python

import os
import json
import time
import boto3
import sagemaker


if __name__ == "__main__":

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root@8423ac911e05:/opt/tritonserver# tritonserver --model-repository=model_repository
I0425 10:08:43.682014 1236 pinned_memory_manager.cc:241] Pinned memory pool is created at '0x77fa88000000' with size 268435456
I0425 10:08:43.682247 1236 cuda_memory_manager.cc:107] CUDA memory pool is created on device 0 with size 67108864
I0425 10:08:43.684950 1236 model_lifecycle.cc:461] loading: detection_postprocessing:1
W0425 10:08:43.685049 1236 model_lifecycle.cc:108] ignore version directory 'hf_cache' which fails to convert to integral number
I0425 10:08:43.685063 1236 model_lifecycle.cc:461] loading: sam_vit:1
W0425 10:08:43.685125 1236 model_lifecycle.cc:108] ignore version directory 'hf_cache' which fails to convert to integral number
I0425 10:08:43.685138 1236 model_lifecycle.cc:461] loading: grounding_dino_tiny:1
I0425 10:08:45.251843 1236 python_be.cc:2363] TRITONBACKEND_ModelInstanceInitialize: detection_postprocessing_0 (GPU device 0)
I0425 10:08:45.654267 1236 model_lifecycle.cc:818] successfully loaded 'd

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import json
import io
import numpy as np
import torch
from transformers import GroundingDinoForObjectDetection, GroundingDinoProcessor
import triton_python_backend_utils as pb_utils
from PIL import Image


class TritonPythonModel:

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package main

// Example of using MCP with Gemini via Function Calls

import (
	"context"
	"encoding/json"
	"fmt"
	"log"
	"os"

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package main

import (
	"context"
	"fmt"
	"log"
	"os"

	"github.com/google/generative-ai-go/genai"
	"github.com/hectormalot/omgo"

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from abc import ABC, abstractmethod
import re
from typing import Dict, Any
import json
import requests
from bs4 import BeautifulSoup


class Scraper(ABC):
    def parse_site(self, url: str, headers: dict={}) -> Dict[str, Any]:

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#image processing resources
from skimage.io import imread, imshow
from skimage.filters import gaussian, threshold_otsu
from skimage.feature import canny
from skimage.transform import probabilistic_hough_line, rotate

#testing 
import numpy as np
import os

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from urllib.parse import urlparse
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from airflow.providers.amazon.aws.operators.s3 import S3FileTransformOperator


def file_from_s3(prefix):
    """
    Fetches S3 processed file from remote to local /tmp
    to allow inference to run on it
    """

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from airflow.models.dag import DAG
from airflow.api.client.local_client import Client
from airflow.providers.amazon.aws.sensors.sqs import SqsSensor


def analyze_data(ti):
    airflow_client = Client(None, None)
    messages = ti.xcom_pull(task_ids="sqs_sensor", key="messages")
    for msg in messages:
        records = json.loads(msg['Body'])

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# Change these variables as necessary.
MAIN_PACKAGE_PATH := ./cmd/example
BINARY_NAME := example

# ==================================================================================== #
# HELPERS
# ==================================================================================== #

## help: print this help message
.PHONY: help